The Allure of Italy's Sun-Kissed Passito Wines - Passito is an Italian word for sweet, dessert-style wines made by the appassimento drying process where the grapes are partially dried in the sun so they lose water, shrivel and become full of concentrated sugars and flavors.
A Review and Comparison of Some Really Super Super-Tuscan Wines - The Super-Tuscan movement came into being in the late 1960's when some visionary producers crafted a new type of red wine that defied regional traditions and regulatory requirements. Utilizing non-traditional varieties or innovative vinification protocols - or both - these innovative producers crafted some truly remarkable wines that quickly caught the attention of wine critics and enthusiasts alike. Today, they are generally recognized as some of Italy's best - and most expensive - wines.
A Tasting and Comparison of Value-priced Chianti Classico Wines - Chianti Classico is Tuscany's most famous and well-known wine. These wines are typically red blends made primarily of Sangiovese grapes from Tuscany's Chianti Classico zone with residual amounts of other traditional or non-native red varieties. In general, Chianti Classico wines are medium-bodied, with floral and tart cherry aromas, firm and very dry with discreet tannins and a lively streak of acidity. With their relatively modest price tags they are one of the wine world's great value wines, especially when compared with other fine wines today. They are also one of the world's most food-friendly wines.
A Dozen Must-try Italian Wines for 2018 that Provide an Escape from the Ordinary - This article reviews a dozen pleasurable, out-of-the-ordinary Italian wines that will give fillip to your plan to escape the ordinary and expand your wine horizons in 2018.
A Tasting of Top-End California Cabernet Wines - I know, California Cabernets have little to do with Italy or Italian wines. But you need to branch out every once in a while, explore new venues and different wines. So in that ecumenical spirit I recently attended a small group tasting of top-rated California Cabernet wines that range in price from $55 to $133. Of the six wines tasted, the clear favorite is Francis Ford Coppola's 2013 "Archimedes" which retails for $86.
More Value-priced Super-Tuscan Wines - I enjoy touring Tuscany and visiting wineries in that beautiful part of Italy. Wines from the Chianti region are among Tuscany's most famous while the Brunello di Montalcino wines from southern Tuscany are some of the best Italy has to offer, prized for their structure, elegance and longevity.
Best Value-Priced Italian Red Wines - Twelve Wines Under $20 - It's that time of year when we start seeing numerous "best of" lists. You know what I mean - lists enumerating the best of this or that for the year such as favorite restaurants, movies, music, etc.
Top Ten Affordable Super-Tuscan Wines - The words "affordable" and "Super-Tuscan" don't often appear in the same sentence. Super-Tuscans are big, rich, intense and modern wines that often carry triple-digit price tags, especially acclaimed Super-Tuscans with bold-face names like Ornellaia, Messorio, Solaia, Tignanello or Sassicaia.
Top Italian Wine Values: Twelve Great Italian Red Wines Under $12 - Italy is a complicated wine country that produces a lot of wine each year, both red and white. Some of Italy's wines are well known and highly-regarded by the international wine community. They also are frequently quite expensive. But a lot of Italian wine comes from less well-known varieties grown in regions other than Tuscany or the Piedmont that are generally less familiar to American consumers.


Copyright 2010 The Mom Egg. All rights reserved.
on her new book of poetry, Easy, on teaching, hip hop and Hallmark, and, of course, poetry!
Marie Ponsot's most recent books include The Bird Catcher, winner of the National Book Critics Circle Award for poetry in 1998, and Springing: New and Selected Poems. Professor emerita of English at Queens College, CUNY, she now teaches at the Unterberg Poetry Center of the 92nd Street Y and at the New School University. Her awards include the Phi Beta Kappa Medal, the Shaughnessy Prize of the Modern Language Association, and the Poetry Society of America's Frost Medal for lifetime achievement. She lives in New York City.


I was speechless. Rachel was writing me about her sister Gina, who lives in Australia with her family and dog Beau. A few weeks earlier, Rachel had asked me whether I could help with Beau, who had been diagnosed with an inoperable tumor on his bladder. I didn't know how much I could help, if at all, but I told Rachel I would try.
I've seen remote energy healing perform what some might call a "miracle": profound changes in an animal's health and behavior accomplished solely through the healing power of Spirit and Intention. Even so, I wondered whether anything could help Beau. Gina told me the tumor had covered his entire bladder wall. And while Beau was still on the operating table the vet called to ask Gina whether Beau shouldn't be euthanized right then…the tumor was so advanced he would have no remaining quality of life anyway.
But Gina and her family couldn't bear to let him go. I, on the other hand, worried about giving them false hope.
Yet a voice inside me told me to try.
When I first talked to Beau after his surgery he said, "I'm not ready to go. I still want to help Gina." As Beau and I talked I could feel the life force inside him—strong and sweet, loving and trusting. That feeling—that faith in him—gave me all that I needed to begin.
On March 22 I conducted the first healing session on Beau. Using a shamanic animal healing technique, I worked on Beau for half an hour. I called in the Spirits and asked them to work on Beau at the energetic level. At the end of this session, I was very tired.
On March 24 I did a distant Reiki healing session on Beau. At the end of the session a large black bird, with a wingspan as wide as a stealth fighter, flew overhead. I could see him clearly in my mind's eye as he passed over Beau and collected the negative energy that had just been released from the dog's body.
On March 30 I organized a group intention healing session for Beau, based on the work of Lynne McTaggart's "The Intention Experiment". Intention is the force that creates worlds, and saves lives. I figured it was worth a shot.
I asked the most powerful healers I know—members of Deborah King's 21st Century Energy Medicine program—to send Beau the exact same healing thought at the exact same time…for ten consecutive minutes. The date was set for 12:15 EST on Wednesday, March 30, 2011.
On April 2 I did the final healing session on Beau, another intense shamanic healing session.
I don't know how long Beau has; I don't know how long anyone has. What I do know is that counting feathers is like counting miracles…you can if you want, but you don't have to. What matters is that you—like Gina and Beau—simply believe.


Brent Civic Centre, built by Skanska, has been awarded the coveted BREEAM 'Outstanding' rating, the highest possible. Its score, at over 93 per cent, makes it the greenest public building in the UK, setting the standard for the rest of the country.
Designed by Hopkins Architects and URS, the building is set to achieve a 40 per cent reduction in carbon emissions compared with a standard building, thanks to a combination of features, including solar shading, natural ventilation, a high-performance façade and a combined cooling, heating and power (CHP) generation system, which uses waste fish oil.
Paul Chandler, Executive Vice President at Skanska, commented: "From the start of this project through to handing over the finished building, the team worked tirelessly to deliver a building that the client will be proud to occupy and would deliver a high environmental performance.
Andy Donald, Brent Council's Director of Regeneration and Growth, said: "One of the most important aspects of the project is the incorporation of sustainable technologies. The council's old buildings were inefficient and were expensive to maintain and run.
"Now we have a combination of energy saving features in our centre, which means we are both supporting our own environmental targets and saving money.
Last week, Brent Civic Centre was named Sustainable Project of the Year at the Building Awards 2014. The project also won the innovation category at the Local Government Chronicle's Business Partnership Awards.
The new building accommodates 2,000 employees and brings together all council functions from across the borough.
The project delivery team included Brent Council, Hopkins Architects, Skanska, as the contractor, URS for town planning, engineering design, environmental and sustainability services, including the BREEAM assessment, and Turner & Townsend as quantity surveyor and project manager.


That is the last of a 5 half collection. The links to 1-4 are on the backside of that web page. CPR coaching classes can be found for each stage of scholar, whether or not they're a complete newbie, new parent, or a healthcare skilled. For essentially the most half, CPR classes are geared in direction of the start student, however are a requirement for medical professionals as a prerequisite for superior certification in Primary Life Help (BLS) and Advanced Cardiac Life Support (ACLS). Make sure any training you are taking is accepted by the American Heart Association and takes place in an AHA certified facility.
In firms, health educators carry out or coordinate worker counseling in addition to training providers, employee health threat appraisals, and health screenings. They design, promote, lead and/or evaluate packages about weight control, hypertension, vitamin, substance abuse prevention, bodily fitness, stress management and smoking cessation; develop educational materials; and write grants for cash to assist these initiatives. They help companies meet occupational health and security laws, work with the media, and identify community health sources for workers.
Versus nations that guarantee healthcare to all, the authors concluded that Individuals' ability to realize high quality healthcare is nearly totally depending on financial standing. It overlooked both the odds or the numbers for a few job titles altogether. Thanks for stopping by! I recognize your remark; I hope to learn Meriwether Lewis's journal a while as properly. From what you said, it sounds fascinating!
Full-textual content articles are available from 2008 to the current. Entry to abstracts is complimentary. Access to full text is restricted to American Association on Health and Incapacity members in addition to personal subscribers. You as the affected person should pay first after which claim. This means you've got the liberty to choose the hospital, the doctor, the dentist or the specialist. If you're poor, you might be unlikely to seek therapy from expensive hospitals (learn good remedy) because the requirement is that it's important to pay first from your pocket and then search reimbursement from the insurance fund. The hospital beds are within the ratio of 65% for public hospitals, 15% non-public however non profit making teams and the remaining 20% of beds are supplied by profit hungry corporations.
Firstly it's very nice when you can arrive on the class just a little early as you lie on your mat / towel and simply chill out. The room is quiet and peaceable and actually really warm and you probably have a very good creativeness you possibly can close your eyes and picture that you're lying on a beach in the course of the Caribbean. Nevertheless the primary distinction between being on a seaside and in a Bikram studio is the odor!! As you possibly can think about – it stinks!! Not a lot sweat and BO, extra tacky ft. Although you do turn out to be acclimatised in a short time and the scent disappears so don't let it put you off.
Each clinical and wellness related appointments can be found at Student Health & Wellness, located within the Westlawn building (map). Consultations are available on a number of wellness subjects ( Diet , Physical Exercise , Stress , Tobacco , & Sexual Health ) on the CRWC (map). The diagnoses of failure in the healthcare system typically focus on a cluster of issues. The image of his altered face has been shown on a product known as Obama Waffles within the method of Aunt Jemima and Uncle Ben. The depictions of Obama as ape-like have arguably sparked probably the most controversy, considering that blacks have been portrayed as monkey-like for hundreds of years to recommend that they're inferior to other groups.
Thyroxine s also referred to as T4 is a hormone produced by the thyroid gland and is likely one of the most vital thyroid hormones. Thyroxine performs a key role in each process in the physique, including regulating physique temperature, progress, and coronary heart fee. It is the inactive form, which is transformed into energetic form referred to as triiodothyronine by liver and kidneys.
Walking like use of cardio fitness tools can put your Goal Heart Rate on the correct degree to maximally burn fat. However strolling & good diet is easier on the body and will get the identical thing accomplished. Additionally, light weights with high repetition for higher physique can keep muscle tone, while sculpting and burning extra physique fat. Thanks guys for commenting, I take advantage of to emphasize out about all the things till I found meditation and yoga.


The Karnataka State Open University will claim for various Under-graduates and Post-Graduate exam Results inside the month of November 2018. Students who may also appear in the University exam, they could test KSOU University Exam Results 2019 on its reputable website.It is knowledgeable to the complete pupil who is associated with the KSOU University might also take a look at Karnataka State Open University Exam Results 2019 on https://ksoumysore.edu.in/. In this article we've got stated all of the details which can be required for to the scholars to test their effects or you may visit legit internet site.
The Karnataka State Open University changed into installed earlier on 1st June 1996. The main goal of the KSOU University is to offer the better educational historical past to the kingdom aspirants and promote the device of distance studying in the nation. This University is located in Karnataka State and it's headquarter is located at Mysore. This college is for individuals who are unable to attend the entire time training. Karnataka State Open University is going to release various UG and PG exam results on its legitimate internet site.
Students need to first go to respectable website @ www.Ksou.Ac.In.
Click on effects segment and select desire direction.
Then input enrollment wide variety as asked.
Then click on put up button.
After submission, within few 2d, result could be show in your display.
Save it and take tough reproduction for future use.
Note: Here we're going to tell all of the college students who regarded inside the college examination held in the month of August every year that the ones who've effectively clean the exam, may additionally take a look at official internet site for in addition method. And the ones who've trouble with their marks, they may touch to examination branch for similarly technique of re-checking or re-evaluation. Students may also go for lower back paper examination.
Here we've got informed all the details about Karnataka State Open University Result 2019 which is prepared to declare on its legitimate website. So it's miles suggested for all of the college students to examine this post with cautiously, as we've got shared a few smooth steps to get your marks scored. Still all of us have any problem concerning KSOU UG PG Exam Results 2019, then they go away message underneath in remark container. For greater information of upcoming government jobs 2019 and latest Results, stay tuned with us.


text:
Oradour-sur-Glane, The Old Village of France
Oradour-sur-Glane, a fully preserved, ruined village that was the site of the worst Nazi massacre of civilians carried out on French soil. Six hundred and 42 people, including 247 children, were shot or burnt alive on 10 June 1944 in an unexplained act of barbarity. A new village was built nearby after the war, but French president Charles de Gaulle ordered the original maintained as a permanent memorial and museum.
Photo credit TruckerPat
The women and children were locked in the church. and the village was looted. The men were led to six barns and sheds, where machine guns were already in place. According to a survivor's account, the SS men then began shooting, aiming for their legs. When victims were unable to move, the Nazis covered them with fuel and set the barns on fire. Only six men managed to escape. One of them was later seen walking down a road and was shot dead. In all, 190 Frenchmen died. Several days later, the survivors were allowed to bury the dead. 642 inhabitants of Oradour-sur-Glane had been killed in just a few hours. Adolf Diekmann said the atrocity was in retaliation for the partisan activity in nearby Tulle and the kidnapping of Helmut Kämpfe.
After the war, General Charles de Gaulle decided the village should never be rebuilt, but would remain a memorial to the cruelty of the Nazi occupation. The new village of Oradour-sur-Glane (population 2,188 in 2006), northwest of the site of the massacre, was built after the war. The ruins of the original village remain as a memorial to the dead and to represent similar sites and events.
In 1999 French president Jacques Chirac dedicated a memorial museum, the Centre de la mémoire d'Oradour, near the entrance to the Village Martyr ("martyred village"). Its museum includes items recovered from the burned-out buildings: watches stopped at the time their owners were burned alive, glasses melted from the intense heat, and various personal items.
Photo credit Alain Devisme
Photo credit Sheriff Of Nothing
Photo credit Verity Cridland
The soldiers proceeded to the church with all the women and children, locked them in and placed an incendiary device there. After it was ignited, the women and children tried to escape through the doors and windows of the church, but they were met with machine-gun fire. Photo credit Michael
Photo credit Pascal Chotard
Photo credit deebanktreasure
Photo credit Fred Yvonne
Source — Wikipedia | Theguardian
Labels: Abandoned, Europe
Location: Oradour-sur-Glane, France

redpajama_set_name: RedPajamaCommonCrawl


text:
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This is V 1
CAGNet: Content-Aware Guidance for Salient Object Detection
Sina Mohammadi1 Mehrdad Noori1 me.noori.1994@gmail.com Ali Bahri Sina Ghofrani Majelan Mohammad Havaei School of Electrical Engineering, Iran University of science and Technology, Tehran, Iran Imagia Inc., Montreal, Canada
11Both the authors contributed equally.
Beneficial from Fully Convolutional Neural Networks (FCNs), saliency detection methods have achieved promising results. However, it is still challenging to learn effective features for detecting salient objects in complicated scenarios, in which i) non-salient regions may have "salient-like" appearance; ii) the salient objects may have different-looking regions. To handle these complex scenarios, we propose a Feature Guide Network which exploits the nature of low-level and high-level features to i) make foreground and background regions more distinct and suppress the non-salient regions which have "salient-like" appearance; ii) assign foreground label to different-looking salient regions. Furthermore, we utilize a Multi-scale Feature Extraction Module (MFEM) for each level of abstraction to obtain multi-scale contextual information. Finally, we design a loss function which outperforms the widely-used Cross-entropy loss. By adopting four different pre-trained models as the backbone, we prove that our method is very general with respect to the choice of the backbone model. Experiments on five challenging datasets demonstrate that our method achieves the state-of-the-art performance in terms of different evaluation metrics. Additionally, our approach contains fewer parameters than the existing ones, does not need any post-processing, and runs fast at a real-time speed of 28 FPS when processing a 480×480 image.
Saliency detection, Fully convolutional neural networks, Attention guidance
††journal: Pattern Recognition\useunder
\ul
Salient object detection aims at localizing the most interesting and prominent parts of an image. Moreover, it is an effective pre-processing step for numerous computer vision tasks such as image classification flores2019saliency , image segmentation li2016robust ; zhi2018saliency ; cai2019saliency , video segmentation wang2015saliency , image editing chen2015improved ; zhang2018novel and object tracking hong2015online .
Traditional approaches are mostly based on low-level cues and hand-crafted features. For example, the method proposed in huo2016object uses color feature to detect salient objects. Some other methods use center prior to improve the performance of salient object detection aksac2017complex ; liang2018material . Because of the lack of semantic information, these methods have limited ability to detect the whole structure of salient objects in complex scenes. In recent years, the methods based on the Fully Convolutional Neural Networks (FCNs), such as luo2017non ; zhang2017amulet ; xi2019salient , have been widely used for saliency detection owing to their high capacity of modeling high-level semantics. Even though these methods have achieved promising results, there are still some challenges due to the complicated scenarios of some images. The learned features by these methods usually lack the ability to i) suppress the non-salient regions which have "salient-like" appearance as depicted in the first row of Figure 1, ii) detect salient objects that have different-looking regions as depicted in the second row of Figure 1.
Figure 1: Examples of complicated scenarios in salient object detection. In the first row, the triangular object has "salient-like" appearance. In the second row, the appearance of the feet of the doll is different from the rest of the doll. While both scenarios have caused confusion for two recent methods (BASNet qin2019basnet and SRM wang2017stagewise ), our method (denoted as CAGNet-R) is capable of handling these complicated scenarios and generating a more accurate prediction.
To address the above-mentioned challenges, we propose the Guide Module which takes advantage of the nature of the high-level and low-level features. By adopting this module, high-level features, which lack the fine spatial details of low-level features, can exploit the nature of low-level features as a guidance to make foreground and background regions more distinct, and thus it can suppress the non-salient regions that have "salient-like" appearance. For example, as illustrated in the first row of Figure 1, although the triangular object has "salient-like" appearance, it should not be labeled as salient object, since it is not the most interesting and prominent part of the image. From Figure 1, we can see that our method (denoted as GAGNet-R) is able to completely suppress the whole triangular object. Furthermore, by adopting the Guide Module, high-level features, which have the ability of category recognition of image regions because of containing high semantic information, can guide the selection of low-level features. By inspiring from the Channel Attention Block (CAB) proposed in yu2018learning , we give our model the ability to guide the selection of low-level features, which equips our network with the power of assigning foreground label to different-looking salient regions. As illustrated in the second row of Figure 1, the appearance of the feet of the doll is different from the rest of the doll, but as it can be seen, our method is able to highlight the whole doll as the salient object. Thus, by benefiting from the content-aware guidance provided by our Guide Modules, our method is able to handle these complicated scenarios.
Some previous salient object detection methods wang2016saliency ; wang2015deep ; zhang2017learning utilize subsequent single-scale convolutional and max pooling layers to produce deep features. Since salient objects have large variations in scale and location, the learned features by these methods might not be able to handle these complicated variations due to the limited field of view. Zhang et al. zhang2018bi use dilated convolutional layers for extracting multi-scale features. The dilated convolution inserts "holes" in the convolution kernels to enlarge the receptive field, which would cause the loss of local information, especially when the dilation rate increases. This problem is called the "gridding issue" which was explored in wang2018understanding . To address these problems, we introduce the Multi-scale Feature Extraction Module (MFEM) which is capable of capturing multi-scale contextual information by enabling densely connections within the multi-scale regions in the feature map. For each level of abstraction (i.e., stage) of the pre-trained backbone, we perform convolutions by adopting a 3×3 trivial convolutional layer and Global Convolutional Networks (GCNs) peng2017large with different kernel sizes. Then, the resulting feature maps are stacked to form multi-scale features. GCNs enable densely connections within a large k×k region in the feature map and thus can alleviate the "gridding issue".
In this paper, we propose a Content-Aware Guidance Network, which we refer to as CAGNet, consisting of three networks: (i) Feature Extraction Network (FEN), (ii) Feature Guide Network (FGN), (iii) Feature Fusion Network (FFN).
The FEN produces multi-scale features at multiple levels of abstraction by adopting the MFEM at each level of a pre-trained backbone. The FGN takes the extracted multi-scale features of the FEN as input and guides the features in order to use by the FFN. Then, by using multiple add operations and Residual Refinement Modules (RRMs) in the FFN, the guided features are fused effectively. Our proposed RRM is a residual block with spatial attention, which refines the features with the ability of focusing on salient regions and avoiding distractions in the non-salient regions. In summary, the FEN, FGN, and FFN in our proposed architecture work collaboratively to generate a more accurate prediction. Additionally, while most saliency detection methods in the literature use Cross-entropy loss for learning the salient objects, we design a loss function which outperforms the Cross-entropy by a large margin.
By conducting experiments on various backbones, we prove the robustness of our method. Furthermore, our method contains a lower number of parameters in comparison with the previous state-of-the-art methods. It is worth mentioning that since salient object detection is a pre-processing step for many computer vision tasks, it is important to evaluate the performance in terms of the running speed. Our method is capable of running at a real-time speed of 28 FPS, which guarantees that our network can be practically adopted as a pre-processing step for computer vision tasks.
In short, our main contributions are summarized as follows:
We propose the Feature Guide Network to equip our model with the power of i) making the foreground and background regions more distinct and suppressing the non-salient regions which have "salient-like" appearance; ii) detecting salient objects that have different-looking regions.
To extract powerful multi-scale features, we propose the Multi-scale Feature Extraction Module which adopts GCNs to enable densely connections within large regions. Additionally, this module helps the model to alleviate the "gridding issue".
We design a loss function that outperforms the widely-used Cross-entropy loss by a large margin.
Our method achieves great performance under different backbones, which shows that our proposed framework is very general with respect to the choice of the backbone model. It is interesting to note that while most methods in the saliency detection literature adopt a single backbone in their framework, we evaluate our framework on four different backbones to prove the generalization capability of our method.
The proposed method achieves the state-of-the-art on several challenging saliency detection datasets. Furthermore, our method contains a lower number of parameters compared to the previous state-of-the-art methods and can run at a real-time speed of 28 FPS.
2 Related work
Over the past years, numerous methods have been proposed for saliency detection. Traditional methods predict the saliency score based on hand-crafted features. Most of these methods utilize heuristic priors such as center prior aksac2017complex ; liang2018material , boundary background yang2013saliency , and color contrast cheng2014global . Aytekin et al. aytekin2018probabilistic propose a probabilistic framework to encode the boundary connectivity saliency cue and smoothness constraints into a global optimization problem. Shan et al. shan2018visual propose a graph-based approach and use background weight map to provide seeds for manifold ranking. Furthermore, they design a third-order smoothness framework to enhance the performance of manifold ranking. These methods, which are based on the traditional approaches, fail to capture semantic and high-level information of the objects.
Recently, deep Convolutional Neural Networks (CNNs) have shown their capabilities in extracting powerful features at multiple levels of abstraction. The CNN features can acquire a richer representation compared to the traditional hand-crafted features, and thus would result in performance improvement. In recent years, a vast number of methods have adopted CNNs for saliency detection task. For example, Li et al. li2015visual extract multi-scale features from a CNN and estimate the saliency score for each image super-pixel. Wang et al. wang2015deep employ two CNNs to combine local estimation of super-pixels and global proposal searching to predict saliency maps. Zhao et al. zhao2015saliency propose multi-context CNNs for exploiting local and global context for salient object detection. Although these CNN-based methods have shown better performance than the traditional methods, they are time-consuming because of taking image patches as input. Moreover, these methods fail to consider important spatial information of the whole image.
To overcome the above-mentioned problems, several methods have utilized FCNs to generate a pixel-wise prediction over the whole image directly. For instance, Li et al. li2016deep propose a multi-scale FCN to explore the semantic properties and visual contrast information of salient objects. Hou et al. hou2017deeply introduce short connections to combine features in different layers. Zhang et al. zhang2017amulet propose a resolution-based feature combination module to integrate multi-level feature maps into multiple resolutions, which captures spatial details and semantic information, simultaneously. Then, by fusing the predicted saliency maps in each resolution, the final saliency map is obtained. Zhang et al. zhang2018bi design a bi-directional message passing architecture to pass messages between multi-level features. Wang et al. wang2018detect propose to locate the salient objects globally and then refine them by taking advantage of local context information. Zhang et al. zhang2019hyperfusion employ a hyper-densely hierarchical feature fusion network to fuse the local and global multi-scale feature maps.
Most of the recent methods focus on using both high-level and low-level features for salient object detection. However, naively using these features may result in confusion for the network, and there needs to be an effective approach to use these features constructively. In this paper, we propose the Feature Guide Network which guides multi-level features to produce more effective features.
To obtain multi-scale features, some previous methods adopted parallel networks and fed them with re-scaled images li2017instance or multi-context super-pixels li2015visual . Different from these methods, we propose Multi-scale Feature Extraction Module (MFEM) to extract multi-scale features.
3 Our method
In this section, we first explain our proposed Content-Aware Guidance Network (CAGNet), consisting of three networks: (i) Feature Extraction Network which extracts multi-scale context information, (ii) Feature Guide Network which guides the extracted features by taking advantage of the spatial details of low-level features and the semantic information of high-level features, (iii) Feature Fusion Network which integrates guided features effectively to generate the saliency map. The architecture of the proposed CAGNet is illustrated in Figure 2. Finally, we describe our designed loss function that has better performance than the widely-used Cross-entropy loss.
Figure 2: The overall architecture of our proposed Content-Aware Guidance Network (CAGNet). CAGNet consists of three networks: (i) Feature Extraction Network which captures multi-scale contextual features, (ii) Feature Guide Network which guides the extracted features by taking advantage of the nature of high-level and low-level features, (iii) Feature Fusion Network which fuses the guided features effectively to generate the saliency map.
3.1 Feature Extraction Network
Feature Extraction Network consists of a pre-trained backbone that takes the input image and produces multi-level feature maps, and Multi-scale Feature Extraction Modules (MFEMs) which we apply them to multi-level feature maps to capture multi-scale contextual features.
3.1.1 Pre-trained backbone
In this study, we examine different pre-trained models in our CAGNet as the backbone model, including VGG-16 simonyan2014very , ResNet50 he2016deep , NASNet-Mobile zoph2018learning , and NASNet-large zoph2018learning , which are denoted as CAGNet-V, CAGNet-R, CAGNet-M, and CAGNet-L, respectively. These backbones are used to produce features at different levels of abstraction. To fit the need of saliency detection task, we remove all the fully connected layers in these backbones. In VGG-16, the features after the last max pooling layer cannot introduce a new level of abstraction. Thus, we use a convolutional layer with 1024 kernels of size 3×3 after the last max pooling layer in VGG-16 to produce a new level.
The output feature maps of all backbones are re-scaled by a factor of 32 with respect to the input image. We take feature maps at four levels from each backbone. Given an input image with size W×H, these feature maps have spatial sizes of W/2n×H/2n with n=2,3,4,5. The details of selected layers for different levels of abstraction in each backbone are shown in Table 1.
Level A
Level B
Level C
VGG-16 simonyan2014very
Conv3-3
(120×120×256)
(60×60×512)
The added layer
(15×15×1024)
ResNet50 he2016deep
Conv2-x
NASNet-Mobile zoph2018learning
1st reduction cell
(120×120×44)
4th normal cell
12th normal cell
NASNet-Large zoph2018learning
Table 1: Selected layers for different levels of abstraction in the adopted backbones. Note that we take the output of these layers for feature extraction. The size of the feature maps are shown in parentheses. The Level D in CAGNet-V (which is denoted as The added layer) is obtained by adding 1024 kernels of size 3×3 after the last max pooling layer of the original VGG-16.
3.1.2 Multi-scale Feature Extraction Module
Salient objects have large variations in scale and location in different images. Due to the variability of scale, using single scale convolution may not capture the right size. Moreover, due to the variability of location, using pyramid pooling as a multi-scale feature extractor, as proposed in wang2017stagewise , would cause the loss of important local information because of the large scale of pooling. Another approach to implement a multi-scale feature extractor is to use dilated convolutions like zhang2018bi , which enlarges the receptive field by inserting "holes" in the convolution kernels, and thus would result in the loss of local information because of sparse connections. This problem, which is called the "gridding issue", was explored in wang2018understanding .
Based on above observation, we find the Global Convolutional Networks (GCNs) peng2017large effective to address the "gridding issue" challenge. To avoid sparse connections and enable densely connections within a large k×k region in the feature map, GCN utilizes a combination of k×1+1×k and 1×k+k×1 convolutions to implement the k×k convolution effectively with a lower number of parameters compared with the trivial k×k convolution. More details about the GCN can be found in peng2017large . Furthermore, to obtain multi-scale contextual information, by taking advantage of GCNs, we propose the Multi-scale Feature Extraction Module (MFEM). This module consists of GCNs with different kernel sizes and can learn multi-scale context information for multiple abstraction levels.
As illustrated in Figure 3, in MFEM we perform convolutions by utilizing the 3×3 trivial convolution and GCNs with k=7,11,15. Then, the resulting feature maps are concatenated to form multi-scale features.
Figure 3: Multi-scale Feature Extraction Module (MFEM). MFEM adopts the 3×3 trivial convolution and GCNs with k=7,11,15 to extract multi-scale features. The '#' symbol denotes the number of layer filters. This figure shows MFEM with N=2.
3.2 Feature Guide Network
By employing the Feature Extraction Network, multi-scale features at multiple levels of abstraction are produced. We use four different levels of the Feature Extraction Network to extract multi-scale features. These different levels have different recognition information. High-level features have semantic and global information because of the large field of view. Thus, these features can help the category recognition of image regions. Low-level features have spatial and local information due to the small field of view. Therefore, the information of low-level features can help to better locate the salient regions.
Based on above observation, we propose the Feature Guide Network to better exploit the diverse recognition abilities of different levels. Feature Guide Network is composed of multiple Guide Modules which help to produce more powerful features for saliency detection. As illustrated in Figure 4, Guide Module consists of Low-level Guide and High-level Guide branches. This module takes low-level and high-level features as inputs and outputs guided low-level and guided high-level features.
Figure 4: The illustration of the Guide Module. This module consists of High-level Guide and Low-level Guide branches and is adopted to guide the features of the different levels. Note that C shows the number of the channels of the input feature maps, and the '#' symbol denotes the number of layer filters.
In saliency detection, some non-salient regions may have "salient-like" appearance. As shown in the first row of Figure 1, the triangular object at the bottom of the image, which has "salient-like" appearance, may cause confusion for saliency prediction. To address this challenge, we take advantage of the nature of the lower levels to guide higher levels. In the lower levels, the Feature Extraction Network captures finer spatial information because of its smaller field of view compared to the higher levels. Thus, by applying a 1×1 convolution on concatenated high-level and low-level features, spatial weights are produced to weigh the spatial information of high-level features. With this design, high-level features, which lack the low-level cues, can exploit the fine spatial details of low-level features as a guidance to make salient and non-salient regions more distinguishable. Therefore, by guiding the spatial information of high-level features, our network is able to enhance the distinction of salient and non-salient regions and suppress the non-salient regions with "salient-like" appearance.
In some complicated scenarios, salient regions may have different appearances. As illustrated in the second row of Figure 1, the appearance of the feet of the doll is different from the rest of the doll. Assigning foreground label to these different-looking regions is challenging. To address this challenge, by inspiring from the Channel Attention Block (CAB) proposed in yu2018learning , we use the nature of high-level features to guide low-level features in our Feature Guide Network. High-level features have higher semantic information due to the large receptive field. By applying an architecture like Squeeze and Excitation Networks hu2018squeeze on concatenated high-level and low-level features, channel weights are generated to weight the channels of low-level feature maps. In this way, by utilizing high-level semantic information, the low-level features are guided to produce more attentive features. Thus, Guide Modules provide content-aware guidance for multi-level features, which would result in a more accurate prediction.
3.3 Feature Fusion Network
By adopting Feature Extraction Network and Feature Guide Network, guided multi-scale features at different levels of abstractions are obtained. To integrate these features effectively, we devise Feature Fusion Network. In this network, we use add operations to combine different feature maps. In order to refine the features effectively, we introduce Residual Refinement Module (RRM), which is schematically depicted in Figure 5. RRM is a residual block he2016deep ; he2016identity with spatial attention. This module is used to refine the features and has the ability of focusing on salient regions and avoiding distractions in the non-salient regions.
Figure 5: Residual Refinement Module (RRM). RRM is a residual block with spatial attention and is adopted to refine the features effectively. The '#' symbol denotes the number of layer filters.
By adopting multiple RRM modules and add operations in Feature Fusion Network, finally the saliency map is obtained by utilizing a convolutional layer with two 1×1 kernels with softmax activation.
3.4 Our designed loss function for learning the salient objects
In saliency detection literature, Cross-entropy loss function is widely used for learning the salient objects. However, the networks trained with Cross-entropy loss often differentiate boundary pixels with low confidence, which would result in the performance degradation. In this paper, we design a loss function that leads to better results compared to the Cross-entropy loss, as shown in the ablation analysis section. Let I={Im,m=1, … ,M}, Sm, and Gm denote the training images, saliency map for the m-th training image, and ground truth for the m-th training image, respectively. Our designed loss is formulated as:
L=α1LP+α2LR+α3LMAE (1)
where α1 , α2 and α3 are the balance parameters. We empirically set α1=1, α2=0.5, and α3=1. LP and LR are computed as:
LP=1−1MM∑m=1P(Sm,Gm) (2)
LR=1−1MM∑m=1R(Sm,Gm) (3)
where P(S.G) and R(S,G) are calculated similar to Precision and Recall:
P(S,G)=∑nsngn∑nsn+ϵ (4)
R(S,G)=∑nsngn∑ngn+ϵ (5)
where sn∈S and gn∈G , and ϵ is a regularization constant. LMAE calculates the discrepancy between the predicted saliency map S and the ground truth G:
LMAE=1MM∑m=1MAE(Sm,Gm) (6)
where MAE(S,G) is computed as :
MAE(S,G)=1N∑n∣sn−gn∣ (7)
where N denotes the total number of pixels. In ablation analysis section, we demonstrate that our designed loss function outperforms the Cross-entropy loss function.
4 Experiments
4.1 Datasets and evaluation metrics
The proposed method is evaluated on five public salient object detection datasets. ECSSD yan2013hierarchical contains 1,000 semantically meaningful and complex images with multiple objects of different sizes. DUT-OMRON yang2013saliency consists of 5,168 challenging images with high variety of content, each of which has complex background and one or two salient objects. HKU-IS li2015visual contains 4447 images with low color contrast. Images in this dataset are selected to include multiple foreground objects or objects touching the image boundary. DUTS wang2017learning dataset is currently the largest salient object detection dataset and comprised of 10,553 images in the training set and 5,019 images in the test set. Both training and test sets have very challenging scenarios. The PASCAL-S li2014secrets dataset has 850 natural images chosen from the PASCAL VOC 2010 everingham2010pascal segmentation dataset.
We use five metrics to evaluate the performance of our method as well as previous state-of-the-art saliency detection methods, including Precision-Recall (PR) curves, F-measure curves, Average F-measure (denoted as avgF) score, weighted F-measure (denoted as wF) score, and Mean Absolute Error (MAE) score. More detailed descriptions about these metrics can be found in borji2015salient ; margolin2014evaluate .
Precision is the fraction of correct salient pixels in the predicted saliency maps, and Recall is defined as the fraction of correct salient pixels in the ground truth. To calculate Precision and Recall, the binarized saliency map is compared against the ground truth mask. The threshold is varied from 0 to 1 to generate a sequence of binary masks. These binary masks are used to calculate (Precision, Recall) pairs and (F-measure, threshold) pairs to plot the PR curves and the F-measure curves.
The Average F-measure score is calculated by using the thresholding method suggested in achanta2009frequency . This threshold is used to generate binary maps for computing the F-measure which is defined as:
Fβ=(1+β2)⋅Precision⋅Recallβ2⋅Precision+Recall (8)
where β2 is set to 0.3 to weight precision more than recall. The weighted F-measure score margolin2014evaluate is also adopted for evaluating the performance. Finally, the MAE score is calculated as the average pixel-wise absolute difference between the ground truth mask and the predicted saliency map.
4.2 Implementation details
We develop our proposed method in Keras chollet2015keras framework using TensorFlow tensorflow2015-whitepaper backend. The backbone models (i.e., VGG-16 simonyan2014very , ResNet-50 he2016deep , NASNet Mobile zoph2018learning , and NASNet Large zoph2018learning ) are initialized with ImageNet russakovsky2015imagenet weights. In our experiments, the input image is uniformly resized into 480×480 pixels for training and testing. To reduce overfitting, two types of data augmentations are randomly employed: horizontal filliping and rotation (range of 0-12 degrees). We do not use validation set and train the model until its training loss converges. All the experiments are performed using the stochastic gradient descent with a momentum coefficient 0.9 and an initial learning rate of 8e -3 which is divided by 10 if no improvement in training loss is seen for 10 epochs. We perform our experiments on an NVIDIA 1080 Ti GPU. We will release our code, the trained models, and the predicted saliency maps upon the publication of the manuscript.
4.3 Comparison with the state-of-the-art
We compare our method with 16 previous state-of-the-art methods, namely MDF li2015visual , RFCN wang2016saliency , UCF zhang2017learning , Amulet zhang2017amulet , NLDF luo2017non , DSS hou2017deeply , BMPM zhang2018bi , PAGR zhang2018progressive , PiCANet liu2018picanet , SRM wang2017stagewise , DGRL wang2018detect , MLMS wu2019mutual , AFNet feng2019attentive , CapSal zhang2019capsal , BASNet qin2019basnet , and CPD wu2019cascaded . For a fair comparison, we use the saliency maps provided by the authors.
Figure 6: The PR curves of the proposed method with previous state-of-the-art methods.
Figure 7: The F-measure curves of the proposed method with previous state-of-the-art methods.
Quantitative Evaluation. P-R curves and F-measure curves on the five datasets are shown in Figure 6 and Figure 7, respectively. We can see that our proposed method performs favorably against other methods in all cases. Especially, it is obvious that our CAGNet-L performs better than all other methods by a relatively large margin. Moreover, we compare our method with other previous state-of-the-art methods in terms of avgF score, wF score, and MAE score on five benchmark datasets in Table 2. As seen from this table, our method ranks first in most cases. It is interesting to note that our method contains fewer parameters than the existing ones, is end-to-end, and does not need any post-processing step such as CRF krahenbuhl2011efficient . Another interesting thing about our method is that although our CAGNet-M has significantly fewer parameters than the other networks (only 5.57 million parameters), it has shown outstanding performance. This functionality is desirable for the applications in which we have limitation in terms of the memory. Furthermore, our CAGNet-V has a real-time speed of 28 FPS when processing a 480×480 image, and therefore it can be practically adopted as a preprocessing step for computer vision tasks.
Dataset DUTS-TE wang2017learning ECSSD yan2013hierarchical DUT-O yang2013saliency PASCAL-S li2014secrets HKU-IS li2015visual
Metric Backbone #Par avgF wF MAE avgF wF MAE avgF wF MAE avgF wF MAE avgF wF MAE
VGG simonyan2014very
MDF li2015visual VGG16 56.86 0.669 0.588 0.093 0.807 0.705 0.105 0644 0.564 0.092 0.711 0.590 0.146 0.784 0.564 0.129
RFCN wang2016saliency VGG16 134.69 0.711 0.586 0.090 0.834 0.698 0.107 0.627 0.524 0.110 0.754 0.636 0.132 0.835 0.680 0.089
UCF zhang2017learning VGG16 23.98 0.631 0.596 0.112 0.844 0.806 0.069 0.621 0.573 0.120 0.738 0.700 0.116 0.823 0.779 0.062
Amulet zhang2017amulet VGG16 33.15 0.678 0.658 0.085 0.868 0.840 0.059 0.647 0.626 0.098 0.771 0.741 0.099 0.841 0.817 0.051
NLDF luo2017non VGG16 35.49 0.739 0.710 0.065 0.878 0.839 0.063 0.684 0.634 0.080 0.782 0.742 0.101 0.873 0.838 0.048
DSS hou2017deeply VGG16 62.23 0.716 0.702 0.065 0.873 0.836 0.062 0.674 0.643 0.074 0.776 0.728 0.103 0.856 0.821 0.050
PAGR zhang2018progressive VGG19 — 0.784 0.724 0.055 0.894 0.833 0.061 0.711 0.622 0.071 0.808 0.738 0.095 0.886 0.820 0.047
BMPM zhang2018bi VGG16 22.09 0.745 0.761 0.049 0.868 0.871 0.045 0.692 0.681 0.064 0.771 0.785 0.075 0.871 0.859 0.039
PiCANet liu2018picanet VGG16 32.85 0.749 0.747 0.054 0.885 0.865 0.046 0.710 0.691 0.068 0.804 0.781 0.079 0.870 0.847 0.042
MLMS wu2019mutual VGG16 74.38 0.745 0.761 0.049 0.868 0.871 0.044 0.692 0.681 0.064 0.771 0.785 0.075 0.871 0.859 0.039
AFNet feng2019attentive VGG16 21.08 0.793 0.785 0.046 0.908 0.886 0.042 0.738 0.717 0.057 0.828 0.804 0.071 0.888 0.869 0.036
CPD wu2019cascaded VGG16 29.32 0.813 0.801 0.043 0.914 0.895 0.040 0.745 0.715 0.057 0.832 0.806 0.074 0.895 0.879 0.033
CAGNet-V VGG16 20.98 0.823 0.797 0.044 0.915 0.893 0.042 0.744 0.718 0.057 0.831 0.799 0.077 0.906 0.886 0.033
ResNet he2016deep
SRM wang2017stagewise ResNet50 43.74 0.753 0.722 0.059 0.892 0.853 0.054 0.707 0.658 0.069 0.803 0.762 0.087 0.874 0.835 0.046
DGRL wang2018detect ResNet50 126.35 0.794 0.774 0.050 0.906 0.891 0.041 0.733 0.709 0.062 0.827 0.802 0.073 0.890 0.875 0.036
PiCANet-R liu2018picanet ResNet50 37.02 0.759 0.755 0.051 0.886 0.867 0.046 0.717 0.695 0.065 0.804 0.782 0.078 0.870 0.840 0.043
CapSal zhang2019capsal ResNet101 91.09 0.755 0.689 0.063 — — — — — — 0.827 0.791 0.074 0.841 0.780 0.058
BASNet qin2019basnet ResNet34 87.06 0.791 0.803 0.047 0.880 0.904 0.037 0.756 0.751 0.056 0.781 0.800 0.077 0.895 0.889 0.032
CPD-R wu2019cascaded ResNet50 47.85 0.805 0.795 0.043 0.917 0.898 0.037 0.747 0.719 0.056 0.831 0.803 0.072 0.891 0.875 0.034
CAGNet-R ResNet50 26.06 0.838 0.817 0.040 0.921 0.903 0.037 0.753 0.729 0.054 0.847 0.820 0.067 0.910 0.893 0.030
NASNet zoph2018learning
CAGNet-M Mobile \ul5.57 0.852 0.832 0.037 0.933 0.916 0.034 0.764 0.743 0.052 0.846 0.819 0.069 0.919 0.904 0.028
CAGNet-L Large 89.42 \ul0.886 \ul0.871 \ul0.029 \ul0.943 \ul0.932 \ul0.026 \ul0.798 \ul0.779 \ul0.047 \ul0.877 \ul0.858 \ul0.053 \ul0.932 \ul0.921 \ul0.024
Table 2: Comparison of the proposed method and other 16 methods on five salient object detection datasets. in terms of avgF, wF, and MAE scores. CAGNet with VGG-16, ResNet50, NASNet-Mobile, and NASNet-Large backbones, are denoted as CAGNet-V, CAGNet-R, CAGNet-M, and CAGNet-L, respectively. The best score and the second best score under each setting are shown in red and blue, respectively, and the best score under all settings is underlined. The unit of the total number of parameters (denoted as #Par) is million. Note that the authors of zhang2018progressive did not release the code, and they just provided the saliency maps, and thus reporting the total number of parameters is not possible for this method.
Qualitative Evaluation. Some qualitative results are shown in Figure 8. Thanks to the proposed modules, it can be seen that our model is capable of highlighting the inner part of foreground regions in various complicated scenes. Furthermore, our model is able to suppress the background regions which are incorrectly labeled by other saliency detection methods. Thus, by taking advantage of different proposed modules, our method is able to handle various complex scenarios.
Figure 8: Qualitative comparisons with previous state-of-the-art methods. As it can be seen, our method is capable of predicting saliency maps that are closer to the ground truth compared to the other methods.
4.4 Ablation analysis
Our proposed CAGNet consists of three modules, including the Multi-scale Feature Extraction Module (MFEM), the Guide Module, and the Residual Refinement Module (RRM). We perform the ablation analysis on CAGNet-V by using three challenging large-scale datasets, namely DUTS-TE wang2017learning , DUT-O yang2013saliency , and HKU-IS li2015visual . In order to investigate the effectiveness of each module, we gradually add them to our base network. Our base network is obtained by applying the following modifications to the CAGNet: i) replacing the MFEM modules with 1×1 convolutions with the same number of filters, ii) removing Guide Modules from the network (which means that the multi-level features are not multiplied by the channel weights and the spatial weights), iii) removing the RRM modules from the model.
We perform ablation analysis by adding each module to our base network in a stepwise manner. The results are shown in Table 3. In this table, the base network is denoted as Base.
The effectiveness of Guide Module. We add the High-level Guide branch, Low-level Guide branch, and the both High-level and Low-level Guide branches (i.e., the Guide Module) to the base network, which are denoted as HG, LG, and GM, respectively in Table 3. As seen from this table, the performance improves, which shows the beneficial effect of using our Guide Module. Using this module results in i) making salient and non-salient regions more distinct and suppressing the non-salient regions that have "salient-like" appearance, ii) assigning foreground label to different-looking salient regions. To further investigate the effectiveness of our guide branches, we show a visual comparison for each branch in Figure 9. As it can be seen, when we add the High-level Guide branch to the base network (Base+HG), the non-salient regions that have "salient-like" appearance are suppressed. Furthermore, when we add the Low-level Guide branch to the base network (Base+LG), different-looking salient regions (head of the pencil and the rest of the pencil, head of the bird and the rest of the bird) are labeled as salient.
DUTS-TE wang2017learning DUT-O yang2013saliency HKU-IS li2015visual
avgF wF MAE avgF wF MAE avgF wF MAE
0.7452 0.7099 0.0627 0.6481 0.6027 0.0862 0.8579 0.8298 0.0482
Base + HG
Base + LG
Base + GM
Base + GM + MFEM
Base + GM + MFEM + RRM
(= CAGNet-V)
CE Loss Function
Dilated Convolution
Trivial Convolution
Table 3: Ablation analysis of our proposed method with different settings. The best results are shown in red.
Figure 9: Visual Comparison for two branches of our Guide Module. The first and second rows show the comparison for High-level Guide branch (denoted as HG). The third and fourth rows show the comparison for Low-level Guide branch (denoted as LG).
The effectiveness of MFEM. Based on the aforementioned architecture, we replace the 1×1 convolutions with the MFEM modules. As seen in Table 3, our proposed MFEM has a beneficial effect on saliency detection and improves the results, which shows extracting multi-scale features can help to detect salient objects with different scales and locations.
The effectiveness of RRM. To reveal the effect of the RRMs, we add them to the aforementioned architecture. From Table 3, it can be observed that using our refinement module is helpful for saliency detection and improves the performance.
The effectiveness of our designed loss. To demonstrate the effectiveness of our designed loss function, we train our CAGNet-V with Cross-entropy, denoted as CE Loss Function, in Table 3. As seen in this table, our designed loss outperforms the cross-entropy loss by a significant margin.
DUTS-TE wang2017learning
DUT-O yang2013saliency
HKU-IS li2015visual
avgF
#Par
N=1 0.8068 0.7777 0.0474 0.7254 0.6932 0.0611 0.8963 0.8734 0.0355 19.61
N=16 0.8189 0.7935 0.0451 0.7474 0.7205 0.0567 0.9050 0.8848 0.0328 22.86
Table 4: The results of CAGNet-V with different settings for the parameter N. The best results are shown in red. The unit of the total number of parameters (denoted as #Par) is million.
To further prove the effectiveness of our MFEM, we implement the MFEMs in CAGNet-V by adopting dilated convolutional layers (kernel size=3, dilation rates=1, 3, 5, 7), denoted as Dilated Convolution in Table 3. We can see that the performance degrades, which shows that our MFEM can capture more powerful multi-scale features by enabling densely connections within a large k×k region in the feature map. We also implement the MFEMs in CAGNet-V by adopting trivial convolutional layers with kernel size=3, 7, 11, 15, denoted as Trivial Convolution in Table 3. As seen from this table, the performance gets worse compared to our CAGNet-V with the proposed MFEM. It is interesting to note that CAGNet-V with our proposed MFEM contains fewer parameters than the CAGNet-V with the MFEM implemented by adopting trivial convolutional layers (20.98 million against 27.03 million), which is due to the architectural design of GCNs.
We perform another experiment on CAGNet-V and train it with different setting for the parameter N. The results are shown in Table 4. By considering the trade-off between the performance and the number of parameters, we have chosen N=8 for our method.
Figure 10: The predictions of our method for two images corrupted by Additive White Gaussian Noise (AWGN) with two variance values (σ=0.01 and σ=0.05).
5 Conclusion and future work
In this paper, we propose a novel end-to-end framework that has the power of i) making the foreground and background regions more distinct and suppressing the non-salient regions which have "salient-like" appearance; ii) detecting salient objects that have different-looking regions. Our proposed model is also capable of capturing multi-scale contextual information effectively. The attentive multi-scale guided features learned by our method and the great results of our deigned loss function proves that a promising approach for saliency detection is introduced in this paper. Experimental evaluations over five datasets demonstrate that our proposed method outperforms the previous state-of-the-art methods under different evaluation metrics.
Based on the great performance and real-time speed of our approach and its superiority over previous approaches, we plan to use our saliency detector in industrial object-related applications, such as object based surveillance and object tracking. However, in real-world scenarios, images are affected by noise, which would lead to performance degradation of the most recently introduced saliency detectors ijcai2018 including ours. Figure 10 shows the predicted saliency maps for images corrupted by Additive White Gaussian Noise (AWGN). As seen, our model fails to output accurate saliency predictions in the presence of noise. This motivates us to plan on enhancing the robustness of our method by handling noise in an end-to-end approach.
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(33) P. Zhang, W. Liu, Y. Lei, H. Lu, Hyperfusion-net: Hyper-densely reflective feature fusion for salient object detection, Pattern Recognition 93 (2019) 521–533.
(34) G. Li, Y. Xie, L. Lin, Y. Yu, Instance-level salient object segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 2386–2395.
(35) K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556.
(36) K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778.
(37) B. Zoph, V. Vasudevan, J. Shlens, Q. V. Le, Learning transferable architectures for scalable image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 8697–8710.
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(39) K. He, X. Zhang, S. Ren, J. Sun, Identity mappings in deep residual networks, in: European conference on computer vision, Springer, 2016, pp. 630–645.
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text:
NYSE:NOK
Nokia Stock Forecast, Price & News
Followed 101 Times
Adding Nokia Co.
Now: $4.08▼
MA: $4.00▼
Volume36.64 million shs
Average Volume38.22 million shs
Nokia Corporation engages in the network and technology businesses worldwide. The company operates in four segments: Ultra Broadband Networks, Global Services, IP Networks and Applications, and Nokia Technologies. It focuses on mobile radio including macro radio, small cells, and cloud native radio solutions for communications service providers and enterprises; and provides network planning and optimization, network implementation, and systems integration, as well as company-wide managed services. The company also offers fixed networking solutions, such as copper and fiber access products, solutions, and services. In addition, it provides network infrastructure and professional services for mobile networks; and managed services for the fixed, mobile, Internet protocol (IP), and optical domains. Further, the company offers network planning, implementation, operation, and maintenance services. Additionally, it provides IP/optical networking solutions, including IP routing and optical transport systems, software, and services; software solutions, such as customer experience management, network operations and management, communications and collaborations, policy and charging, as well as Cloud, IoT, security, and analytics platforms; and submarine networks and radio frequency systems. The company has a strategic collaboration with Microsoft; and a collaboration with CommScope Holding Company, Inc. to develop an interleaved passive-active antenna radio platform. Nokia Corporation was founded in 1865 and is headquartered in Espoo, Finland.
Nokia has received a consensus rating of Hold. The company's average rating score is 2.29, and is based on 6 buy ratings, 10 hold ratings, and 1 sell rating.
According to analysts' consensus price target of $4.98, Nokia has a forecasted upside of 22.1% from its current price of $4.08.
Nokia has been the subject of 10 research reports in the past 90 days, demonstrating strong analyst interest in this stock.
Nokia has received 918 "outperform" votes. (Add your "outperform" vote.)
Nokia has received 1,166 "underperform" votes. (Add your "underperform" vote.)
Nokia has received 55.95% "underperform" votes from our community.
MarketBeat's community ratings are surveys of what our community members think about Nokia and other stocks. Vote "Outperform" if you believe NOK will outperform the S&P 500 over the long term. Vote "Underperform" if you believe NOK will underperform the S&P 500 over the long term. You may vote once every thirty days.
Nokia does not currently pay a dividend.
Nokia does not have a long track record of dividend growth.
In the past three months, Nokia insiders have not sold or bought any company stock.
Only 4.21% of the stock of Nokia is held by institutions.
Earnings for Nokia are expected to decrease by -15.38% in the coming year, from $0.26 to $0.22 per share.
The P/E ratio of Nokia is 27.20, which means that it is trading at a more expensive P/E ratio than the market average P/E ratio of about 22.55.
The P/E ratio of Nokia is 27.20, which means that it is trading at a less expensive P/E ratio than the Computer and Technology sector average P/E ratio of about 72.67.
Price to Earnings Growth Ratio
Nokia has a PEG Ratio of 12.25. PEG Ratios above 1 indicate that a company could be overvalued.
Nokia has a P/B Ratio of 1.32. P/B Ratios below 3 indicates that a company is reasonably valued with respect to its assets and liabilities.
Stock Exchange NYSE
Industry Radio & t.v. communications equipment
SectorComputer and Technology
Current SymbolNYSE:NOK
CIK924613
Webwww.nokia.com
Phone358 0 10 44 88 000
Employees98,322
P/E Growth12.25
Annual Sales$26.12 billion
Book Value$3.08 per share
Net Income$7.84 million
Net Margins3.31%
Return on Equity9.34%
Outstanding Shares5,617,060,000
Market Cap$22.92 billion
Receive NOK News and Ratings via Email
Sign-up to receive the latest news and ratings for NOK and its competitors with MarketBeat's FREE daily newsletter.
Nokia (NYSE:NOK) Frequently Asked Questions
How has Nokia's stock been impacted by COVID-19 (Coronavirus)?
Nokia's stock was trading at $3.13 on March 11th, 2020 when COVID-19 (Coronavirus) reached pandemic status according to the World Health Organization. Since then, NOK stock has increased by 30.4% and is now trading at $4.08.
Is Nokia a buy right now?
17 Wall Street equities research analysts have issued "buy," "hold," and "sell" ratings for Nokia in the last twelve months. There are currently 1 sell rating, 10 hold ratings and 6 buy ratings for the stock. The consensus among Wall Street equities research analysts is that investors should "hold" Nokia stock. A hold rating indicates that analysts believe investors should maintain any existing positions they have in NOK, but not buy additional shares or sell existing shares.
View analyst ratings for Nokia or view MarketBeat's top 5 stock picks.
What stocks does MarketBeat like better than Nokia?
Wall Street analysts have given Nokia a "Hold" rating, but there may be better short-term opportunities in the market. Some of MarketBeat's past winning trading ideas have resulted in 5-15% weekly gains. MarketBeat just released five new trading ideas, but Nokia wasn't one of them. MarketBeat thinks these five stocks may be even better buys.
When is Nokia's next earnings date?
Nokia is scheduled to release its next quarterly earnings announcement on Thursday, February 4th 2021.
View our earnings forecast for Nokia.
How were Nokia's earnings last quarter?
Nokia Co. (NYSE:NOK) announced its earnings results on Thursday, October, 29th. The technology company reported $0.06 earnings per share for the quarter, meeting analysts' consensus estimates of $0.06. The technology company had revenue of $6.19 billion for the quarter, compared to analyst estimates of $6.41 billion. Nokia had a net margin of 3.31% and a return on equity of 9.34%.
View Nokia's earnings history.
What guidance has Nokia issued on next quarter's earnings?
Nokia updated its FY 2020 Pre-Market earnings guidance on Thursday, October, 29th. The company provided earnings per share guidance of 0.23-0.30 for the period, compared to the Thomson Reuters consensus estimate of $0.27.
What price target have analysts set for NOK?
17 Wall Street analysts have issued 12 month price objectives for Nokia's shares. Their forecasts range from $2.90 to $6.00. On average, they expect Nokia's stock price to reach $4.98 in the next twelve months. This suggests a possible upside of 22.1% from the stock's current price.
View analysts' price targets for Nokia or view Wall Street analyst' top-rated stocks.
Who are some of Nokia's key competitors?
Some companies that are related to Nokia include Telefonaktiebolaget LM Ericsson (publ) (ERIC), Motorola Solutions (MSI), Ubiquiti (UI), CommScope (COMM), Viasat (VSAT), Maxar Technologies (MAXR), EchoStar (SATS), Harmonic (HLIT), Cambium Networks (CMBM), Casa Systems (CASA), Sierra Wireless (SWIR), Comtech Telecommunications (CMTL), Loral Space & Communications Inc. (LORL), Ceragon Networks (CRNT) and Vuzix (VUZI).
View all of NOK's competitors.
What other stocks do shareholders of Nokia own?
Based on aggregate information from My MarketBeat watchlists, some companies that other Nokia investors own include Telefonaktiebolaget LM Ericsson (publ) (ERIC), NVIDIA (NVDA), Advanced Micro Devices (AMD), Intel (INTC), AT&T (T), QUALCOMM (QCOM), Cisco Systems (CSCO), Micron Technology (MU), General Electric (GE) and Tesla (TSLA).
Who are Nokia's key executives?
Nokia's management team includes the following people:
Mr. Pekka Ilmari Lundmark, Pres & CEO (Age 58)
Mr. Marco Wiren, Chief Financial Officer (Age 55)
Dr. Marcus Weldon, Corp. CTO & Pres of Nokia Bell Labs (Age 53)
Matt Shimao, Head of Investor Relations
Mr. Nassib Abou-Khalil, Chief Legal Officer (Age 49)
Ms. Susan Sheehan, Sr. VP of Communications
Ms. Stephanie Werner-Dietz, Chief HR Officer (Age 49)
Mr. Sergei Khraby, Head of Belarussian office
Mr. Kristian Pullola, Exec. Officer (Age 48)
Mr. Rajeev Suri, Advisor (Age 54)
What is Nokia's stock symbol?
Nokia trades on the New York Stock Exchange (NYSE) under the ticker symbol "NOK."
Who are Nokia's major shareholders?
Nokia's stock is owned by a variety of institutional and retail investors. Top institutional shareholders include Crossmark Global Holdings Inc. (0.01%), Kovack Advisors Inc. (0.00%), Pennsylvania Capital Management Inc. ADV (0.00%), Fulton Bank N.A. (0.00%), OneAscent Financial Services LLC (0.00%) and Raymond James Trust N.A. (0.00%).
Which major investors are selling Nokia stock?
NOK stock was sold by a variety of institutional investors in the last quarter, including Pacifica Partners Inc., Fulton Bank N.A., and Raymond James Trust N.A..
Which major investors are buying Nokia stock?
NOK stock was purchased by a variety of institutional investors in the last quarter, including Kovack Advisors Inc., Pennsylvania Capital Management Inc. ADV, Patriot Financial Group Insurance Agency LLC, Sheets Smith Wealth Management, OneAscent Financial Services LLC, Crossmark Global Holdings Inc., and Sowell Financial Services LLC.
How do I buy shares of Nokia?
Shares of NOK can be purchased through any online brokerage account. Popular online brokerages with access to the U.S. stock market include WeBull, Vanguard Brokerage Services, TD Ameritrade, E*TRADE, Robinhood, Fidelity, and Charles Schwab.
What is Nokia's stock price today?
One share of NOK stock can currently be purchased for approximately $4.08.
How big of a company is Nokia?
Nokia has a market capitalization of $22.92 billion and generates $26.12 billion in revenue each year. The technology company earns $7.84 million in net income (profit) each year or $0.25 on an earnings per share basis. Nokia employs 98,322 workers across the globe.
What is Nokia's official website?
The official website for Nokia is www.nokia.com.
How can I contact Nokia?
Nokia's mailing address is KARAKAARI 7, ESPOO H9, 02610. The technology company can be reached via phone at 358 0 10 44 88 000 or via email at [email protected]


Plants size, shape and adaptability are determined in large by their capacity to sustain, spur, redirect or arrest localized and whole-plant growth. We show that GA signaling and metabolism determines the level and extent of shoot and root growth. Increase of GA concentrations or signaling leads to increased shoot growth but suppresses root development. These GA-related responses are underpinned by sets of highly specialized in their functions enzymes and signaling factors and cross-talk with other hormonal pathways. The differential effects of GA on root and shoot growth and development are likely associated with a regulatory mechanism responding to optimum and stress conditions. Ongoing work in the laboratory employs genomics and genetics approaches to more thoroughly understand poplar growth and development under stress conditions including nitrogen and water limitations. We use genetic networks analysis and forward genetics approaches to identify key regulators of poplar roots response to stress. Besides overall growth, trees show incredible repertoire of spatiotemporal regulation of growth in relation to control of organ size, growth periodicity and tropic responses. We have identified and characterized novel regulators of these processes in poplar.


Welcome to Magnolia Meadows where we have been raising Tennessee Walking Horses for 21 years. We have carefully selected our breeding stock with an emphasis on temperament and gait and combined it with horses that are sweet and lovely to look at. We spend a lot of time on our horses, starting with imprinting and halter training at a young age. We then progress to ground work including driving from the ground until they are ready to go under saddle.
We spend a lot of time in the mountains riding, packing and driving. We also do a lot of sleigh riding in the winter and wagon trips in the summer.
We have a Canadian Champion stallion, Toddys Royal Image, and have shown several times at Spruce Meadows and the Mane Event in Red Deer.
If you want a horse for the show or the trail, come on out and try one of our Tennessee Walkers.


text:
Il Riviera Ligure di Ponente Vermentino Riviera dei Fiori è un vino DOC la cui produzione è consentita nella provincia di Imperia.

Caratteristiche organolettiche
colore: paglierino.
odore: delicato, caratteristico, fruttato.
sapore: asciutto, fresco, armonico, delicatamente fruttato.

Storia

Abbinamenti consigliati

Produzione
Provincia, stagione, volume in ettolitri
 nessun dato disponibile

Vini DOC della provincia di Imperia
Vini DOC e DOCG prodotti con uva Vermentino

redpajama_set_name: RedPajamaWikipedia


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text:
Nicola Sturgeon opens door to Westminster Labour pact to deliver indy dream
The First Minister revealed the SNP will 'work hard with others' to end Boris Johnson's premiership and trigger a general election.
Nicola Sturgeon opened the door to a deal with Labour leader Jeremy Corbyn to deliver independence (Image: REUTERS)
Nicola Sturgeon has given her strongest indication yet of a pact with Labour – in return for the right to hold an independence referendum next year.
Writing exclusively for the Sunday Mail, the First Minister has revealed her MSPs will begin planning for a new poll in 2020 immediately after returning from recess in three weeks' time.
She has said the SNP will "work hard with others" to end Boris Johnson's premiership but warned a key election issue will be "who has the right to decide Scotland's future".
Sturgeon has insisted that her Government has a "cast iron mandate" for a new vote and opened the door to a deal with Labour leader Jeremy Corbyn to deliver it.
She writes: "We'll continue to work hard with others to stop Brexit and remove the Tories from office.
"A UK general election seems inevitable sooner rather than later.
Nicola Sturgeon insisted that her Government has a "cast iron mandate" for a new indy vote (Image: Getty Images)
Labour Party civil war as MSPs raise 'serious concerns' over plans to allow indyref2
"In Scotland, with Labour too busy fighting themselves to care about jobs and living standards, that election will be a two-horse race between the SNP and the Tories.
"It will give us the opportunity to play our part in locking this right-wing Tory party out of government.
"A key issue will be this – who has the right to decide Scotland's future – Boris Johnson or the people of Scotland?
"It is perfectly legitimate to oppose independence but it can't be right to deny people in Scotland the right to choose – and just as Labour's position has crumbled in the last week, the Tories will find that trying to block democracy is an unsustainable position in a democratic country."
There is speculation Corbyn will trigger a no-confidence vote in Johnson over his Brexit plans. The votes of SNP MPs would be crucial in achieving that aim and also in propping up a potential Labour government.
If successful, it would almost certainly lead to a general election this autumn in which Labour could join forces with the SNP to force the Tories into opposition.
In return, Sturgeon would expect Corbyn as prime minister to grant a Section 30 order that would allow IndyRef2.
While predicting annihilation for Richard Leonard's Scottish Labour over his refusal to support a referendum, Sturgeon praised Shadow Chancellor John McDonnell's "refreshing" comments at the Edinburgh Fringe Festival last week that holding a new independence vote would be up to Holyrood.
She writes: "Since 2014, we have been hearing from Westminster parties that despite the people of Scotland having a clear right to decide their future, they'd block that right.
"That's to be expected from the anti-democratic Tories but Labour should always have known better.
Nicola Sturgeon said she'd "work hard with others" to oust Boris Johnson (Image: Getty Images)
'Jeremy Corbyn backs plan to allow indyref2' says Labour's shadow chancellor
"So it was a refreshing change to hear John McDonnell recognise and accept that it is the Scottish Parliament and people who should decide on the question of whether to hold a referendum on independence and that Westminster should not block that decision."
The FM added it was hard to see how Scottish Labour could recover from Leonard's refusal to support McDonnell's views and claimed many of the party's supporters north of the border were in "complete despair".
While Sturgeon would like to leverage an independence referendum by supporting Labour, there have also been rumours that Remain-supporting MPs in the Tory and Labour parties are prepared to join forces to form a "government of national unity" to oust Johnson and delay Brexit.
Scottish Tory leader Ruth Davidson has responded to McDonnell's remarks by saying that her party would "always stand up for our United Kingdom".
Scottish National Party


text:
package org.apache.hadoop.yarn.server.resourcemanager.webapp;

import org.apache.hadoop.yarn.server.resourcemanager.ResourceManager;
import org.apache.hadoop.yarn.server.resourcemanager.webapp.dao.ClusterInfo;
import org.apache.hadoop.yarn.util.Times;
import org.apache.hadoop.yarn.webapp.view.HtmlBlock;
import org.apache.hadoop.yarn.webapp.view.InfoBlock;

import com.google.inject.Inject;

public class AboutBlock extends HtmlBlock {
  final ResourceManager rm;

  @Inject
  AboutBlock(ResourceManager rm, ViewContext ctx) {
    super(ctx);
    this.rm = rm;
  }

  @Override
  protected void render(Block html) {
    html._(MetricsOverviewTable.class);
    ResourceManager rm = getInstance(ResourceManager.class);
    ClusterInfo cinfo = new ClusterInfo(rm);
    info("Cluster overview").
      _("Cluster ID:", cinfo.getClusterId()).
      _("ResourceManager state:", cinfo.getState()).
      _("ResourceManager HA state:", cinfo.getHAState()).
      _("ResourceManager HA zookeeper connection state:",
          cinfo.getHAZookeeperConnectionState()).
      _("ResourceManager RMStateStore:", cinfo.getRMStateStore()).
      _("ResourceManager started on:", Times.format(cinfo.getStartedOn())).
      _("ResourceManager version:", cinfo.getRMBuildVersion() +
          " on " + cinfo.getRMVersionBuiltOn()).
      _("Hadoop version:", cinfo.getHadoopBuildVersion() +
          " on " + cinfo.getHadoopVersionBuiltOn());
    html._(InfoBlock.class);
  }

}

redpajama_set_name: RedPajamaGithub


It's spring fair time and The Great Cow Race is about to begin! Play carnival games. Write love poetry. Outsmart a really big bee. Cheat the locals out of their hard-earned eggs. Bang on some pots and pans. Escape from an angry mob. Oh, and dress up like a cow.