Progressive Attention Networks for Visual Attribute Prediction
We propose a novel attention model which can accurately attend to target objects of various scales and shapes in images. The model is trained to gradually suppress irrelevant regions in an input image via a progressive attentive process over multiple layers of a convolutional neural network. The attentive process in each layer determines whether to pass or suppress features at certain spatial locations for use in the next layer. We further employ local contexts to estimate attention probability at each location since it is difficult to infer accurate attention by observing a feature vector from a single location only. The experiments on synthetic and real datasets show that the proposed attention network outperforms traditional attention methods in visual attribute prediction tasks.
This paper presents a hierarchical attention model that uses multiple stacked layers of soft attention in a convnet. The authors provide results on a synthetic dataset in addition to doing attribute prediction on the Visual Genome dataset. Overall I think this is a well executed paper, with good experimental results and nice qualitative visualizations. The main thing I believe it is missing would be experiments on a dataset like VQA which would help better place the significance of this work in context of other approaches. An important missing citation is Graves 2013 which had an early version of the attention model. Minor typo: *It confins possible attributes..* -> It confines.. *ImageNet (Deng et al., 2009), is used, and three additional* -> *.., are used,*
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Progressive Attention Networks for Visual Attribute Prediction
We propose a novel attention model which can accurately attend to target objects of various scales and shapes in images. The model is trained to gradually suppress irrelevant regions in an input image via a progressive attentive process over multiple layers of a convolutional neural network. The attentive process in each layer determines whether to pass or suppress features at certain spatial locations for use in the next layer. We further employ local contexts to estimate attention probability at each location since it is difficult to infer accurate attention by observing a feature vector from a single location only. The experiments on synthetic and real datasets show that the proposed attention network outperforms traditional attention methods in visual attribute prediction tasks.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This paper presents a hierarchical attention model that uses multiple stacked layers of soft attention in a convnet. The authors provide results on a synthetic dataset in addition to doing attribute prediction on the Visual Genome dataset. Overall I think this is a well executed paper, with good experimental results and nice qualitative visualizations. The main thing I believe it is missing would be experiments on a dataset like VQA which would help better place the significance of this work in context of other approaches. An important missing citation is Graves 2013 which had an early version of the attention model. Minor typo: *It confins possible attributes..* -> It confines.. *ImageNet (Deng et al., 2009), is used, and three additional* -> *.., are used,*
assistant
This paper presents a hierarchical attention model that uses multiple stacked layers of soft attention in a convnet. The authors provide results on a synthetic dataset in addition to doing attribute prediction on the Visual Genome dataset. Overall I think this is a well executed paper, with good experimental results and nice qualitative visualizations. The main thing I believe it is missing would be experiments on a dataset like VQA which would help better place the significance of this work in context of other approaches. An important missing citation is Graves 2013 which had an early version of the attention model. Minor typo: *It confins possible attributes..* -> It confines.. *ImageNet (Deng et al., 2009), is used, and three additional* -> *.., are used,*