text:
Ben Affleck's adaptation of Dennis Lehane's novel Gone, Baby, Gone stars Casey Affleck as Patrick Kenzie, a private investigator from working-class Boston who takes on a case involving a kidnapped girl. The girl's aunt begs Patrick to take the case because he has connections to criminal Boston that the police do not. He agrees and along with his partner, Angie Gennaro (Michelle Monaghan), they uncover a web of corruption that threatens the relationship between the two. Ed Harris and Morgan Freeman co-star as members of the Boston Police Department.
Actor: John Ashton, Amy Ryan, Amy Madigan
Director: Ben Affleck
Keywords: Gone Baby Gone, John Ashton, Amy Ryan
Uncle John
Law & Order: Special Victims Unit - Season 24
Law & Order - Season 22
Criminal Minds - Season 16
Blue Bloods - Season 13
Truth Be Told - Season 3
Big Sky - Season 3
Hunters - Season 2
CSI: Vegas - Season 2
Law & Order: Organized Crime - Season 3


How many different formulas of Tea For Two are there and what are the differences?
Thread: How many different formulas of Tea For Two are there and what are the differences?
Re: How many different formulas of Tea For Two are there and what are the differences?
I'm far from an expert on these matters, but I've owned both bottles (currently own the newer one in the second photo). I didn't notice much difference between them at all. Maybe the older bottle had slightly better longevity, but I can't be sure about that.
A great fragrance, which I enjoy for its honeyed tobacco note and black tea vibe. I've had a few people tell me it smells a bit 'odd' over the years. I tend to enjoy wearing this one at home. It's more of a 'zen' scent than a 'night out' fragrance to me.
I tried the old version, and found it similar to Luten's Five O'Clock Au Gingembre in spirit, but I'm not a fan of either to be honest.
PS: if you want a nice green tea note as a foil to orange flowers, try to get a sample of Carven Paris Séville.
My bottle looks like the one below and I have tried the one that you inhaled from the decant which came from the bottle in the picture. I didn't notice any difference in scent or performance.
Thanks folks. So they've rebottled the juice thrice it seems? Glad to hear its still the same.
I tried both bottles as pictured and the differences were minimal. The first bottle you listed had better longevity when I tried it many years ago.


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MMD GROUP has begun to develop since 1995. The company has begun importing meat and meat products from abroad into the Republic of Moldova.
Since 1997 the customers' network significantly expands throughout the country, among them developing network of supermarkets, meat processing plants and the largest wholesale deposits of the country.
goods in the Republic of Moldova, the management of the company decides to acquire its own refrigerated transport, which later has become a new kind of activity of the company. At present the Transportation and Logistics Department provides service and delivery of cargo by refrigerated transport units on the territory of Europe from the South to the CIS countries.
In 2000, the company signs a number of long-term supply contracts with the Western producers of beef, pork and poultry meat.
In 2001, due to the expansion of the market, increasing consumption and growing competition produced the necessity for distribution. The company has acquired its car fleet, trained the staff, and due to this, the distribution network has expanded. During the period from 2005 to 2010, MMD GROUP has been the largest importer of frozen and chilled meat products in the Republic of Moldova.
In March 2010, the company has decided to invest in the agricultural sector. The company has signed long-term industrial land lease contracts, has purchased, from the state, the agricultural equipment and the equipment for processing of agricultural raw materials.
In the autumn of 2010, the company has obtained the first finished products (canned peas, canned sweet corn and tomato paste).
To expand the range of the output products, the company has acquired the equipment for quick freezing of vegetables, products and berries (Frigoscandia).
In 2011 , the company has launched the production of frozen vegetables. Also, it has prepared and put into operation refrigerated warehouses with a capacity of up to 5000 tons.
The holding carries on its manufacturing and commercial activities on the territory of Moldova, Ukraine, Romania and Russia.
In 2013, there has been opened a branch of the company in Ukraine. The UMD Company has started to operate in Odessa.
"The Tiraspol Factory for Canned Baby Food"
The development of new directions of activity continues. We look to the future with confidence!


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Kong Phok and Barbara Lau, conducted by Oral History Interview with Kong Phok, December 19, 2000. Interview K-0273. Southern Oral History Program Collection (#4007).
Oral History Interview with Kong Phok, December 19, 2000. Interview K-0273. Southern Oral History Program Collection (#4007).
Kong Phok fled the Khmer Rouge in Cambodia with his family when he was very young, eventually arriving in Greensboro, North Carolina, at the age of nine. In this interview, he recalls adjusting to his new life in the United States, describing some of the cultural differences he encountered. He describes his work at Guilford Mills before the plant's owners moved it to Mexico. He recounts his struggles with discrimination at the mill, which he soon overcame, eventually earning a promotion to production manager. Conscious of his own good fortune, he treated his workers fairly and with kindness. This interview offers an instructive, if brief, look at North Carolina's mill industry from a different perspective: that of a recent immigrant to the state. It also offers insights into a Cambodian-American's effort to find a balance between his loyalty to his birthplace and his devotion to his adopted homeland.


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text:
Angel Subero, Jesse Taitt, and Grant Cook Join Music Department
The Music Department is delighted to welcome three new members: trombonist Angel Subero, pianist/composer Jesse Taitt, and arts/education administrator Grant Cook. Angel Subero will teach IIP lessons in trombone and low brass (MUS 55), Jesse Taitt will teach IIP lessons in jazz piano (MUS 53), and Grant Cook will work as Administrative Assistant with Samantha Candon in our front office.
Congratulations, Angel, Jesse, and Grant, and welcome to our community!
Please view their bios below.
Angel Subero
Venezuelan trombonist Angel Subero attended the Conservatorio Itinerante in Caracas, Venezuela, where he studied with the legendary Michel Becquet. Since coming to the United States in 2001 he has attended the Boston Conservatory Of Music, where he studied with Lawrence Isaacson, and at the New England Conservatory of Music with Douglas Yeo. He also studied with John Rojak at the Aspen Music Festival.
Mr. Subero has performed with the Boston Symphony Orchestra, the Boston Pops, the Pittsburgh Symphony Orchestra, the American Composers Orchestra, the Boston Modern Orchestra Project, the Iceland Symphony, the Boston Ballet Orchestra, the Starwars Symphony Orchestra, Naples Philharmonic, Albany Symphony Orchestra, the Boston Philharmonic, the Venezuela Symphony Orchestra, the Caracas Philharmonic, and the Gran Mariscal de Ayacucho Symphony, among others.
As a chamber musician, he has appeared with the Burning River Brass, the Camerata Pacifica, the Atlantic Brass Quintet, Boston Symphony Brass Ensemble and Boston Pops Brass Ensemble. He is also a member of the award-winning Triton Brass Quintet, the brass ensemble-in-residence at the Boston Conservatory Of Music. He can be heard on the Triton Brass debut recording release in 2014 under the Hip-Bone Music record label.
He has worked with such conductors as James Levine, Seiji Ozawa, John Williams, Kurt Masur, Keith Lockhart, Robert Spano, Sir Colin Davis, James Conlon, Rafael Fruhbeck de Burgos, David Robertson, Christoph von Dohnanyi, Charles Dutoit, Ingo Metzmacher, James Depreist, among many others.
In the realms of jazz, Latin and commercial music, Mr. Subero has appeared with artists such as Slide Hampton, Bob Brookmeyer, Jim McNeely, Claudio Roditi, Danilo Perez, New York Voices, Chris Botti, Aretha Franklin, Rockapella, Boston, the Temptations, the Ebonys, the Stylistics, the Manhattans, Jethro Tull, Oscar D'León, Dimension Latina, Isaac Delgado, Jose "Cheo" Feliciano, Larry Harlow, La Clave Secreta, Rebel Tumbao, Greg Hopkins Big Band, Bonerama, Ensamble Gurrufio, and Serenata Guayanesa, among others.
The Boston Modern Orchestra Project featured Angel Subero on their album From the Institutes of Groove, which was recorded on the BMOP/sound record label and released in 2013. Grammy-nominated composer Michael Gandolfi was commissioned by BMOP to write the title piece for Mr. Subero to premiere on the album. In a personal letter to the performer, John Williams wrote: "I found your recording of the Michael Gandolfi bass trombone concerto to be among the greatest examples of individuality and artistry applied to this fabulous instrument, that I can identify or remember."
Mr. Subero is on faculty at the Boston Conservatory of Music and the Longy School of Music of Bard College. He is also faculty at the Atlantic Brass Quintet Seminar, where he collaborates with members from both the Atlantic Brass Quintet and Triton Brass Quintet.
Angel Subero plays Edwards trombones exclusively and is a clinician and artist for Edwards Instrument Co. He has given master classes and recitals in the USA, Europe, Japan, Mexico, Colombia and Venezuela. He is a regular guest professor at the State Foundation for the National System of Youth and Children's Orchestras of Venezuela "EL SISTEMA."
Jesse Taitt
Jesse Taitt, a dynamic pianist/composer, completed a dual degree in Jazz Composition and Professional Music from Berklee College of Music. He began his journey developing his sensitivity and technical facility for the instrument at the Boston Arts Academy, where he studied classical music under Boston Conservatory Professor Harriet Lundberg and jazz with Hey Rim Jeon via the Berklee City Music Program.
As a student at the Arts Academy and later at Berklee College of Music, Jesse played and attended master classes with Phillip Bailey, Seymour Bernstein, Dr. Billy Taylor, Regina Carter, George Duke, Walter Beasley, Doug Johnson, Laszlo Gardony, Joanne Brackeen, Patrice Rushen, Geri Allen, Gavin Degraw, Hugh Masakela, Bill Charlap, Vincent Herring, and Cecil Bridgewater.
At Berklee, Jesse served as the music director for Boston-based group Jazz Urbane, which is led/produced by Dr. William Banfield. His band, the Jesse Taitt Trio, performs at Boston restaurants and nightspots. Jesse continues to be an active pianist in the Boston music scene. He is also an AmeriCorps Community Leader, Public Allies Leader and an Artist-Educator in Boston Arts Academy's Alumni Creative Core program.
During the summer of 2013, Jesse was an International Guest Artist of the U.S. Ambassador in Rwanda. There he led and co-led master classes with Joey Blake (Bobby McFerrin's Voicestra) and headlined both nights in Rwanda's annual Kigali UP Festival. Jesse's work can be found on Jeff Perry's Quartet, Sarpay Ozcatagay's Unexpectable and most recently on Dr. William Banfield's album Playing with Other People's Heads. There, Jesse shares album credits with the late George Duke as well as Terri Lyne Carrington, Stokely Williams and Christian Scott.
As he continues to explore music and the world, Jesse's dedication to excellence drives his creativity. Jesse Taitt is an accomplished pianist and composer who is constantly seeking new musical and philanthropic areas to explore. He is currently working as a sideman on numerous projects and working as a musician in Boston, New Jersey, and New York.
Grant Cook
I am an arts/education administrator and an enthusiastic student of ethnomusicology, folklore, and anthropology, interested in amateur and participatory traditions of music and dance in New England. A proud alum of the Dartmouth Music Department, I received my B.A. with a major in music and a minor in computer science before pursuing an M.A. in Music (Ethnomusicology) at Wesleyan University. I am compelled by the liberatory potential of music education to illuminate social histories of musicking in the experiences of ordinary people, equip students with the tools to take autoethnographic looks at their own musical lives, and kindle vivid imaginations of how we can organize ourselves—socially as well as musically.
In 2021, as Festival Director for through the square window, I headed the production team for an online festival of experimental music hosted by the London-based Vocal Constructivists ensemble. Before that, I held arts/education administration positions with the Wesleyan University Music Department, the Dartmouth Symphony Orchestra, and the Berkeley Symphony, in addition to course co-instructor and teaching assistant roles at Wesleyan and Dartmouth.
Academically, my work has explored the revivalist shape-note singing tradition in northern New England, nigun-singing cohorts in New Hampshire and Connecticut, riot grrrl and queercore punk subcultures, and the music of Vermont's Bread and Puppet Theater. My latest venture, still in its infancy, is an investigation into the role of the clarinet in old-time New England country dance music, before, during, and after the contra dance revival.
In my spare time, I like to loudly and amateurishly sing shape-note hymns and old ballads, clumsily pluck my Appalachian dulcimer and bass ukulele, cheerfully stumble through fiddle tunes on the clarinet, and curate Spotify playlists that are, by any measure, way too long.

redpajama_set_name: RedPajamaCommonCrawl


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text:
\section{Introduction}

Most approaches for counting similar objects in images assume a single object class~\cite{kang2018beyond, norouzzadeh2018automatically, onoro2016towards, falk2018u}; when is not, ad-hoc learning is necessary~\cite{lu2018class, setti2018count, laradji2018blobs}.
None of them are truly agnostic and multi-class, i.e., able to capture generic repeated patterns of different type \emph{without any tuning}.
The work of Laradji~\cite{laradji2018blobs} can be applied to different datasets after completely training the model; in the work of Lu~\cite{lu2018class} it is not required to fully retrain the model, but a specialization phase that involves the training of an adapter module with a few labeled samples is still required.
The work of Setti~\cite{setti2018count} is the most similar one to this work, in the sense that no training is required at all.
Counting approaches are based on regression (\cite{Zhang_2015_CVPR}) or density estimation  (\cite{onoro2016towards, kang2018beyond});
here we focus on counting by detection~\cite{setti2018count, subburaman2012counting, laradji2018blobs} so the counted objects are individually detected first.

Research on agnostic counting is important in many fields. It serves for visual question answering~\cite{jabri2016revisiting,zhang2018learning}, where counting questions could be made on too-specific entities, outside the semantic span of the available classes~\cite{agrawal2016analyzing} (e.g. ``What is the most occurrent thing?'' in Fig.~\ref{fig:lego}). In representation learning, unsupervised counting of visual primitives (i.e., visual ``things'') is crucial to obtain a rich image representation~\cite{onoro2016towards,noroozi2017representation,katsuki2016unsupervised}. Counting is a popular topic in cognitive  robotics~\cite{rucinski2014modelling,cangelosi2016embodied}, where autonomous agents learn by separating sensory input into a finite number of classes (without a precise semantics), building a classification system that counts on each
of them.

Application-wise, agnostic counting may help the manual tagging of training images ~\cite{andriluka2018fluid}, providing a starting guess for the annotator on  single-~\cite{spampinato2008detecting,joly2016lifeclef} or multi-spectral~\cite{ferrari2017bacterial,hollings2018you} images.
Inpainting filters on image editing applications may benefit from a magic wand capturing repeated instances to remove. Examples of these applications are shown in Sec. \ref{sec:experandresult}.
These kinds of tasks require a framework that works \emph{without any kind of training/specialization phase}, since training data may not be available (e.g. a single image of that kind in which to perform counting or a small set of images without any annotations).

\begin{figure}[t!]
\includegraphics[width=8.0cm]{pics/lego.pdf}
  \caption{
\textbf{SIMCO on visual question answering}: the most occurrent object? SIMCO finds 47 LEGO heads.
  }
\label{fig:lego}

\end{figure}

In this paper, we present the SIMilarity-based object COunting (SIMCO) approach, which is completely agnostic, i.e. with no need of any \emph{ad-hoc} class-specific fine tuning, and multi-class, i.e. finding different types of repeated patterns. The method works on the assumption that the image contains repeated elements and those elements are the only ones that can be counted (outlier elements that are not repeated, are filtered away). It is a reasonable assumption, since it is usually desirable to count multiple repeated elements.
Two main ideas characterise SIMCO.

First, every object to be counted is considered as a specialization of a
basic 2D shape: this is particularly true with many and small objects
~\cite{rosch1976basic,yan2017intelligent} (see Fig.~\ref{fig:lego}: LEGO heads can be approximated as circles).
SIMCO incorporates this idea building upon a novel Mask-RCNN-based classifier, fine-tuned just once
on a novel synthetic shape dataset, \emph{InShape}.

The second idea is that one can perform unsupervised grouping of the detected objects, discovering
\emph{different} types of repeated entities (without resorting to a particular set of classes). SIMCO realizes this with a head branch in the network architecture implementing triplet losses, which provides a 64-dim embedding that maps objects close if they share the same shape class plus some appearance attributes. Affinity propagation clustering~\cite{frey2007clustering} finds groups over this embedding.
Results are state-of-the-art on cell counting benchmarks~\cite{arteta2014interactive}, and on the RepTile dataset~\cite{setti2018count}, explicitly suited for agnostic object counting. Qualitative results illustrate some of the many scenarios where SIMCO can definitely help.

\section{The SIMCO approach}
\label{sec:SIMCO}
The SIMCO two-step algorithm (Fig.~\ref{fig:scheme}) uses a deep architecture which is trained just once, and that generalizes to whichever image.
It is the first multi-class counting by detection approach in this sense: the first step (``Detection'', Sec.~\ref{sec:setup}) provides a single class of generic foreground objects; each detected object is described by a 64-dimensional neural feature vector. The second step (``Clustering'', Sec.~\ref{sec:clustering}) groups the detections into clusters so that each resulting cluster is a different visual ``thing'' with its own count.

\subsection{SIMCO detection} \label{sec:setup}
SIMCO builds upon the Mask-RCNN architecture~\cite{he2017mask} as implemented in~\cite{massa2018mrcnn}, due to its well-known detection capabilities, superior to the previous iterations of the model~\cite{ren2015faster} and with the addition of instance segmentation capabilities. Mask-RCNN is modified to provide, for each detection bounding box, a specific feature descriptor to perform clustering afterwards. Inspired by the work of~\cite{faugeras19833}, we consider each object to be counted as approximable by a particular 2D shape. This assumption is embedded into the detector by training it on a novel 2D shape dataset, \emph{InShape}.

\begin{figure}[tb!]

\begin{minipage}[b]{1.0\linewidth}
  \begin{center}

  \centerline{\includegraphics[width=8.5cm]{pics/taxonomy.pdf}}
   \end{center}
   \vspace{-2em}
  \caption{
2D shape ontology inspired by~\cite{niknam2011modeling}.}
    \label{fig:ontology}
\end{minipage}
\end{figure}

InShape is a synthetic dataset of 50k images.
Each one contains one or more basic shapes selected from the leaves of a 2D shape ontology inspired by~\cite{niknam2011modeling}: \emph{lined, triangle, rectangle, diamond, pentagon, hexagon, ellipse} (Fig. \ref{fig:ontology}).
For each shape, we generate instances by varying color (randomly in the RGB space) and scale (randomly from 5\% to 20\% of the image dimension). Instances of different shape classes are co-located in a single image in different spatial patterns (aligned with diverse geometrical layouts, or misaligned and following a Poisson spatial process). Images are made photorealistic (as they were taken from the real world as textural patterns) with the help of the Substance Designer tool (\url{www.allegorithmic.com}).
Three InShape samples are shown in Fig.~\ref{fig:dataset}. Each image is annotated with bounding box coordinates and labels modeling the shape class, size and color for each object.

\begin{figure}[tb!]

\begin{minipage}[b]{1.0\linewidth}
  \begin{center}
  \centerline{\includegraphics[width=8.5cm]{pics/FigureInShape.pdf}}
  \end{center}
  \vspace{-2em}
  \caption{
\textbf{InShape image samples}. These samples show how different basic shapes are merged together in InShape. In a), two ``types'' of instances are present, dark gray hexagons and light gray squares; in b), circles and lines; in c), lines.}
    \label{fig:dataset}
\end{minipage}
\end{figure}

\begin{figure*}[t!]
  \begin{center}
  \includegraphics[width=0.98\textwidth]{pics/schemaSIMCO.pdf}
  \end{center}
  \vspace{-1em}
  \caption{
\textbf{SIMCO scheme.} In red the standard Mask R-CNN~\cite{he2017mask} framework. In blue our new similarity head that is able to map similar objects close in the descriptors space. The descriptor can be use for the clustering procedure.}
\label{fig:scheme}

\end{figure*}

Mask-RCNN is modified by adding a new head branch dubbed \emph{similarity head}, providing a similarity-based visual descriptor \(desc(b)\) for each detection bounding box \(b \in D\), where \(D\) is defined as the set of all the detected entities in the current training batch. This branch is placed after the bounding box regression branch of the Mask-RCNN model~\cite{he2017mask}. The \(desc\) function is implemented as a 64-dimensional fully connected layer from the RoI features corresponding to the computed box \(b\). The output of this layer is constrained on the 64-dimensional hypersphere~\cite{ding2015deep} (achieved by normalizing each embedding i.e. \(\|desc(b)\|_{2} = 1\)).

We train this similarity-based descriptor so that: 1) instances of the same class (same basic shape) with the same color and scale (same ''type'') are maximally close; 2) object instances of the same class with different color and/or scale or object instances of different classes have high distance.
These two conditions are formally implemented as a triplet loss \(L_{sim}\), and added to the Mask-RCNN loss as $(L = L_{cls} + L_{box} + L_{mask} + L_{sim})$, with the first three terms being standard~\cite{massa2018mrcnn}, while
the new \(L_{sim}\) is defined as:

\begin{footnotesize}
\noindent
\vspace{1em}
\[\sum_{\substack{(a,p) \in P \\ (a,n) \in N}} max( \|desc(a) - desc(p)\|_{2} - \|desc(a) - desc(n)\|_{2} + \alpha, 0)\]
\end{footnotesize}

Above, \(P\) and \(N\) are respectively the set of positive detection pairs and the set of negative detection pairs, with \(P,N  \subseteq D\times D\); the definition of positive or negative object instance pairs is better described in Sec.~\ref{sec:clustering}.
One may note that during training the descriptors of each detection are compared not only within a single image, but also between detections belonging to different images in the same batch, according to the \emph{Batch All} strategy as presented in~\cite{ding2015deep, hermans2017defense}.
The training procedure follows the one presented in~\cite{he2017mask}.
After the training on InShape, SIMCO can be applied to any image with no further fine-tuning. While a domain shift will occur, the purpose of the InShape dataset is to provide a robust and general model that can overcome that as much as possible. For a given test image, it produces a set of detection bounding boxes \(D\) (see Fig.~\ref{fig:qualitative_fish} left), each one equipped with the related feature descriptor \(desc(b)\) (\(b\in D\)).

\subsection{SIMCO clustering}  \label{sec:clustering}
The embedding provided by the descriptor  \(desc(b)\) of Sec.~\ref{sec:setup} maps closely objects with the same shape and visual properties so that clustering can be applied to discover natural groupings. The idea is that each cluster is a ``visual thing''. Each cluster represent a class of objects that are distinguished from other classes in the image. The nature of the class depends on the other classes, it could be shape if there are differently shaped objects in the image (e.g. Fig. \ref{fig:dataset}a) or color if all the objects are of the same shape (e.g. Fig. \ref{fig:clusters_qual} bottom row) or a combination of the two (e.g. Fig. \ref{fig:bobode}).
In this phase, outliers that don't belong to any cluster are filtered away (making it possible to count only repeated elements).

As clustering procedure we choose the affinity propagation algorithm~\cite{frey2007clustering}, since it exploits measures of similarity between pairs of data points (over which we had the Mask-RCNN loss computed) and simultaneously considers all data points as potential exemplars (specific representative of clusters). Affinity propagation has a single parameter (the \emph{preference}) regulating the tendency to select less or more exemplars.
By varying this parameter one can appreciate how the embedded features organize the detections (Fig.~\ref{fig:qualitative_fish}): starting from a low value and subsequently increasing it, drastically different scales are first separated (Fig.~\ref{fig:qualitative_fish} in the center), followed by shape and color (Fig.~\ref{fig:qualitative_fish} on the right). The correct value of the preference depends on the desired granularity of the clustering. In a real application that depends on the desire of the user. More details on how to chose the parameter for testing are given in the experimental section.

\section{Experiments and Results}
\label{sec:experandresult}

We evaluate counting performance of SIMCO on the Cells~\cite{lempitsky2010learning} and RepTile~\cite{setti2018count} datasets and test it also on novel images to show diverse potential applications. Standard indexes of Mean Absolute Error (MAE) and Normalized Mean Absolute Error (NMAE) are evaluated~\cite{willmott2005advantages}.
These testing datasets are much different from the InShape training dataset, so there is a domain shift between the two, which is expected since the purpose of InShape training was to learn a general and robus description.
The challenge in this problem is being able to have good performance of images of any kind without being able to finetune the model on them. A domain shift is always expected and being able to generalize is a very challenging part of agnostic counting.
Datasets such as CLEVR~\cite{johnson2017clevr} or COCO Count ~\cite{chattopadhyay2017counting} are not suitable for our experiments because while they are counting datasets, objects in the image are often not repeated (which is a required assumption for our method) and they belong to a fixed set of classes, so the generalization capabilities of the method would not be tested, just the performance on that particular domain.
In all of the experiments, SIMCO has the Mask-RCNN ResNet50-FPN~\cite{massa2018mrcnn, lin2017feature} as backbone architecture

In the experiments we compare only with approaches that don't require any kind of training, so agnostic counting approaches such as the one of Lu~\cite{lu2018class} that require a specialization phase are not considered.

In the experiments with the Cells dataset, we compare with approaches that \emph{don't require any kind of seed initialization}. In the experiments with the RepTile dataset, we compare with approaches that can handle multiple classes by providing seeds for each object class before computation. SIMCO can work in both modalities and so can be compared with different approaches in the two experiments.

\begin{figure*}[t!]
  \begin{center}
  \includegraphics[width=0.98\textwidth]{pics/image_clustering.pdf}
  \end{center}
  \vspace{-1em}
  \caption{
\textbf{SIMCO clustering process.} By varying the preference value of the affinity propagation clustering, meaningful partitions are obtained. Left: results of the detection (no clustering). Center: the first separation individuates close and far fishes. Right: the second separation captures big circle-like fishes (red), foreground red fishes (green), background red fishes (cyan). It is important to note that while the clustering result changes based on the preference value, none of these results is wrong. A user could want to select each fish regardless of type or select each species separately. The ability to regulate the granularity of the classes is a feature of the method.}
\label{fig:qualitative_fish}

\end{figure*}
\begin{figure}[tb!]

\begin{minipage}[b]{1.0\linewidth}
\begin{center}
\includegraphics[width=0.9\textwidth]{pics/sec1.pdf}
\end{center}
\vspace{-1em}
\caption{
\textbf{SIMCO on challenging tasks.} Top row: three types of cells are spotted. Middle row: highly elongated shapes such as light/dark green fields are correctly captured. Bottom row: \emph{light and dark belly ducks} are separated into two clusters. This may help an annotator interested only in one of the two species. The high density of the mosaic of bee cells, the difficult shapes ratio in the second task and the fine-grained classes separation in the third further highlight the strengths of SIMCO. }
\label{fig:clusters_qual}
\end{minipage}
\vspace{-0.5em}
\end{figure}
\begin{figure}[tb!]
\begin{minipage}[b]{1.0\linewidth}
  \begin{center}
\includegraphics[width=0.95\textwidth]{pics/sec2.pdf}
\end{center}
\vspace{-1em}
\caption{
\textbf{Photoediting task.} A magic wand based on SIMCO can select clusters objects to be removed (dots and flowers on the left). Selected objects can be removed with inpainting (right).
}
\label{fig:magic_wand_etc}
\end{minipage}
\end{figure}

\textbf{Cells counting.}
The Cells dataset~\cite{lempitsky2010learning} contains images of a single class of cells in challenging spatial configurations (variable density, occlusions) where the detection capabilities of SIMCO can be fully tested
SIMCO counting performances on the Cells dataset are compared with those obtained by another
recent object proposal model that can be used on images \emph{without training on the dataset},  \emph{SharpMask}~\cite{pinheiro2016learning}, and by the \emph{Cai and Baciu}~\cite{cai2013detecting} counting by detection algorithm. These are both single-class general-purpose automated approaches. The SharpMask is generally not a counting approach, but in this experiment it can be used as such since the Cells images contain only one class of elements. By thresholding the proposals provided by the method based on the confidence and then counting how many proposals are found it is possible to use Sharpmask as an effective counting method for a single class of elements \emph{without having ever seen this kind of images} (like our proposed method). The threshold is chosen as the one that gives the best performance in terms of NMAE.
The Cai and Baciu approach follows the setting presented in Setti~\cite{setti2018count}.
To demonstrate the importance of using InShape for training we also report the results obtained training the same Mask-RCNN with COCO images. The same approach of thresholding based on confidence is used with the Mask-RCNN trained on COCO and with SIMCO.

Results are shown in Table~\ref{tab:cells_results}, reporting also the running time on a NVIDIA GeForce GTX 1080 GPU. They show SIMCO definitely performing better than the alternatives.
Results of~\cite{cai2013detecting} are poor due to the high spatial random displacements and the heavy occlusions of the Cells' images; SharpMask scores closer to SIMCO, but behaves badly on occlusions. Mask-RCNN trained on COCO provides the worst counting accuracy scores.

In the table, the results from \cite{lempitsky2010learning} on Cells are not shown as their experimental setting is much easier than ours: those results are obtained after training on a part of the dataset, while the results in the table are obtained \emph{having never seen those images before testing}.

The running time further promotes SIMCO, with one tenth of second per image.

\begin{table}
\begin{center}
\begin{tabular}{|l|l|l|l|l|}
\hline
\textbf{Method} & \multicolumn{2}{l|}{\textbf{Counting}} & \multicolumn{1}{l|}{\textbf{Running}}\\% & \multicolumn{1}{l|}{\textbf{Initialization}}\\
 & MAE & NMAE  & \textbf{Time (s)}  \\ \hline \hline
Cai and Baciu~\cite{cai2013detecting} & 149  & 0.809 & 753\\ \hline
SharpMask~\cite{pinheiro2016learning} & 42 & 0.21 & 8.76 \\ \hline \hline
COCO/Mask-RCNN & 175,65  & 0.99 & 0.12 \\ \hline
\textbf{SIMCO (no clustering)}  & \textbf{12}  & \textbf{0.07} & \textbf{0.11} \\ \hline
\end{tabular}
 \end{center}
\caption{
\label{tab:cells_results}
 Counting results on Cells~\cite{lempitsky2010learning} dataset.
 No clustering is performed since only a single class of elements is expected.
 }

\end{table}
\textbf{SIMCO on RepTile.}
To demonstrate the ability of SIMCO to count unknown objects of multiple classes we tested it on the RepTile dataset~\cite{setti2018count}.
RepTile is composed of 50 heterogeneous images (from hyperspectral imagery to aerial photos) with a total of 3173 annotated objects of varying and arbitrarily complex shapes (from candies to airplanes), taken at different scales, illumination conditions etc. In addition, each image has a few specific types of objects annotated while other are not, in order to simulate a user which is interested only in some ``things'' in the image. This makes it even more challenging, as these unannotated objects can be considered distractors that the method needs to deal with.
While the dataset is small, it is very challenging because each image is completely different from the others, so the generalization capabilities of the method can be tested.
This fits perfectly with the clustering parts of SIMCO. In particular, RepTile is thought for a human-in-the-loop approach, since each image has few ``seed'' detections, indicating the types of objects the user is interested to.
For a fair comparison with the other alternative semi-supervised approaches using seed objects as input, we designed an automatic procedure to set the preference parameter based on the input seeds' annotation. The procedure consists of increasing the preference parameter until each of the seed annotations is covered by a different cluster. Objects of these clusters are counted, while the others are discarded.

Compared methods are those of Cai and Baciu~\cite{cai2013detecting}, Arteta~\cite{arteta2014interactive} and Setti~\cite{setti2018count}, all exploiting the initial seed annotations (Cai and Baciu can work both automatically as in the Cells test, and with supervision as required by the RepTile protocol).
To demonstrate the advantages of clustering on our similarity branch output, we also report the results obtained with the affinity propagation clustering applied to the standard fully connected feature of the Mask-RCNN classification branch.
Table~\ref{tab:reptile_results_manual} details how SIMCO produces a little more than half of the errors than
Setti et al.~\cite{setti2018count}, with significantly lower running time, definitely overcoming all of the other algorithms. Qualitatively, one may appreciate
Fig.~\ref{fig:qualitative_fish}, showing the process of modulating the clustering until each of the desired seed detection was covered by a cluster; in particular for that image we stop at the second partition, being the yellow and the red fishes requested by the users.
The same clustering procedure is not as effective when applied to the features of the classification branch (see Table~\ref{tab:reptile_results_manual}).

\begin{figure}[tb!]
\begin{minipage}[b]{1.0\linewidth}
  \begin{center}
\includegraphics[width=8.0cm]{materials_pics/bobode.pdf}
\end{center}
\vspace{-1em}
\caption{\textbf{Herd animals found by SIMCO.} Clustering is able to not only distinguish between species but also between different varieties of the same type of animal. Cows are in red (for brown cows) or in cyan (for black cows). Green or yellow are sheep.
}
\label{fig:bobode}
\end{minipage}
\end{figure}

\textbf{SIMCO applications.} Due to his high generalization and speed, SIMCO can be the engine for many useful and fancy tasks. SIMCO may help, in a picture, to answer visual questions related to counting, as in the case of Fig.~\ref{fig:lego}.
In many application domains, it can be used to solve hard multi-class clustering and counting problems, as shown in Figs.~\ref{fig:clusters_qual},~\ref{fig:bobode}.
In top row we see a complex mosaic of three types of bee cells, which SIMCO is able to precisely spot.
In the middle row we show that SIMCO can handle also highly elongated shapes like fields partitions in remote sensing images, since InShape contains lines.
In the bottom row we show the ability of the method to discriminate fine-grained classes of animals (e.g. \emph{light/dark belly} ducks).
This property can be used, in zoology or other domains, to develop user tools for image annotation aimed at training classifiers for very specific species (e.g., \emph{light belly} ducks).

Finally, a photoediting example is shown in Fig.~\ref{fig:magic_wand_etc}: a magic wand driven by SIMCO can select clusters of similar objects with very few clicks, so that they can be simultaneously removed and inpainted afterwards thanks to the segmentation provided by the underlying Mask-RCNN.

While it could certainly be argued that SIMCO is inferior in raw counting performance to approaches that can be trained and specialized on the specific dataset, it is also true that for many applications (such as a magic wand in photoediting) it is not possible to provide any training set, since only one sample to elaborate may be available. SIMCO is flexible, being able to count with or without any seed initialization.

\begin{table}[t!]
\begin{center}
\begin{tabular}{|l|l|l|l|}
\hline

\textbf{Method} & \multicolumn{2}{l|}{\textbf{Counting}}& \multicolumn{1}{l|}{\textbf{Running}}\\
 & MAE & NMAE  & \textbf{Time (s)}  \\ \hline \hline
Cai and Baciu~\cite{cai2013detecting} & 59 & 1.034 & 2814\\ \hline
Arteta et al.~\cite{arteta2014interactive}& 50 & 1.629 & 685\\ \hline
Setti et al. TM & 18  & 0.186 & - \\ \hline
Setti et al. TM + CE & 18  & 0.164 & -\\ \hline
Setti et al. complete~\cite{setti2018count} & 14  & 0.109 & 867\\
\hline \hline
COCO/Mask-RCNN/FC & 46  & 0.521 & 0.18\\ \hline
InShape/Mask-RCNN/FC & 19  & 0.272 & 0.18\\ \hline

\textbf{SIMCO}  & \textbf{8.66}  & \textbf{0.086} & \textbf{0.18}\\
\hline
\end{tabular}
 \end{center}
\caption{
\label{tab:reptile_results_manual} Counting results on RepTile~\cite{setti2018count} dataset, exploiting the protocol of~\cite{setti2018count}.}
\end{table}
\vspace{-0.3em}

\section{Conclusion}
\label{sec:conclusion}
We presented SIMCO, a powerful and flexible framework to select and count clusters of similar objects in images. An extensive experimental testing showed that the main ideas behind the method, e.g. training the detection on a custom dataset made of photorealistic images with repeated basic shapes (InShape) and learning an optimal embedding for elements' clustering based on InShape annotations are particularly effective, making the framework suitable for a variety of practical applications in different domains.

\section*{Acknowledgments}
This work is partially supported by the Italian MIUR through PRIN 2017 - Project Grant 20172BH297: I-MALL - improving the customer experience in stores by intelligent computer vision.

\bibliographystyle{IEEEtran}

\section{Introduction}

Most approaches for counting similar objects in images assume a single object class~\cite{kang2018beyond, norouzzadeh2018automatically, onoro2016towards, falk2018u}; when is not, ad-hoc learning is necessary~\cite{lu2018class, setti2018count, laradji2018blobs}.
None of them are truly agnostic and multi-class, i.e., able to capture generic repeated patterns of different type \emph{without any tuning}.
The work of Laradji~\cite{laradji2018blobs} can be applied to different datasets after completely training the model; in the work of Lu~\cite{lu2018class} it is not required to fully retrain the model, but a specialization phase that involves the training of an adapter module with a few labeled samples is still required.
The work of Setti~\cite{setti2018count} is the most similar one to this work, in the sense that no training is required at all.
Counting approaches are based on regression (\cite{Zhang_2015_CVPR}) or density estimation  (\cite{onoro2016towards, kang2018beyond});
here we focus on counting by detection~\cite{setti2018count, subburaman2012counting, laradji2018blobs} so the counted objects are individually detected first.

Research on agnostic counting is important in many fields. It serves for visual question answering~\cite{jabri2016revisiting,zhang2018learning}, where counting questions could be made on too-specific entities, outside the semantic span of the available classes~\cite{agrawal2016analyzing} (e.g. ``What is the most occurrent thing?'' in Fig.~\ref{fig:lego}). In representation learning, unsupervised counting of visual primitives (i.e., visual ``things'') is crucial to obtain a rich image representation~\cite{onoro2016towards,noroozi2017representation,katsuki2016unsupervised}. Counting is a popular topic in cognitive  robotics~\cite{rucinski2014modelling,cangelosi2016embodied}, where autonomous agents learn by separating sensory input into a finite number of classes (without a precise semantics), building a classification system that counts on each
of them.

Application-wise, agnostic counting may help the manual tagging of training images ~\cite{andriluka2018fluid}, providing a starting guess for the annotator on  single-~\cite{spampinato2008detecting,joly2016lifeclef} or multi-spectral~\cite{ferrari2017bacterial,hollings2018you} images.
Inpainting filters on image editing applications may benefit from a magic wand capturing repeated instances to remove. Examples of these applications are shown in Sec. \ref{sec:experandresult}.
These kinds of tasks require a framework that works \emph{without any kind of training/specialization phase}, since training data may not be available (e.g. a single image of that kind in which to perform counting or a small set of images without any annotations).

\begin{figure}[t!]
\includegraphics[width=8.0cm]{pics/lego.pdf}
  \caption{
\textbf{SIMCO on visual question answering}: the most occurrent object? SIMCO finds 47 LEGO heads.
  }
\label{fig:lego}

\end{figure}

In this paper, we present the SIMilarity-based object COunting (SIMCO) approach, which is completely agnostic, i.e. with no need of any \emph{ad-hoc} class-specific fine tuning, and multi-class, i.e. finding different types of repeated patterns. The method works on the assumption that the image contains repeated elements and those elements are the only ones that can be counted (outlier elements that are not repeated, are filtered away). It is a reasonable assumption, since it is usually desirable to count multiple repeated elements.
Two main ideas characterise SIMCO.

First, every object to be counted is considered as a specialization of a
basic 2D shape: this is particularly true with many and small objects
~\cite{rosch1976basic,yan2017intelligent} (see Fig.~\ref{fig:lego}: LEGO heads can be approximated as circles).
SIMCO incorporates this idea building upon a novel Mask-RCNN-based classifier, fine-tuned just once
on a novel synthetic shape dataset, \emph{InShape}.

The second idea is that one can perform unsupervised grouping of the detected objects, discovering
\emph{different} types of repeated entities (without resorting to a particular set of classes). SIMCO realizes this with a head branch in the network architecture implementing triplet losses, which provides a 64-dim embedding that maps objects close if they share the same shape class plus some appearance attributes. Affinity propagation clustering~\cite{frey2007clustering} finds groups over this embedding.
Results are state-of-the-art on cell counting benchmarks~\cite{arteta2014interactive}, and on the RepTile dataset~\cite{setti2018count}, explicitly suited for agnostic object counting. Qualitative results illustrate some of the many scenarios where SIMCO can definitely help.

\section{The SIMCO approach}
\label{sec:SIMCO}
The SIMCO two-step algorithm (Fig.~\ref{fig:scheme}) uses a deep architecture which is trained just once, and that generalizes to whichever image.
It is the first multi-class counting by detection approach in this sense: the first step (``Detection'', Sec.~\ref{sec:setup}) provides a single class of generic foreground objects; each detected object is described by a 64-dimensional neural feature vector. The second step (``Clustering'', Sec.~\ref{sec:clustering}) groups the detections into clusters so that each resulting cluster is a different visual ``thing'' with its own count.

\subsection{SIMCO detection} \label{sec:setup}
SIMCO builds upon the Mask-RCNN architecture~\cite{he2017mask} as implemented in~\cite{massa2018mrcnn}, due to its well-known detection capabilities, superior to the previous iterations of the model~\cite{ren2015faster} and with the addition of instance segmentation capabilities. Mask-RCNN is modified to provide, for each detection bounding box, a specific feature descriptor to perform clustering afterwards. Inspired by the work of~\cite{faugeras19833}, we consider each object to be counted as approximable by a particular 2D shape. This assumption is embedded into the detector by training it on a novel 2D shape dataset, \emph{InShape}.

\begin{figure}[tb!]

\begin{minipage}[b]{1.0\linewidth}
  \begin{center}

  \centerline{\includegraphics[width=8.5cm]{pics/taxonomy.pdf}}
   \end{center}
   \vspace{-2em}
  \caption{
2D shape ontology inspired by~\cite{niknam2011modeling}.}
    \label{fig:ontology}
\end{minipage}
\end{figure}

InShape is a synthetic dataset of 50k images.
Each one contains one or more basic shapes selected from the leaves of a 2D shape ontology inspired by~\cite{niknam2011modeling}: \emph{lined, triangle, rectangle, diamond, pentagon, hexagon, ellipse} (Fig. \ref{fig:ontology}).
For each shape, we generate instances by varying color (randomly in the RGB space) and scale (randomly from 5\% to 20\% of the image dimension). Instances of different shape classes are co-located in a single image in different spatial patterns (aligned with diverse geometrical layouts, or misaligned and following a Poisson spatial process). Images are made photorealistic (as they were taken from the real world as textural patterns) with the help of the Substance Designer tool (\url{www.allegorithmic.com}).
Three InShape samples are shown in Fig.~\ref{fig:dataset}. Each image is annotated with bounding box coordinates and labels modeling the shape class, size and color for each object.

\begin{figure}[tb!]

\begin{minipage}[b]{1.0\linewidth}
  \begin{center}
  \centerline{\includegraphics[width=8.5cm]{pics/FigureInShape.pdf}}
  \end{center}
  \vspace{-2em}
  \caption{
\textbf{InShape image samples}. These samples show how different basic shapes are merged together in InShape. In a), two ``types'' of instances are present, dark gray hexagons and light gray squares; in b), circles and lines; in c), lines.}
    \label{fig:dataset}
\end{minipage}
\end{figure}

\begin{figure*}[t!]
  \begin{center}
  \includegraphics[width=0.98\textwidth]{pics/schemaSIMCO.pdf}
  \end{center}
  \vspace{-1em}
  \caption{
\textbf{SIMCO scheme.} In red the standard Mask R-CNN~\cite{he2017mask} framework. In blue our new similarity head that is able to map similar objects close in the descriptors space. The descriptor can be use for the clustering procedure.}
\label{fig:scheme}

\end{figure*}

Mask-RCNN is modified by adding a new head branch dubbed \emph{similarity head}, providing a similarity-based visual descriptor \(desc(b)\) for each detection bounding box \(b \in D\), where \(D\) is defined as the set of all the detected entities in the current training batch. This branch is placed after the bounding box regression branch of the Mask-RCNN model~\cite{he2017mask}. The \(desc\) function is implemented as a 64-dimensional fully connected layer from the RoI features corresponding to the computed box \(b\). The output of this layer is constrained on the 64-dimensional hypersphere~\cite{ding2015deep} (achieved by normalizing each embedding i.e. \(\|desc(b)\|_{2} = 1\)).

We train this similarity-based descriptor so that: 1) instances of the same class (same basic shape) with the same color and scale (same ''type'') are maximally close; 2) object instances of the same class with different color and/or scale or object instances of different classes have high distance.
These two conditions are formally implemented as a triplet loss \(L_{sim}\), and added to the Mask-RCNN loss as $(L = L_{cls} + L_{box} + L_{mask} + L_{sim})$, with the first three terms being standard~\cite{massa2018mrcnn}, while
the new \(L_{sim}\) is defined as:

\begin{footnotesize}
\noindent
\vspace{1em}
\[\sum_{\substack{(a,p) \in P \\ (a,n) \in N}} max( \|desc(a) - desc(p)\|_{2} - \|desc(a) - desc(n)\|_{2} + \alpha, 0)\]
\end{footnotesize}

Above, \(P\) and \(N\) are respectively the set of positive detection pairs and the set of negative detection pairs, with \(P,N  \subseteq D\times D\); the definition of positive or negative object instance pairs is better described in Sec.~\ref{sec:clustering}.
One may note that during training the descriptors of each detection are compared not only within a single image, but also between detections belonging to different images in the same batch, according to the \emph{Batch All} strategy as presented in~\cite{ding2015deep, hermans2017defense}.
The training procedure follows the one presented in~\cite{he2017mask}.
After the training on InShape, SIMCO can be applied to any image with no further fine-tuning. While a domain shift will occur, the purpose of the InShape dataset is to provide a robust and general model that can overcome that as much as possible. For a given test image, it produces a set of detection bounding boxes \(D\) (see Fig.~\ref{fig:qualitative_fish} left), each one equipped with the related feature descriptor \(desc(b)\) (\(b\in D\)).

\subsection{SIMCO clustering}  \label{sec:clustering}
The embedding provided by the descriptor  \(desc(b)\) of Sec.~\ref{sec:setup} maps closely objects with the same shape and visual properties so that clustering can be applied to discover natural groupings. The idea is that each cluster is a ``visual thing''. Each cluster represent a class of objects that are distinguished from other classes in the image. The nature of the class depends on the other classes, it could be shape if there are differently shaped objects in the image (e.g. Fig. \ref{fig:dataset}a) or color if all the objects are of the same shape (e.g. Fig. \ref{fig:clusters_qual} bottom row) or a combination of the two (e.g. Fig. \ref{fig:bobode}).
In this phase, outliers that don't belong to any cluster are filtered away (making it possible to count only repeated elements).

As clustering procedure we choose the affinity propagation algorithm~\cite{frey2007clustering}, since it exploits measures of similarity between pairs of data points (over which we had the Mask-RCNN loss computed) and simultaneously considers all data points as potential exemplars (specific representative of clusters). Affinity propagation has a single parameter (the \emph{preference}) regulating the tendency to select less or more exemplars.
By varying this parameter one can appreciate how the embedded features organize the detections (Fig.~\ref{fig:qualitative_fish}): starting from a low value and subsequently increasing it, drastically different scales are first separated (Fig.~\ref{fig:qualitative_fish} in the center), followed by shape and color (Fig.~\ref{fig:qualitative_fish} on the right). The correct value of the preference depends on the desired granularity of the clustering. In a real application that depends on the desire of the user. More details on how to chose the parameter for testing are given in the experimental section.

\section{Experiments and Results}
\label{sec:experandresult}

We evaluate counting performance of SIMCO on the Cells~\cite{lempitsky2010learning} and RepTile~\cite{setti2018count} datasets and test it also on novel images to show diverse potential applications. Standard indexes of Mean Absolute Error (MAE) and Normalized Mean Absolute Error (NMAE) are evaluated~\cite{willmott2005advantages}.
These testing datasets are much different from the InShape training dataset, so there is a domain shift between the two, which is expected since the purpose of InShape training was to learn a general and robus description.
The challenge in this problem is being able to have good performance of images of any kind without being able to finetune the model on them. A domain shift is always expected and being able to generalize is a very challenging part of agnostic counting.
Datasets such as CLEVR~\cite{johnson2017clevr} or COCO Count ~\cite{chattopadhyay2017counting} are not suitable for our experiments because while they are counting datasets, objects in the image are often not repeated (which is a required assumption for our method) and they belong to a fixed set of classes, so the generalization capabilities of the method would not be tested, just the performance on that particular domain.
In all of the experiments, SIMCO has the Mask-RCNN ResNet50-FPN~\cite{massa2018mrcnn, lin2017feature} as backbone architecture

In the experiments we compare only with approaches that don't require any kind of training, so agnostic counting approaches such as the one of Lu~\cite{lu2018class} that require a specialization phase are not considered.

In the experiments with the Cells dataset, we compare with approaches that \emph{don't require any kind of seed initialization}. In the experiments with the RepTile dataset, we compare with approaches that can handle multiple classes by providing seeds for each object class before computation. SIMCO can work in both modalities and so can be compared with different approaches in the two experiments.

\begin{figure*}[t!]
  \begin{center}
  \includegraphics[width=0.98\textwidth]{pics/image_clustering.pdf}
  \end{center}
  \vspace{-1em}
  \caption{
\textbf{SIMCO clustering process.} By varying the preference value of the affinity propagation clustering, meaningful partitions are obtained. Left: results of the detection (no clustering). Center: the first separation individuates close and far fishes. Right: the second separation captures big circle-like fishes (red), foreground red fishes (green), background red fishes (cyan). It is important to note that while the clustering result changes based on the preference value, none of these results is wrong. A user could want to select each fish regardless of type or select each species separately. The ability to regulate the granularity of the classes is a feature of the method.}
\label{fig:qualitative_fish}

\end{figure*}
\begin{figure}[tb!]

\begin{minipage}[b]{1.0\linewidth}
\begin{center}
\includegraphics[width=0.9\textwidth]{pics/sec1.pdf}
\end{center}
\vspace{-1em}
\caption{
\textbf{SIMCO on challenging tasks.} Top row: three types of cells are spotted. Middle row: highly elongated shapes such as light/dark green fields are correctly captured. Bottom row: \emph{light and dark belly ducks} are separated into two clusters. This may help an annotator interested only in one of the two species. The high density of the mosaic of bee cells, the difficult shapes ratio in the second task and the fine-grained classes separation in the third further highlight the strengths of SIMCO. }
\label{fig:clusters_qual}
\end{minipage}
\vspace{-0.5em}
\end{figure}
\begin{figure}[tb!]
\begin{minipage}[b]{1.0\linewidth}
  \begin{center}
\includegraphics[width=0.95\textwidth]{pics/sec2.pdf}
\end{center}
\vspace{-1em}
\caption{
\textbf{Photoediting task.} A magic wand based on SIMCO can select clusters objects to be removed (dots and flowers on the left). Selected objects can be removed with inpainting (right).
}
\label{fig:magic_wand_etc}
\end{minipage}
\end{figure}

\textbf{Cells counting.}
The Cells dataset~\cite{lempitsky2010learning} contains images of a single class of cells in challenging spatial configurations (variable density, occlusions) where the detection capabilities of SIMCO can be fully tested
SIMCO counting performances on the Cells dataset are compared with those obtained by another
recent object proposal model that can be used on images \emph{without training on the dataset},  \emph{SharpMask}~\cite{pinheiro2016learning}, and by the \emph{Cai and Baciu}~\cite{cai2013detecting} counting by detection algorithm. These are both single-class general-purpose automated approaches. The SharpMask is generally not a counting approach, but in this experiment it can be used as such since the Cells images contain only one class of elements. By thresholding the proposals provided by the method based on the confidence and then counting how many proposals are found it is possible to use Sharpmask as an effective counting method for a single class of elements \emph{without having ever seen this kind of images} (like our proposed method). The threshold is chosen as the one that gives the best performance in terms of NMAE.
The Cai and Baciu approach follows the setting presented in Setti~\cite{setti2018count}.
To demonstrate the importance of using InShape for training we also report the results obtained training the same Mask-RCNN with COCO images. The same approach of thresholding based on confidence is used with the Mask-RCNN trained on COCO and with SIMCO.

Results are shown in Table~\ref{tab:cells_results}, reporting also the running time on a NVIDIA GeForce GTX 1080 GPU. They show SIMCO definitely performing better than the alternatives.
Results of~\cite{cai2013detecting} are poor due to the high spatial random displacements and the heavy occlusions of the Cells' images; SharpMask scores closer to SIMCO, but behaves badly on occlusions. Mask-RCNN trained on COCO provides the worst counting accuracy scores.

In the table, the results from \cite{lempitsky2010learning} on Cells are not shown as their experimental setting is much easier than ours: those results are obtained after training on a part of the dataset, while the results in the table are obtained \emph{having never seen those images before testing}.

The running time further promotes SIMCO, with one tenth of second per image.

\begin{table}
\begin{center}
\begin{tabular}{|l|l|l|l|l|}
\hline
\textbf{Method} & \multicolumn{2}{l|}{\textbf{Counting}} & \multicolumn{1}{l|}{\textbf{Running}}\\% & \multicolumn{1}{l|}{\textbf{Initialization}}\\
 & MAE & NMAE  & \textbf{Time (s)}  \\ \hline \hline
Cai and Baciu~\cite{cai2013detecting} & 149  & 0.809 & 753\\ \hline
SharpMask~\cite{pinheiro2016learning} & 42 & 0.21 & 8.76 \\ \hline \hline
COCO/Mask-RCNN & 175,65  & 0.99 & 0.12 \\ \hline
\textbf{SIMCO (no clustering)}  & \textbf{12}  & \textbf{0.07} & \textbf{0.11} \\ \hline
\end{tabular}
 \end{center}
\caption{
\label{tab:cells_results}
 Counting results on Cells~\cite{lempitsky2010learning} dataset.
 No clustering is performed since only a single class of elements is expected.
 }

\end{table}
\textbf{SIMCO on RepTile.}
To demonstrate the ability of SIMCO to count unknown objects of multiple classes we tested it on the RepTile dataset~\cite{setti2018count}.
RepTile is composed of 50 heterogeneous images (from hyperspectral imagery to aerial photos) with a total of 3173 annotated objects of varying and arbitrarily complex shapes (from candies to airplanes), taken at different scales, illumination conditions etc. In addition, each image has a few specific types of objects annotated while other are not, in order to simulate a user which is interested only in some ``things'' in the image. This makes it even more challenging, as these unannotated objects can be considered distractors that the method needs to deal with.
While the dataset is small, it is very challenging because each image is completely different from the others, so the generalization capabilities of the method can be tested.
This fits perfectly with the clustering parts of SIMCO. In particular, RepTile is thought for a human-in-the-loop approach, since each image has few ``seed'' detections, indicating the types of objects the user is interested to.
For a fair comparison with the other alternative semi-supervised approaches using seed objects as input, we designed an automatic procedure to set the preference parameter based on the input seeds' annotation. The procedure consists of increasing the preference parameter until each of the seed annotations is covered by a different cluster. Objects of these clusters are counted, while the others are discarded.

Compared methods are those of Cai and Baciu~\cite{cai2013detecting}, Arteta~\cite{arteta2014interactive} and Setti~\cite{setti2018count}, all exploiting the initial seed annotations (Cai and Baciu can work both automatically as in the Cells test, and with supervision as required by the RepTile protocol).
To demonstrate the advantages of clustering on our similarity branch output, we also report the results obtained with the affinity propagation clustering applied to the standard fully connected feature of the Mask-RCNN classification branch.
Table~\ref{tab:reptile_results_manual} details how SIMCO produces a little more than half of the errors than
Setti et al.~\cite{setti2018count}, with significantly lower running time, definitely overcoming all of the other algorithms. Qualitatively, one may appreciate
Fig.~\ref{fig:qualitative_fish}, showing the process of modulating the clustering until each of the desired seed detection was covered by a cluster; in particular for that image we stop at the second partition, being the yellow and the red fishes requested by the users.
The same clustering procedure is not as effective when applied to the features of the classification branch (see Table~\ref{tab:reptile_results_manual}).

\begin{figure}[tb!]
\begin{minipage}[b]{1.0\linewidth}
  \begin{center}
\includegraphics[width=8.0cm]{materials_pics/bobode.pdf}
\end{center}
\vspace{-1em}
\caption{\textbf{Herd animals found by SIMCO.} Clustering is able to not only distinguish between species but also between different varieties of the same type of animal. Cows are in red (for brown cows) or in cyan (for black cows). Green or yellow are sheep.
}
\label{fig:bobode}
\end{minipage}
\end{figure}

\textbf{SIMCO applications.} Due to his high generalization and speed, SIMCO can be the engine for many useful and fancy tasks. SIMCO may help, in a picture, to answer visual questions related to counting, as in the case of Fig.~\ref{fig:lego}.
In many application domains, it can be used to solve hard multi-class clustering and counting problems, as shown in Figs.~\ref{fig:clusters_qual},~\ref{fig:bobode}.
In top row we see a complex mosaic of three types of bee cells, which SIMCO is able to precisely spot.
In the middle row we show that SIMCO can handle also highly elongated shapes like fields partitions in remote sensing images, since InShape contains lines.
In the bottom row we show the ability of the method to discriminate fine-grained classes of animals (e.g. \emph{light/dark belly} ducks).
This property can be used, in zoology or other domains, to develop user tools for image annotation aimed at training classifiers for very specific species (e.g., \emph{light belly} ducks).

Finally, a photoediting example is shown in Fig.~\ref{fig:magic_wand_etc}: a magic wand driven by SIMCO can select clusters of similar objects with very few clicks, so that they can be simultaneously removed and inpainted afterwards thanks to the segmentation provided by the underlying Mask-RCNN.

While it could certainly be argued that SIMCO is inferior in raw counting performance to approaches that can be trained and specialized on the specific dataset, it is also true that for many applications (such as a magic wand in photoediting) it is not possible to provide any training set, since only one sample to elaborate may be available. SIMCO is flexible, being able to count with or without any seed initialization.

\begin{table}[t!]
\begin{center}
\begin{tabular}{|l|l|l|l|}
\hline

\textbf{Method} & \multicolumn{2}{l|}{\textbf{Counting}}& \multicolumn{1}{l|}{\textbf{Running}}\\
 & MAE & NMAE  & \textbf{Time (s)}  \\ \hline \hline
Cai and Baciu~\cite{cai2013detecting} & 59 & 1.034 & 2814\\ \hline
Arteta et al.~\cite{arteta2014interactive}& 50 & 1.629 & 685\\ \hline
Setti et al. TM & 18  & 0.186 & - \\ \hline
Setti et al. TM + CE & 18  & 0.164 & -\\ \hline
Setti et al. complete~\cite{setti2018count} & 14  & 0.109 & 867\\
\hline \hline
COCO/Mask-RCNN/FC & 46  & 0.521 & 0.18\\ \hline
InShape/Mask-RCNN/FC & 19  & 0.272 & 0.18\\ \hline

\textbf{SIMCO}  & \textbf{8.66}  & \textbf{0.086} & \textbf{0.18}\\
\hline
\end{tabular}
 \end{center}
\caption{
\label{tab:reptile_results_manual} Counting results on RepTile~\cite{setti2018count} dataset, exploiting the protocol of~\cite{setti2018count}.}
\end{table}
\vspace{-0.3em}

\section{Conclusion}
\label{sec:conclusion}
We presented SIMCO, a powerful and flexible framework to select and count clusters of similar objects in images. An extensive experimental testing showed that the main ideas behind the method, e.g. training the detection on a custom dataset made of photorealistic images with repeated basic shapes (InShape) and learning an optimal embedding for elements' clustering based on InShape annotations are particularly effective, making the framework suitable for a variety of practical applications in different domains.

\section*{Acknowledgments}
This work is partially supported by the Italian MIUR through PRIN 2017 - Project Grant 20172BH297: I-MALL - improving the customer experience in stores by intelligent computer vision.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


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many of which provided slave labor for armaments factories and other industries, eventually generating millions of Reichsmarks for Nazi Germany. In March new gas chambers and crematoria opened at Auschwitz-Birkenau, dramatically increasing the camp's killing capacity.
A few hundred yards from Birkenau's gas chambers and crematoria was an area of the camp the inmates called "Canada." It was so named because Canada was thought to be a country of great riches. Inmates' possessions were taken from them upon arrival and brought there. The items were sorted and sent back to Germany, although some were stolen by SS guards.
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Helena Citrónová, a Slovakian Jew deported to Auschwitz in 1942, drew the attention of a SS guard named Franz Wunsch.
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Franz Wunsch was able to save Helena's sister by saying she worked for him in Canada, but he could do nothing for the children. Helena and her sister survived Auschwitz, and although her relationship with Wunsch never developed further, she did testify on his behalf years later at his war crimes trial.
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text:
Fire, Fire, Many Happen Here
By David Rattiner Posted on November 24, 2011
Credit: 4028mdk09
Something that has been kind of spooky this month is how many fires have taken place in the Hamptons. Our volunteer firefighters have had to battle blazes that have resulted in hospitalizations for exhaustion and smoke inhalation. There have been three major fires in the Hamptons this month. Entire buildings were completely engulfed in flames causing a total loss.
Firefighters in the Hamptons are a rare breed. They are always local, many with families established in their communities for decades, and the fire departments themselves feel like families. If you see any of these fine men and women in the street, stop them to shake their hand and to say thank you. They have been busy. [expand]
In East Hampton, the Hampton Market Place went up in flames on November 15. The grocery store and eatery is right by the East Hampton Y.M.C.A. RECenter and is known for their egg sandwiches. This was an especially dangerous fire because chlorofluorocarbon was present in the fumes due to the large refrigeration systems that were housed in the building to store food. You could smell the smoke by the movie theater on Main Street in East Hampton. Firefighters from East Hampton, Sag Harbor, Amagansett and Springs all responded. It remains unclear what started that fire. [expand]
Even more crazy, on that same day at Southampton Hospital's Emergency Room, a man tried to commit suicide by filling his car with poisonous chemicals right in front of the emergency room. So the five firefighters that had to be rushed to the hospital from the Hampton Market Place fire encountered a hazmat unit dealing with that emergency there.
On November 11, a Friday, a house fire in Amagansett believed to have been caused by two dogs who were left alone with a box of cupcakes inside of the house, completely destroyed the home. Both dogs died in the fire. An investigation revealed that most likely the dogs went after the cupcakes, which had been left on the stove in the kitchen, and accidentally turned on the stove.
So far, there is nothing suspicious about these fires, or any indication they were set deliberately, but it has been on the minds of a few people who have been discussing these incidents. One possible reason for these suspicions, is that an arsonist has just pleaded guilty for causing a house fire last year in East Hampton. A fire he set caused injuries to three firefighters.
The man also admitted to town police that he deliberately set fire to wood siding he'd found on that home, as well as twigs and leaves, and the blaze got out of control. His reason for lighting it? He was trying to keep warm, he said, while squatting at the home. One firefighter from East Hampton who responded to that fire suffered second degree burns on his legs. $360,000 worth of damage was done to the house.
The final bit of fire news was back on November 4, when there was a terrible fire in Water Mill that set ablaze an outdoor shed on the property of a home. The fire was next to two propane tanks that soon exploded, completely engulfing the shed. Fifty–five firefighters responded. The explosions from the propane tanks were heard throughout the entire area, frightening a lot of people, but our brave volunteers took care of that fire as well.
I don't know what the heck is going on with all of these fires in the Hamptons this month, but one thing I do know, I'm glad that my neighbors out there who are volunteer firefighters are always prepared, whenever the siren sounds, to battle a blaze 24 hours a day, seven days a week, 52 weeks a year.
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GRAMMY Museum and Pepperdine University Announce Affiliate Partnership
January 8, 2019 | 2 min read
Alumni | Faculty | Staff | Students
The GRAMMY Museum® at L.A. LIVE and Pepperdine University are proud to announce that the University has been named an official University Affiliate. The partnership, established through the GRAMMY Museum's University Affiliates program, will bring the museum's lauded traveling exhibits, tailored academic opportunities, and full wealth of resources to Pepperdine students, alumni, faculty, and staff.
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The affiliate relationship builds on a tradition of creative excellence at Pepperdine, which is home to distinguished faculty and alumni mentors, including renowned classical guitarist and GRAMMY® nominee Christopher Parkening, who leads Pepperdine's classical guitar program, and soprano Jessica Rivera ('96), Pepperdine alumna and 2007 GRAMMY® Award winner.
"Our University Affiliates Program is a great source of pride for the museum as it allows like-minded educational institutions to share valuable resources with one another," said Michael Sticka, GRAMMY Museum executive director. "We are thrilled to welcome Pepperdine University to our network and are eager to see what grows from this exceptional partnership."
Pepperdine Libraries will lead the partnership on behalf of the University and is expected to host its first GRAMMY Museum traveling exhibit in 2019.
"Our new affiliation with the GRAMMY Museum presents immense opportunities for enriching the cultural programming that we provide to the community at Pepperdine," said Mark Roosa, dean of libraries. "As the department leading the partnership, Pepperdine Libraries looks forward to hosting special exhibitions, guest speakers, and other programs that celebrate the musical and performing arts."
The GRAMMY Museum affiliation, which was penned in August 2018, complements Pepperdine's exclusive education partnership with AEG that offers sports and entertainment-focused education to Pepperdine students.
A plaque commemorating the partnership will be presented to Roosa by Kaitlyn Nader, director of community engagement for the GRAMMY Museum, and Stacie Takaoka, director of special projects for the GRAMMY Museum, Tuesday, January 8, 2019, at 1 PM in the Payson Library Surfboard Room at Pepperdine University's Malibu campus.
About the GRAMMY Museum
Established in 2008 as a partnership between the Recording Academy™ and AEG, the GRAMMY Museum is a nonprofit organization dedicated to cultivating a greater understanding of the history and significance of music. Paying tribute to our collective musical heritage, the museum explores and celebrates all aspects of the art form—from the technology of the recording process to the legends who've made lasting marks on our cultural identity. In 2017, the museum integrated with its sister organization, the GRAMMY Foundation®, to broaden the reach of its music education and preservation initiatives. As a unified organization today, the GRAMMY Museum fulfills its mission of making music a valued and indelible part of our society through exhibits, education, grants, and public programming.
Photo L-R: Kaitlyn Nader and Mark Roosa
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