Count-guided Weakly Supervised Localization Based on Density Map
Weakly supervised localization (WSL) aims at training a model to find the positions of objects by providing it with only abstract labels. For most of the existing WSL methods, the labels are the class of the main object in an image. In this paper, we generalize WSL to counting machines that apply convolutional neural networks (CNN) and density maps for counting. We show that given only ground-truth count numbers, the density map as a hidden layer can be trained for localizing objects and detecting features. Convolution and pooling are the two major building blocks of CNNs. This paper discusses their impacts on an end-to-end WSL network. The learned features in a density map present in the form of dots. In order to make these features interpretable for human beings, this paper proposes a Gini impurity penalty to regularize the density map. Furthermore, it will be shown that this regularization is similar to the variational term of the -variational autoencoder. The details of this algorithm are demonstrated through a simple bubble counting task. Finally, the proposed methods are applied to the widely used crowd counting dataset the Mall to learn discriminative features of human figures.
The main contribution of the paper is the extension of techniques for weakly supervised localization, i.e. given ground truth counts of objects in a given image, one can do training to generate hidden layer density maps that allow for feature detection and localization of objects. The main contribution of the paper seems to be the regularization of the density map by incorporation of a Gini impurity penalty and the contrasting of the regularizer against beta-variational autoencoder formulation. The experiments show the utility of the method in a toy example and an example involving pedestrians in video surveillance. While the extensions are interesting, I find that the paper contributions are incremental and do not offer sufficient experimental results. The experiments are qualitative (just illustrations of results on a few images). While I understand the context of the contribution from authors point of view and can appreciate the study of weakly-supervised learning, I find it important to ask how the present framework compares with a traditional method that uses background subtraction (thus gaining appearance invariance to an extent) followed by geometric prior driven indexing of counts and locations (see for example: Lan Dong et al (ICCV 2007 - Fast Crowd Segmentation via Shape Indexing). The inductive bias and interpretability of design is extremely transparent and obvious. Can you please elaborate how your work compares in performance to such a technique and what you mean by feature interpretability in your context? My rating is based on the fact that the results are preliminary and further quantitative experimentation is needed.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Count-guided Weakly Supervised Localization Based on Density Map
Weakly supervised localization (WSL) aims at training a model to find the positions of objects by providing it with only abstract labels. For most of the existing WSL methods, the labels are the class of the main object in an image. In this paper, we generalize WSL to counting machines that apply convolutional neural networks (CNN) and density maps for counting. We show that given only ground-truth count numbers, the density map as a hidden layer can be trained for localizing objects and detecting features. Convolution and pooling are the two major building blocks of CNNs. This paper discusses their impacts on an end-to-end WSL network. The learned features in a density map present in the form of dots. In order to make these features interpretable for human beings, this paper proposes a Gini impurity penalty to regularize the density map. Furthermore, it will be shown that this regularization is similar to the variational term of the -variational autoencoder. The details of this algorithm are demonstrated through a simple bubble counting task. Finally, the proposed methods are applied to the widely used crowd counting dataset the Mall to learn discriminative features of human figures.
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>



The main contribution of the paper is the extension of techniques for weakly supervised localization, i.e. given ground truth counts of objects in a given image, one can do training to generate hidden layer density maps that allow for feature detection and localization of objects. The main contribution of the paper seems to be the regularization of the density map by incorporation of a Gini impurity penalty and the contrasting of the regularizer against beta-variational autoencoder formulation. The experiments show the utility of the method in a toy example and an example involving pedestrians in video surveillance. While the extensions are interesting, I find that the paper contributions are incremental and do not offer sufficient experimental results. The experiments are qualitative (just illustrations of results on a few images). While I understand the context of the contribution from authors point of view and can appreciate the study of weakly-supervised learning, I find it important to ask how the present framework compares with a traditional method that uses background subtraction (thus gaining appearance invariance to an extent) followed by geometric prior driven indexing of counts and locations (see for example: Lan Dong et al (ICCV 2007 - Fast Crowd Segmentation via Shape Indexing). The inductive bias and interpretability of design is extremely transparent and obvious. Can you please elaborate how your work compares in performance to such a technique and what you mean by feature interpretability in your context? My rating is based on the fact that the results are preliminary and further quantitative experimentation is needed.
assistant
The main contribution of the paper is the extension of techniques for weakly supervised localization, i.e. given ground truth counts of objects in a given image, one can do training to generate hidden layer density maps that allow for feature detection and localization of objects. The main contribution of the paper seems to be the regularization of the density map by incorporation of a Gini impurity penalty and the contrasting of the regularizer against beta-variational autoencoder formulation. The experiments show the utility of the method in a toy example and an example involving pedestrians in video surveillance. While the extensions are interesting, I find that the paper contributions are incremental and do not offer sufficient experimental results. The experiments are qualitative (just illustrations of results on a few images). While I understand the context of the contribution from authors point of view and can appreciate the study of weakly-supervised learning, I find it important to ask how the present framework compares with a traditional method that uses background subtraction (thus gaining appearance invariance to an extent) followed by geometric prior driven indexing of counts and locations (see for example: Lan Dong et al (ICCV 2007 - Fast Crowd Segmentation via Shape Indexing). The inductive bias and interpretability of design is extremely transparent and obvious. Can you please elaborate how your work compares in performance to such a technique and what you mean by feature interpretability in your context? My rating is based on the fact that the results are preliminary and further quantitative experimentation is needed.