MIST: Multiple Instance Spatial Transformer Networks
We propose a deep network that can be trained to tackle image reconstruction and classification problems that involve detection of multiple object instances, without any supervision regarding their whereabouts. The network learns to extract the most significant top-K patches, and feeds these patches to a task-specific network -- e.g., auto-encoder or classifier -- to solve a domain specific problem. The challenge in training such a network is the non-differentiable top-K selection process. To address this issue, we lift the training optimization problem by treating the result of top-K selection as a slack variable, resulting in a simple, yet effective, multi-stage training. Our method is able to learn to detect recurrent structures in the training dataset by learning to reconstruct images. It can also learn to localize structures when only knowledge on the occurrence of the object is provided, and in doing so it outperforms the state-of-the-art.
This paper proposes a method for multi-instance object classification and reconstruction that does not require any location-based supervision. The main contribution is the introduction of a differentiable top-K region proposal that allows to train the whole model with only a supervision of the total number of instances (and their class) in the image. They test the performance of their method in simple visual tasks like cluttered MNIST, street digit recognition and finding the basis of procedural texture generation. The paper is well written and motivated. The proposed method is clear and well formalized. Their reported results seem substantially better than the baselines they compare against. The additional experiments in their Appendix Fig. 7, analyzing the evolution of the heatmap loss, is interesting, although I think the heatmap visualization could be improved to better understand what the model is learning across iterations (improve legend to include at what iteration the heatmap is taken, give on what image this is evaluated). Unfortunately, their tasks seem quite easy, and it is hard to assess the impact of their method when working with more real-world data-sets, where the number of instances of every class is more loosely defined (we could always describe more objects in a real image from the COCO dataset for example). It seems of great importance to evaluate the limitations of their method in this direction, as the source of supervision might be too weak in the cases where the generated dataset might not have all the combinations of number of instances per image (as the cluttered MNIST has given that it’s procedurally generated). The results on SVHN are a little bit confusing, and it’s unclear what the “Supervised” method is, specially knowing that there are available methods that do obtain much better performance on this task using all the supervision available. It should also be better explained why the performance drops so drastically when the IoU threshold is increased. Finally, the texture generation experiments are very hard to interpret and are even further from real-world tasks. Given my concerns on how this unsupervised approach can scale to real-life datasets, I suggest a weak reject, but I think the proposed method has some interest for the community and I strongly encourage the authors to provide further evidence of performance of their method on more complex vision tasks.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
MIST: Multiple Instance Spatial Transformer Networks
We propose a deep network that can be trained to tackle image reconstruction and classification problems that involve detection of multiple object instances, without any supervision regarding their whereabouts. The network learns to extract the most significant top-K patches, and feeds these patches to a task-specific network -- e.g., auto-encoder or classifier -- to solve a domain specific problem. The challenge in training such a network is the non-differentiable top-K selection process. To address this issue, we lift the training optimization problem by treating the result of top-K selection as a slack variable, resulting in a simple, yet effective, multi-stage training. Our method is able to learn to detect recurrent structures in the training dataset by learning to reconstruct images. It can also learn to localize structures when only knowledge on the occurrence of the object is provided, and in doing so it outperforms the state-of-the-art.
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 proposes a method for multi-instance object classification and reconstruction that does not require any location-based supervision. The main contribution is the introduction of a differentiable top-K region proposal that allows to train the whole model with only a supervision of the total number of instances (and their class) in the image. They test the performance of their method in simple visual tasks like cluttered MNIST, street digit recognition and finding the basis of procedural texture generation. The paper is well written and motivated. The proposed method is clear and well formalized. Their reported results seem substantially better than the baselines they compare against. The additional experiments in their Appendix Fig. 7, analyzing the evolution of the heatmap loss, is interesting, although I think the heatmap visualization could be improved to better understand what the model is learning across iterations (improve legend to include at what iteration the heatmap is taken, give on what image this is evaluated). Unfortunately, their tasks seem quite easy, and it is hard to assess the impact of their method when working with more real-world data-sets, where the number of instances of every class is more loosely defined (we could always describe more objects in a real image from the COCO dataset for example). It seems of great importance to evaluate the limitations of their method in this direction, as the source of supervision might be too weak in the cases where the generated dataset might not have all the combinations of number of instances per image (as the cluttered MNIST has given that it’s procedurally generated). The results on SVHN are a little bit confusing, and it’s unclear what the “Supervised” method is, specially knowing that there are available methods that do obtain much better performance on this task using all the supervision available. It should also be better explained why the performance drops so drastically when the IoU threshold is increased. Finally, the texture generation experiments are very hard to interpret and are even further from real-world tasks. Given my concerns on how this unsupervised approach can scale to real-life datasets, I suggest a weak reject, but I think the proposed method has some interest for the community and I strongly encourage the authors to provide further evidence of performance of their method on more complex vision tasks.
assistant
This paper proposes a method for multi-instance object classification and reconstruction that does not require any location-based supervision. The main contribution is the introduction of a differentiable top-K region proposal that allows to train the whole model with only a supervision of the total number of instances (and their class) in the image. They test the performance of their method in simple visual tasks like cluttered MNIST, street digit recognition and finding the basis of procedural texture generation. The paper is well written and motivated. The proposed method is clear and well formalized. Their reported results seem substantially better than the baselines they compare against. The additional experiments in their Appendix Fig. 7, analyzing the evolution of the heatmap loss, is interesting, although I think the heatmap visualization could be improved to better understand what the model is learning across iterations (improve legend to include at what iteration the heatmap is taken, give on what image this is evaluated). Unfortunately, their tasks seem quite easy, and it is hard to assess the impact of their method when working with more real-world data-sets, where the number of instances of every class is more loosely defined (we could always describe more objects in a real image from the COCO dataset for example). It seems of great importance to evaluate the limitations of their method in this direction, as the source of supervision might be too weak in the cases where the generated dataset might not have all the combinations of number of instances per image (as the cluttered MNIST has given that it’s procedurally generated). The results on SVHN are a little bit confusing, and it’s unclear what the “Supervised” method is, specially knowing that there are available methods that do obtain much better performance on this task using all the supervision available. It should also be better explained why the performance drops so drastically when the IoU threshold is increased. Finally, the texture generation experiments are very hard to interpret and are even further from real-world tasks. Given my concerns on how this unsupervised approach can scale to real-life datasets, I suggest a weak reject, but I think the proposed method has some interest for the community and I strongly encourage the authors to provide further evidence of performance of their method on more complex vision tasks.