Spectral Nonlocal Block for Neural Network
The nonlocal network is designed for capturing long-range spatial-temporal dependencies in several computer vision tasks. Although having shown excellent performances, it needs an elaborate preparation for both the number and position of the building blocks. In this paper, we propose a new formulation of the nonlocal block and interpret it from the general graph signal processing perspective, where we view it as a fully-connected graph filter approximated by Chebyshev polynomials. The proposed nonlocal block is more efficient and robust, which is a generalized form of existing nonlocal blocks (e.g. nonlocal block, nonlocal stage). Moreover, we give the stable hypothesis and show that the steady-state of the deeper nonlocal structure should meet with it. Based on the stable hypothesis, a full-order approximation of the nonlocal block is derived for consecutive connections. Experimental results illustrate the clear-cut improvement and practical applicability of the generalized nonlocal block on both image and video classification tasks.
SUMMARY: - Propose spectral non-local block - improvement on image and video classification tasks Apologies, I am not at all familiar with the theory and math behind this proposal, I do not think I am in a position to review this paper. The experiments seem convincing enough that the authors made enough effort to prove their method might work. - Feature maps to show robustness of method is a good point - CIFAR-10 and CIFAR-100 are certainly a good start, but might not be the best datasets to test for image classification, in lieu of ImageNet and others. - Classification itself is a good start, it might be interesting to think about using this in a generative model such as GAN. The content reminds me of Self-Attention GAN which uses a similar non-local block (self-attention).
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Spectral Nonlocal Block for Neural Network
The nonlocal network is designed for capturing long-range spatial-temporal dependencies in several computer vision tasks. Although having shown excellent performances, it needs an elaborate preparation for both the number and position of the building blocks. In this paper, we propose a new formulation of the nonlocal block and interpret it from the general graph signal processing perspective, where we view it as a fully-connected graph filter approximated by Chebyshev polynomials. The proposed nonlocal block is more efficient and robust, which is a generalized form of existing nonlocal blocks (e.g. nonlocal block, nonlocal stage). Moreover, we give the stable hypothesis and show that the steady-state of the deeper nonlocal structure should meet with it. Based on the stable hypothesis, a full-order approximation of the nonlocal block is derived for consecutive connections. Experimental results illustrate the clear-cut improvement and practical applicability of the generalized nonlocal block on both image and video classification tasks.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



SUMMARY: - Propose spectral non-local block - improvement on image and video classification tasks Apologies, I am not at all familiar with the theory and math behind this proposal, I do not think I am in a position to review this paper. The experiments seem convincing enough that the authors made enough effort to prove their method might work. - Feature maps to show robustness of method is a good point - CIFAR-10 and CIFAR-100 are certainly a good start, but might not be the best datasets to test for image classification, in lieu of ImageNet and others. - Classification itself is a good start, it might be interesting to think about using this in a generative model such as GAN. The content reminds me of Self-Attention GAN which uses a similar non-local block (self-attention).
assistant
SUMMARY: - Propose spectral non-local block - improvement on image and video classification tasks Apologies, I am not at all familiar with the theory and math behind this proposal, I do not think I am in a position to review this paper. The experiments seem convincing enough that the authors made enough effort to prove their method might work. - Feature maps to show robustness of method is a good point - CIFAR-10 and CIFAR-100 are certainly a good start, but might not be the best datasets to test for image classification, in lieu of ImageNet and others. - Classification itself is a good start, it might be interesting to think about using this in a generative model such as GAN. The content reminds me of Self-Attention GAN which uses a similar non-local block (self-attention).