Tree-structured Attention Module for Image Classification
Recent studies in attention modules have enabled higher performance in computer vision tasks by capturing global contexts and accordingly attending important features. In this paper, we propose a simple and highly parametrically efficient module named Tree-structured Attention Module (TAM) which recursively encourages neighboring channels to collaborate in order to produce a spatial attention map as an output. Unlike other attention modules which try to capture long-range dependencies at each channel, our module focuses on imposing non-linearities be- tween channels by utilizing point-wise group convolution. This module not only strengthens representational power of a model but also acts as a gate which controls signal flow. Our module allows a model to achieve higher performance in a highly parameter-efficient manner. We empirically validate the effectiveness of our module with extensive experiments on CIFAR-10/100 and SVHN datasets. With our proposed attention module employed, ResNet50 and ResNet101 models gain 2.3% and 1.2% accuracy improvement with less than 1.5% parameter over- head. Our PyTorch implementation code is publicly available.
The authors propose a simple and highly parametrically efficient module named TAM, recursively encouraging neighbouring channels to collaborate in order to produce a spatial attention map as an output. The paper offers a good read and diagrams are informative. Below are comments for improvement and clarification. - The authors claim the effectiveness of capturing inter-channel relations based on performance but it would be great if they can illustrate inter-channel relations. - Also, since the authors mentioned other works of attention modules (NLNet, GCNet, BAM, CBAM), it would be good to compare with them (although they compared with CBAM) - In Figure 6, on ResNet-50, TAM shows faster convergence and lower Top-1 Error but on ResNext-50, it is hard to see the difference. Any comment on this? - It would be interesting to see any segmentation task and performance comparison by imposing nonlinearities between neighboring channels and considering inter-channel relations.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Tree-structured Attention Module for Image Classification
Recent studies in attention modules have enabled higher performance in computer vision tasks by capturing global contexts and accordingly attending important features. In this paper, we propose a simple and highly parametrically efficient module named Tree-structured Attention Module (TAM) which recursively encourages neighboring channels to collaborate in order to produce a spatial attention map as an output. Unlike other attention modules which try to capture long-range dependencies at each channel, our module focuses on imposing non-linearities be- tween channels by utilizing point-wise group convolution. This module not only strengthens representational power of a model but also acts as a gate which controls signal flow. Our module allows a model to achieve higher performance in a highly parameter-efficient manner. We empirically validate the effectiveness of our module with extensive experiments on CIFAR-10/100 and SVHN datasets. With our proposed attention module employed, ResNet50 and ResNet101 models gain 2.3% and 1.2% accuracy improvement with less than 1.5% parameter over- head. Our PyTorch implementation code is publicly available.
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 authors propose a simple and highly parametrically efficient module named TAM, recursively encouraging neighbouring channels to collaborate in order to produce a spatial attention map as an output. The paper offers a good read and diagrams are informative. Below are comments for improvement and clarification. - The authors claim the effectiveness of capturing inter-channel relations based on performance but it would be great if they can illustrate inter-channel relations. - Also, since the authors mentioned other works of attention modules (NLNet, GCNet, BAM, CBAM), it would be good to compare with them (although they compared with CBAM) - In Figure 6, on ResNet-50, TAM shows faster convergence and lower Top-1 Error but on ResNext-50, it is hard to see the difference. Any comment on this? - It would be interesting to see any segmentation task and performance comparison by imposing nonlinearities between neighboring channels and considering inter-channel relations.
assistant
The authors propose a simple and highly parametrically efficient module named TAM, recursively encouraging neighbouring channels to collaborate in order to produce a spatial attention map as an output. The paper offers a good read and diagrams are informative. Below are comments for improvement and clarification. - The authors claim the effectiveness of capturing inter-channel relations based on performance but it would be great if they can illustrate inter-channel relations. - Also, since the authors mentioned other works of attention modules (NLNet, GCNet, BAM, CBAM), it would be good to compare with them (although they compared with CBAM) - In Figure 6, on ResNet-50, TAM shows faster convergence and lower Top-1 Error but on ResNext-50, it is hard to see the difference. Any comment on this? - It would be interesting to see any segmentation task and performance comparison by imposing nonlinearities between neighboring channels and considering inter-channel relations.