Published as a conference paper at ICLR 2018 LEARN TO PAY ATTENTION
We propose an end-to-end-trainable attention module for convolutional neural network (CNN) architectures built for image classification. The module takes as input the 2D feature vector maps which form the intermediate representations of the input image at different stages in the CNN pipeline, and outputs a 2D matrix of scores for each map. Standard CNN architectures are modified through the incorporation of this module, and trained under the constraint that a convex combination of the intermediate 2D feature vectors, as parameterised by the score matrices, must alone be used for classification. Incentivised to amplify the relevant and suppress the irrelevant or misleading, the scores thus assume the role of attention values. Our experimental observations provide clear evidence to this effect: the learned attention maps neatly highlight the regions of interest while suppressing background clutter. Consequently, the proposed function is able to bootstrap standard CNN architectures for the task of image classification, demonstrating superior generalisation over 6 unseen benchmark datasets. When binarised, our attention maps outperform other CNN-based attention maps, traditional saliency maps, and top object proposals for weakly supervised segmentation as demonstrated on the Object Discovery dataset. We also demonstrate improved robustness against the fast gradient sign method of adversarial attack.
This paper proposes a network with the standard soft-attention mechanism for classification tasks, where the global feature is used to attend on multiple feature maps of local features at different intermediate layers of CNN. The attended features at different feature maps are then used to predict the final classes by either concatenating features or ensembling results from individual attended features. The paper shows that the proposed model outperforms the baseline models in classification and weakly supervised segmentation.
Strength:
- It is interesting idea to use the global feature as a query in the attention mechanism while classification tasks do not naturally involve a query unlike other tasks such as visual question answering and image captioning.
- The proposed model shows superior performances over GAP in multiple tasks.
Weakness:
- There are a lot of missing references. There have been a bunch of works using the soft-attention mechanism in many different applications including visual question answering [A-C], attribute prediction [D], image captioning [E,F] and image segmentation [G]. Only two previous works using the soft-attention (Bahdanau et al., 2014; Xu et al., 2015) are mentioned in Introduction but they are not discussed while other types of attention models (Mnih et al., 2014; Jaderberg et al., 2015) are discussed more.
- Section 2 lacks discussions about related work but is more dedicated to emphasizing the contribution of the paper.
- The global feature is used as the query vector for the attention calculation. Thus, if the global feature contains information for a wrong class, the attention quality should be poor too. Justification on this issue can improve the paper.
- [H] reports the performance on the fine-grained bird classification using different type of attention mechanism. Comparison and justification with this method can improve the paper. The performance in [H] is almost 10 % point higher accuracy than the proposed model.
- In the segmentation experiments, the models are trained on extremely small images, which is unnatural in segmentation scenarios. Experiments on realistic settings should be included. Moreover, [G] introduces a method of using an attention model for segmentation, while the paper does not contain any discussion about it.
Overall, I am concerned that the proposed model is not well discussed with important previous works. I believe that the comparisons and discussions with these works can greatly improve the paper.
I also have some questions about the experiments:
- Is there any reasoning why we have to simplify the concatenation into an addition in Section 3.2? They are not equivalent.
- When generating the fooling images of VGG-att, is the attention module involved, or do you use the same fooling images for both VGG and VGG-att?
Minor comments:
- Fig. 1 -> Fig. 2 in Section 3.1. If not, Fig. 2 is never referred.
References
[A] Huijuan Xu and Kate Saenko. Ask, attend and answer: Exploring question-guided spatial attention for visual question answering. In ECCV, 2016.
[B] Zichao Yang, Xiaodong He, Jianfeng Gao, Li Deng, and Alex Smola. Stacked attention networks for image question answering. In CVPR, 2016.
[C] Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Dan Klein. Deep compositional question answering with neural module networks. In CVPR, 2016.
[D] Paul Hongsuck Seo, Zhe Lin, Scott Cohen, Xiaohui Shen, and Bohyung Han. Hierarchical attention networks. arXiv preprint arXiv:1606.02393, 2016.
[E] Quanzeng You, Hailin Jin, Zhaowen Wang, Chen Fang, and Jiebo Luo. Image captioning with semantic attention. In CVPR, 2016.
[F] Jonghwan Mun, Minsu Cho, and Bohyung Han. Text-Guided Attention Model for Image Captioning. AAAI, 2017.
[G] Seunghoon Hong, Junhyuk Oh, Honglak Lee and Bohyung Han, Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network, In CVPR, 2016.
[H] Max Jaderberg, Karen Simonyan, Andrew Zisserman, Koray Kavukcuoglu, Spatial Transformer Networks, NIPS, 2015