Progressive Co-Attention Network for Fine-grained Visual Classification

• URL: http://arxiv.org/abs/2101.08527v1
• Date: Thu, 21 Jan 2021 10:19:02 GMT
• Title: Progressive Co-Attention Network for Fine-grained Visual Classification
• Authors: Tian Zhang, Dongliang Chang, Zhanyu Ma and Jun Guo
• Abstract summary: Fine-grained visual classification aims to recognize images belonging to multiple sub-categories within a same category. Most existing methods only take individual image as input. We propose an effective method called progressive co-attention network (PCA-Net) to tackle this problem.
• Score: 20.838908090777885
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fine-grained visual classification aims to recognize images belonging to multiple sub-categories within a same category. It is a challenging task due to the inherently subtle variations among highly-confused categories. Most existing methods only take individual image as input, which may limit the ability of models to recognize contrastive clues from different images. In this paper, we propose an effective method called progressive co-attention network (PCA-Net) to tackle this problem. Specifically, we calculate the channel-wise similarity by interacting the feature channels within same-category images to capture the common discriminative features. Considering that complementary imformation is also crucial for recognition, we erase the prominent areas enhanced by the channel interaction to force the network to focus on other discriminative regions. The proposed model can be trained in an end-to-end manner, and only requires image-level label supervision. It has achieved competitive results on three fine-grained visual classification benchmark datasets: CUB-200-2011, Stanford Cars, and FGVC Aircraft.

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