An attention-driven hierarchical multi-scale representation for visual recognition

• URL: http://arxiv.org/abs/2110.12178v1
• Date: Sat, 23 Oct 2021 09:22:22 GMT
• Title: An attention-driven hierarchical multi-scale representation for visual recognition
• Authors: Zachary Wharton, Ardhendu Behera and Asish Bera
• Abstract summary: Convolutional Neural Networks (CNNs) have revolutionized the understanding of visual content. We propose a method to capture high-level long-range dependencies by exploring Graph Convolutional Networks (GCNs) Our approach is simple yet extremely effective in solving both the fine-grained and generic visual classification problems.
• Score: 3.3302293148249125
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Convolutional Neural Networks (CNNs) have revolutionized the understanding of visual content. This is mainly due to their ability to break down an image into smaller pieces, extract multi-scale localized features and compose them to construct highly expressive representations for decision making. However, the convolution operation is unable to capture long-range dependencies such as arbitrary relations between pixels since it operates on a fixed-size window. Therefore, it may not be suitable for discriminating subtle changes (e.g. fine-grained visual recognition). To this end, our proposed method captures the high-level long-range dependencies by exploring Graph Convolutional Networks (GCNs), which aggregate information by establishing relationships among multi-scale hierarchical regions. These regions consist of smaller (closer look) to larger (far look), and the dependency between regions is modeled by an innovative attention-driven message propagation, guided by the graph structure to emphasize the neighborhoods of a given region. Our approach is simple yet extremely effective in solving both the fine-grained and generic visual classification problems. It outperforms the state-of-the-arts with a significant margin on three and is very competitive on other two datasets.

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