Weakly-supervised attention mechanism via score-CAM for fine-grained visual classification

Yizhou He; ErBo Zou; Qiang Fan

doi:10.1117/12.2644399

12 October 2022 Weakly-supervised attention mechanism via score-CAM for fine-grained visual classification

Yizhou He, ErBo Zou, Qiang Fan

Proceedings Volume 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022); 123421V (2022) https://doi.org/10.1117/12.2644399
Event: Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 2022, Wuhan, China

Abstract

Along with the prosperity and development of computer vision technologies, fine-grained visual classification (FGVC) has now become an intriguing research field due to its broad application prospects. The major challenges of fine-grained classification are mainly two-fold: localization of discriminative region and extraction of fine-grained features. The attention mechanism is a common choice for current state-of-art (SOTA) methods in the FGVC that can significantly improve the performance of distinguishing among fine-grained categories. The attention module in different designs is utilized to capture the discriminative region, and region-based feature representation encodes subtle inter-class differences. However, the attention mechanism without proper supervision may not learn to provide informative guidance to the discriminative region, thus could be meaningless in the FGVC tasks that lack part annotations. We propose a weakly-supervised attention mechanism that integrates visual explanation methods to address confusing issues in the discriminative region localization caused by the absence of supervision and avoid labor-intensive bounding box/part annotations in the meanwhile. We employ Score-CAM, a novel post-hoc visual explanation method based on class activation mapping, to provide supervision and constrain the attention module. We conduct extensive experiments and show that the proposed method outperforms the current SOTA methods in three fine-grained classification tasks on CUB Birds, FGVC Aircraft, and Stanford Cars.

Citation Download Citation

Yizhou He, ErBo Zou, and Qiang Fan "Weakly-supervised attention mechanism via score-CAM for fine-grained visual classification", Proc. SPIE 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 123421V (12 October 2022); https://doi.org/10.1117/12.2644399

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KEYWORDS

Visualization

Feature extraction

Image processing

Convolutional neural networks

Distance measurement

Performance modeling

Visual process modeling

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