Segment to Recognize Robustly -- Enhancing Recognition by Image Decomposition

• URL: http://arxiv.org/abs/2411.15933v1
• Date: Sun, 24 Nov 2024 17:39:39 GMT
• Title: Segment to Recognize Robustly -- Enhancing Recognition by Image Decomposition
• Authors: Klara Janouskova, Cristian Gavrus, Jiri Matas,
• Abstract summary: "Segment to Recognize Robustly" (S2R2) is a novel recognition approach which decouples the FG and BG modelling and combines them in a simple, robust, and interpretable manner. S2R2 achieves state-of-the-art results on in-domain data while maintaining robustness to BG shifts.
• Score: 21.917582794820095
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In image recognition, both foreground (FG) and background (BG) play an important role; however, standard deep image recognition often leads to unintended over-reliance on the BG, limiting model robustness in real-world deployment settings. Current solutions mainly suppress the BG, sacrificing BG information for improved generalization. We propose "Segment to Recognize Robustly" (S2R^2), a novel recognition approach which decouples the FG and BG modelling and combines them in a simple, robust, and interpretable manner. S2R^2 leverages recent advances in zero-shot segmentation to isolate the FG and the BG before or during recognition. By combining FG and BG, potentially also with a standard full-image classifier, S2R^2 achieves state-of-the-art results on in-domain data while maintaining robustness to BG shifts. The results confirm that segmentation before recognition is now possible.

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