Segment to Recognize Robustly - Enhancing Recognition by Image Decomposition

2024-11-24

arXiv.org

AI Summary

To address the poor robustness of image recognition models caused by over-reliance on background cues, this paper proposes a “segment-then-recognize” decoupling paradigm. Specifically, it leverages a zero-shot segmentation model to explicitly separate foreground and background, independently modeling their features, and fusing them via a learnable weighted mechanism for joint inference. This work is the first to integrate zero-shot segmentation into the recognition pipeline, effectively suppressing background bias while preserving contextual information—thereby enhancing both robustness and interpretability. On standard benchmarks, the method achieves state-of-the-art in-distribution accuracy and demonstrates significant improvements in generalization to out-of-distribution scenarios, including natural adversarial perturbations and background domain shifts. These results validate the effectiveness and feasibility of foreground-background segmentation as a prerequisite for robust recognition.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Adversarial Learning & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Addresses over-reliance on background in object recognition.
• Proposes context-aware classification with robustness to distribution shifts.
• Improves recognition performance using localization before classification.

Innovation

• Localize FG before recognition using zero-shot detection
• Maintains robustness to distribution shifts and long-tail BGs
• Improves supervised and multimodal zero-shot recognition performance