Explaining Object Detectors via Collective Contribution of Pixels
This work addresses the need for more reliable object detectors in computer vision by providing enhanced explanations, though it is incremental as it builds on existing explanation methods by incorporating collective contributions.
The paper tackles the problem of generating visual explanations for object detectors by considering the collective contribution of multiple pixels, rather than individual ones, using game-theoretic concepts like Shapley values and interactions. The result is a method that more accurately identifies important regions in detection results compared to state-of-the-art methods, as demonstrated through extensive experiments.
Visual explanations for object detectors are crucial for enhancing their reliability. Since object detectors identify and localize instances by assessing multiple features collectively, generating explanations that capture these collective contributions is critical. However, existing methods focus solely on individual pixel contributions, ignoring the collective contribution of multiple pixels. To address this, we proposed a method for object detectors that considers the collective contribution of multiple pixels. Our approach leverages game-theoretic concepts, specifically Shapley values and interactions, to provide explanations. These explanations cover both bounding box generation and class determination, considering both individual and collective pixel contributions. Extensive quantitative and qualitative experiments demonstrate that the proposed method more accurately identifies important regions in detection results compared to current state-of-the-art methods. The code will be publicly available soon.