Explaining Object Detectors via Collective Contribution of Pixels
Existing interpretability methods for object detection predominantly rely on single-pixel attribution, failing to capture the joint influence of multi-pixel collaborations on both bounding box localization and class prediction—thus overlooking compositional cues or introducing spurious correlations. To address this, we introduce Shapley interaction values to object detection interpretation for the first time, proposing the first end-to-end differentiable framework that explicitly models high-order cooperative effects among pixel groups. Our approach jointly models feature-space perturbations and detection output sensitivity to simultaneously quantify individual pixel contributions and higher-order interactions. Extensive experiments on COCO and other benchmarks demonstrate that our method significantly outperforms state-of-the-art interpretability baselines. Both qualitative visualizations and quantitative metrics confirm its superior ability to localize discriminative visual regions accurately. The source code will be made publicly available.