PIEDet: Prototype-Driven Intrinsically Explainable Object Detection
Existing object detectors typically make predictions in a black-box manner and struggle to simultaneously provide discriminative evidence for their predictions, which limits their deployment in safety-critical scenarios. To explain model predictions, existing post-hoc explanation methods mostly rely on gradient-based or perturbation-based operators. These methods not only introduce additional memory and computational overhead but also make it difficult to ensure that the generated explanations faithfully reflect the model's internal decision-making process. To address these limitations, we propose PIEDet, a prototype-driven intrinsically explainable object detection framework. PIEDet inno...
| Authors | Jianlin Xiang, Linhui Dai, Xue Yang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 15 April 2026 |
| Last updated at source | 15 August 2026 |
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