Toward Faithful Segmentation Attribution via Benchmarking and Dual-Evidence Fusion
Attribution maps for semantic segmentation are almost always judged by visual plausibility. Yet looking convincing does not guarantee that the highlighted pixels actually drive the model's prediction, nor that attribution credit stays within the target region. These questions require a dedicated evaluation protocol. We introduce a reproducible benchmark that tests intervention-based faithfulness, off-target leakage, perturbation robustness, and runtime on Pascal VOC and SBD across three pretrained backbones. To further demonstrate the benchmark, we propose Dual-Evidence Attribution (DEA), a lightweight correction that fuses gradient evidence with region-level intervention signals through ...
| Authors | Abu Noman Md Sakib, OFM Riaz Rahman Aranya, Kevin Desai et al. |
|---|---|
| Primary category | cs.CV |
| Published | 23 March 2026 |
| Last updated at source | 23 March 2026 |
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