{"ID":2868470,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.16745","arxiv_id":"2509.16745","title":"CAMBench-QR : A Structure-Aware Benchmark for Post-Hoc Explanations with QR Understanding","abstract":"Visual explanations are often plausible but not structurally faithful. We introduce CAMBench-QR, a structure-aware benchmark that leverages the canonical geometry of QR codes (finder patterns, timing lines, module grid) to test whether CAM methods place saliency on requisite substructures while avoiding background. CAMBench-QR synthesizes QR/non-QR data with exact masks and controlled distortions, and reports structure-aware metrics (Finder/Timing Mass Ratios, Background Leakage, coverage AUCs, Distance-to-Structure) alongside causal occlusion, insertion/deletion faithfulness, robustness, and latency. We benchmark representative, efficient CAMs (LayerCAM, EigenGrad-CAM, XGrad-CAM) under two practical regimes of zero-shot and last-block fine-tuning. The benchmark, metrics, and training recipes provide a simple, reproducible yardstick for structure-aware evaluation of visual explanations. Hence we propose that CAMBENCH-QR can be used as a litmus test of whether visual explanations are truly structure-aware.","short_abstract":"Visual explanations are often plausible but not structurally faithful. We introduce CAMBench-QR, a structure-aware benchmark that leverages the canonical geometry of QR codes (finder patterns, timing lines, module grid) to test whether CAM methods place saliency on requisite substructures while avoiding background. CAM...","url_abs":"https://arxiv.org/abs/2509.16745","url_pdf":"https://arxiv.org/pdf/2509.16745v1","authors":"[\"Ritabrata Chakraborty\",\"Avijit Dasgupta\",\"Sandeep Chaurasia\"]","published":"2025-09-20T17:13:38Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[]","has_code":false}