CAMBench-QR : A Structure-Aware Benchmark for Post-Hoc Explanations with QR Understanding
This provides a reproducible yardstick for evaluating structure-aware visual explanations, which is incremental as it builds on existing CAM methods with a new benchmark.
The paper tackles the problem of visual explanations lacking structural faithfulness by introducing CAMBench-QR, a benchmark that uses QR codes to test if CAM methods accurately highlight substructures and avoid background, reporting metrics like Finder/Timing Mass Ratios and Background Leakage.
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.