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arXiv · 2509.16745

CAMBench-QR : A Structure-Aware Benchmark for Post-Hoc Explanations with QR Understanding

Ritabrata Chakraborty·Avijit Dasgupta·Sandeep Chaurasia
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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.

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Ritabrata Chakraborty, Avijit Dasgupta, Sandeep Chaurasia. 2025-09-20. CAMBench-QR : A Structure-Aware Benchmark for Post-Hoc Explanations with QR Understanding. https://arxiv.org/abs/2509.16745

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