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ARXIV:2605.10142 · COMPUTER VISION EXPLAINABILITY · SUBMITTED 12 MAY · 20:16 UTC · FRESHNESS FRESH
ARXIV:2605.10142COMPUTER VISION EXPLAINABILITYSUBMITTED 12 MAY · 20:16 UTCFRESHNESS FRESHMateusz Cedro · Marcin Chlebus · arXiv
Investigating the impact of scaling computer vision models on the quality of localization-based explanations, finding that larger models do not consistently improve explanation accuracy.
Opportunity summary
Pain Investigating the impact of scaling computer vision models on the quality of localization-based explanations, finding that larger models do not consistently improve explanation accuracy.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
Investigating the impact of scaling computer vision models on the quality of localization-based explanations, finding that larger models do not consistently improve explanation accuracy. We investigate this relationship by evaluating 11 computer vision models…
Artificial intelligence models are increasingly scaled to improve predictive accuracy, yet it remains unclear whether scale improves the quality of post-hoc explanations. We investigate this relationship by evaluating 11 computer vision models representing increasing…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Artificial intelligence models are increasingly scaled to improve predictive accuracy, yet it remains unclear whether scale improves the quality of post-hoc explanations. Code availability…
Computer Vision Explainability moved forward this cycle; last verified May 2026. Public score 4.0/10. Production flags indicate code availability.
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Investigating the impact of scaling computer vision models on the quality of localization-based explanations, finding that larger models do not consistently improve explanation accuracy.
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10.48550/arXiv.2605.10142Investigating the impact of scaling computer vision models on the quality of localization-based explanations, finding that larger models do not consistently improve explanation accuracy.
Abstract
Artificial intelligence models are increasingly scaled to improve predictive accuracy, yet it remains unclear whether scale improves the quality of post-hoc explanations. We investigate this relationship by evaluating 11 computer vision models representing increasing levels of depth and complexity within the ResNet, DenseNet, and Vision Transformer families, trained from scratch or pretrained, across three image datasets with ground-truth segmentation masks. For each model, we generate explanations using five post-hoc explainable AI methods and quantify mask alignment using two localisation metrics: Relevance Rank Accuracy (Arras et al., 2022) and the proposed Dual-Polarity Precision, which measures positive attributions inside the class mask and negative attributions outside it. Across datasets and methods, increasing architectural depth and parameter count does not improve explanation quality in most statistical comparisons, and smaller models often match or exceed deeper variants. While pretraining typically improves predictive performance and increases the dependence of explanations on learned weights, it does not consistently increase localisation scores. We also observe scenarios in which models achieve strong predictive performance while localisation precision is near zero, suggesting that performance metrics alone may not indicate whether predictions are based on the annotated regions. These results indicate that larger models do not reliably provide higher-quality explanations, and that explainability should therefore be assessed explicitly during model selection for safety-sensitive deployments.
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PROBLEM
Investigating the impact of scaling computer vision models on the quality of localization-based explanations, finding that larger models do not consistently improve explanation accuracy. We investigate this relationship by evaluating 11 computer vision models representing increa...
METHOD
Artificial intelligence models are increasingly scaled to improve predictive accuracy, yet it remains unclear whether scale improves the quality of post-hoc explanations. We investigate this relationship by evaluating 11 computer vision models representing increasing levels of d...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Artificial intelligence models are increasingly scaled to improve predictive accuracy, yet it remains unclear whether scale improves the quality of post-hoc explanations. Code availability is flagged in t...
WHY NOW
Computer Vision Explainability moved forward this cycle; last verified May 2026. Public score 4.0/10. Production flags indicate code availability.
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Investigating the impact of scaling computer vision models on the quality of localization-based explanations, finding that larger models do not consistently improve explanation accuracy. We investigate this relationship by evaluating 11 computer vision models representing increasing levels of depth and complexity within the ResNet, DenseNet, and Vision Transformer families, trained from scratch or pretrained, across three image datasets with ground-truth segmentation masks.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Artificial intelligence models are increasingly scaled to improve predictive accuracy, yet it remains unclear whether scale improves the quality of post-hoc explanations. We investigate this relationship by evaluating 11 computer vision models representing increasing levels of depth and complexity within the ResNet, DenseNet, and Vision Transformer families, trained from scratch or pretrained, across three image datasets with ground-truth segmentation masks.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Artificial intelligence models are increasingly scaled to improve predictive accuracy, yet it remains unclear whether scale improves the quality of post-hoc explanations. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Computer Vision Explainability moved forward this cycle; last verified May 2026. Public score 4.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Investigating the impact of scaling computer vision models on the quality of localization-based explanations, finding that larger models do not consistently improve explanation accuracy.
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Computer Vision Explainability
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