Existing visual attribution methods often fail to identify the visual evidence used by LVLMs in chest X-ray reasoning, while MedFocus using unbalanced optimal transport and targeted interventions substantially outperforms them across multiple models and settings.
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FAMPE is a new attribution method that applies FFT-based frequency-selective perturbations integrated with model parameter exploration to produce fine-grained feature importance maps, showing gains over AttEXplore on ImageNet.
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Rethinking Visual Attribution for Chest X-ray Reasoning in Large Vision Language Models
Existing visual attribution methods often fail to identify the visual evidence used by LVLMs in chest X-ray reasoning, while MedFocus using unbalanced optimal transport and targeted interventions substantially outperforms them across multiple models and settings.
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Frequency-Aware Model Parameter Explorer: A new attribution method for improving explainability
FAMPE is a new attribution method that applies FFT-based frequency-selective perturbations integrated with model parameter exploration to produce fine-grained feature importance maps, showing gains over AttEXplore on ImageNet.