FOCI adds a post-hoc readout to frozen WSI-MIL models to find compact output-consistent tile subsets and measures selection headroom with SHI, showing transformer-based models allow smaller rationales than attention-pooling baselines.
Grad-cam: Visual explanations from deep networks via gradient-based localization
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SEF introduces GAN upsampling for diverse artifacts and expert fusion to reduce domain interference, yielding stronger generalization on 13 benchmarks for AI-generated image detection.
Pretrained instruction-based image editing models exhibit early foreground-background separability that enables a training-free framework for zero-shot referring image segmentation using a single denoising step.
Uncertainty-aware extensions to Variational Information Pursuit (EUAV-IP and IUAV-IP) improve reliability and conciseness of concept-based predictions on five medical imaging datasets.
Benchmark study of ten GNN explainers on eight architectures and six datasets that isolates usable components and issues practical recommendations.
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Are Compact Rationales Free? Measuring Tile Selection Headroom in Frozen WSI-MIL
FOCI adds a post-hoc readout to frozen WSI-MIL models to find compact output-consistent tile subsets and measures selection headroom with SHI, showing transformer-based models allow smaller rationales than attention-pooling baselines.
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Reduce the Artifacts Bias for More Generalizable AI-Generated Image Detection
SEF introduces GAN upsampling for diverse artifacts and expert fusion to reduce domain interference, yielding stronger generalization on 13 benchmarks for AI-generated image detection.
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Early Semantic Grounding in Image Editing Models for Zero-Shot Referring Image Segmentation
Pretrained instruction-based image editing models exhibit early foreground-background separability that enables a training-free framework for zero-shot referring image segmentation using a single denoising step.
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Uncertainty-Aware Information Pursuit for Interpretable and Reliable Medical Image Analysis
Uncertainty-aware extensions to Variational Information Pursuit (EUAV-IP and IUAV-IP) improve reliability and conciseness of concept-based predictions on five medical imaging datasets.
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Explaining the Explainers in Graph Neural Networks: a Comparative Study
Benchmark study of ten GNN explainers on eight architectures and six datasets that isolates usable components and issues practical recommendations.