An iERF-centric framework unifies local, global, and mechanistic interpretability in vision models via SRD for saliency, CAFE for concept anchoring, and ICAT for interlayer attribution.
Understanding the effective receptive field in deep convolutional neural networks,
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TopoMamba improves medical image segmentation by combining topology-aware diagonal scans with standard cross-scans and a HSIC Gate for efficient fusion, yielding gains on thin and curved targets like the pancreas.
SALD decouples remote sensing images into compressed payload plus structural prior at the edge and uses structure-gated diffusion on the cloud to improve super-resolution and downstream detection under extreme bandwidth limits.
citing papers explorer
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From Local to Global to Mechanistic: An iERF-Centered Unified Framework for Interpreting Vision Models
An iERF-centric framework unifies local, global, and mechanistic interpretability in vision models via SRD for saliency, CAFE for concept anchoring, and ICAT for interlayer attribution.
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TopoMamba: Topology-Aware Scanning and Fusion for Segmenting Heterogeneous Medical Visual Media
TopoMamba improves medical image segmentation by combining topology-aware diagonal scans with standard cross-scans and a HSIC Gate for efficient fusion, yielding gains on thin and curved targets like the pancreas.
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Edge-Cloud Collaborative Reconstruction via Structure-Aware Latent Diffusion for Downstream Remote Sensing Perception
SALD decouples remote sensing images into compressed payload plus structural prior at the edge and uses structure-gated diffusion on the cloud to improve super-resolution and downstream detection under extreme bandwidth limits.