GroundingAnomaly uses a Spatial Conditioning Module and Gated Self-Attention in a frozen diffusion U-Net to synthesize spatially accurate few-shot anomalies, reaching SOTA on MVTec AD and VisA for detection, segmentation, and instance detection.
Advances in neural information processing systems34, 12077–12090 (2021)
3 Pith papers cite this work. Polarity classification is still indexing.
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WoundFormer modifies SegFormer with a spatially-preserving multi-scale aggregation head for multi-class wound tissue segmentation, reporting 81.9% Dice on the WoundTissueSeg dataset with gains over baselines.
DepthPolyp is a compact model using pseudo-depth multi-task learning and efficient feature modules that delivers strong generalization and real-time performance for polyp segmentation in noisy colonoscopy data.
citing papers explorer
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GroundingAnomaly: Spatially-Grounded Diffusion for Few-Shot Anomaly Synthesis
GroundingAnomaly uses a Spatial Conditioning Module and Gated Self-Attention in a frozen diffusion U-Net to synthesize spatially accurate few-shot anomalies, reaching SOTA on MVTec AD and VisA for detection, segmentation, and instance detection.
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WoundFormer: Multi-Scale Spatial Feature Fusion for Multi-Class Wound Tissue Segmentation
WoundFormer modifies SegFormer with a spatially-preserving multi-scale aggregation head for multi-class wound tissue segmentation, reporting 81.9% Dice on the WoundTissueSeg dataset with gains over baselines.
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DepthPolyp: Pseudo-Depth Guided Lightweight Segmentation for Real-Time Colonoscopy
DepthPolyp is a compact model using pseudo-depth multi-task learning and efficient feature modules that delivers strong generalization and real-time performance for polyp segmentation in noisy colonoscopy data.