UniV2D is a dual-branch network that lets high-level saliency masks guide low-level image restoration and lets restored features improve saliency detection, outperforming prior separate-stage methods on underwater benchmarks.
Enhanced-alignment measure for binary foreground map evaluation
8 Pith papers cite this work. Polarity classification is still indexing.
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Camo-M3FD is a curated visible-thermal benchmark dataset for cross-spectral camouflaged pedestrian detection, with annotations and baseline evaluations showing the value of fusion.
MHENet enhances RGB texture and depth geometry features hierarchically with dedicated modules before adaptive fusion, outperforming prior RGB-D COD methods on four benchmarks.
HVPNet introduces a Retinal Integration Module and cortical decoder to achieve strong accuracy-efficiency trade-offs on 22 datasets for seven salient and camouflaged object detection tasks across four modalities.
BASFNet fuses boundary-aware frequency-domain edge exploration with spatial core segmentation and interaction modules to outperform prior methods on camouflaged object detection benchmarks.
EviRCOD integrates reference-guided deformable encoding, uncertainty-aware evidential decoding, and boundary refinement to achieve state-of-the-art performance on referring camouflaged object detection benchmarks with calibrated uncertainty.
DifferSeg introduces learnable differential operators for modality fusion and cross-frequency decoder interactions, claiming superior performance over 67 prior methods on 29 datasets across 18 tasks.
CATP prunes low-confidence tokens in COD Transformers and uses dual-path compensation to cut computation while preserving segmentation accuracy on boundary regions.
citing papers explorer
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UniV2D: Bridging Visual Restoration and Semantic Perception for Underwater Salient Object Detection
UniV2D is a dual-branch network that lets high-level saliency masks guide low-level image restoration and lets restored features improve saliency detection, outperforming prior separate-stage methods on underwater benchmarks.
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Camo-M3FD: A New Benchmark Dataset for Cross-Spectral Camouflaged Pedestrian Detection
Camo-M3FD is a curated visible-thermal benchmark dataset for cross-spectral camouflaged pedestrian detection, with annotations and baseline evaluations showing the value of fusion.
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HVPNet: A Bio-Inspired Network for General Salient and Camouflaged Object Detection
HVPNet introduces a Retinal Integration Module and cortical decoder to achieve strong accuracy-efficiency trade-offs on 22 datasets for seven salient and camouflaged object detection tasks across four modalities.
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Exploring Boundary-Aware Spatial-Frequency Fusion for Camouflaged Object Detection
BASFNet fuses boundary-aware frequency-domain edge exploration with spatial core segmentation and interaction modules to outperform prior methods on camouflaged object detection benchmarks.
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EviRCOD: Evidence-Guided Probabilistic Decoding for Referring Camouflaged Object Detection
EviRCOD integrates reference-guided deformable encoding, uncertainty-aware evidential decoding, and boundary refinement to achieve state-of-the-art performance on referring camouflaged object detection benchmarks with calibrated uncertainty.
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DifferSeg: Towards Diverse Multimodal Binary Segmentation via Differential Perception and Frequency Guidance
DifferSeg introduces learnable differential operators for modality fusion and cross-frequency decoder interactions, claiming superior performance over 67 prior methods on 29 datasets across 18 tasks.
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CATP: Confidence-Aware Token Pruning for Camouflaged Object Detection
CATP prunes low-confidence tokens in COD Transformers and uses dual-path compensation to cut computation while preserving segmentation accuracy on boundary regions.