A dual-hypothesis segmentation architecture with prosecution/defense streams and an RL judge model achieves superior performance in localizing image manipulations by explicitly contrasting evidence.
Boundary-guided camouflaged object detection
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
years
2026 6verdicts
UNVERDICTED 6representative citing papers
CamoNAS applies neural architecture search with an RGB-frequency dual-stream design to reach state-of-the-art results on four camouflaged object detection benchmarks.
Proposes HCL framework with HRR, TAG, and PCC modules for test-time adaptation in camouflaged object detection, claiming consistent outperformance on benchmarks under distribution shifts.
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.
GLASSNet outperforms prior methods on salient object detection benchmarks by freezing SAMv2, adding a spatially aware adapter, and fusing outputs from global and local decoders.
citing papers explorer
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The Courtroom Trial of Pixels: Robust Image Manipulation Localization via Adversarial Evidence and Reinforcement Learning Judgment
A dual-hypothesis segmentation architecture with prosecution/defense streams and an RL judge model achieves superior performance in localizing image manipulations by explicitly contrasting evidence.
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CamoNAS: Neural Architecture Search for Enhanced Camouflaged Object Detection
CamoNAS applies neural architecture search with an RGB-frequency dual-stream design to reach state-of-the-art results on four camouflaged object detection benchmarks.
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Hierarchical Consistency Learning for Test-time Adaptation in Camouflage Perception
Proposes HCL framework with HRR, TAG, and PCC modules for test-time adaptation in camouflaged object detection, claiming consistent outperformance on benchmarks under distribution shifts.
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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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Global-Local Feature Decoding with Adapter-Guided SAMv2 for Salient Object Detection
GLASSNet outperforms prior methods on salient object detection benchmarks by freezing SAMv2, adding a spatially aware adapter, and fusing outputs from global and local decoders.