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Boundary-Guided Camouflaged Object Detection
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Boundary-Guided Camouflaged Object Detection
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Camouflaged object detection (COD), segmenting objects that are elegantly blended into their surroundings, is a valuable yet challenging task. Existing deep-learning methods often fall into the difficulty of accurately identifying the camouflaged object with complete and fine object structure. To this end, in this paper, we propose a novel boundary-guided network (BGNet) for camouflaged object detection. Our method explores valuable and extra object-related edge semantics to guide representation learning of COD, which forces the model to generate features that highlight object structure, thereby promoting camouflaged object detection of accurate boundary localization. Extensive experiments on three challenging benchmark datasets demonstrate that our BGNet significantly outperforms the existing 18 state-of-the-art methods under four widely-used evaluation metrics. Our code is publicly available at: https://github.com/thograce/BGNet.
Forward citations
Cited by 7 Pith papers
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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...
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MSRNet: A Multi-Scale Recursive Network for Camouflaged Object Detection
MSRNet, a multi-scale recursive network with attention-based scale integration and recursive-feedback decoding, reports state-of-the-art or runner-up camouflaged object detection on four standard benchmarks.
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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.
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