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.
Spi- der: A unified framework for context-dependent concept segmentation,
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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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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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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.