WD-FQDet decouples modality-shared and modality-specific features in infrared-visible images via wavelet-based frequency decomposition and frequency-aware query selection to achieve state-of-the-art detection performance.
Microsoft coco: Common objects in context
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InstAP introduces instance-aware pre-training with a new dual-granularity dataset InstVL that improves both fine-grained instance retrieval and global video understanding over standard VLP baselines.
DC-TTA improves interactive segmentation accuracy by partitioning user clicks into subsets for independent test-time adaptation of SAM models and merging the specialized predictors.
A quantization technique for diffusion models that aligns sampling trajectories to preserve high-order sampler performance under quantization noise.
citing papers explorer
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WD-FQDet: Multispectral Detection Transformer via Wavelet Decomposition and Frequency-aware Query Learning
WD-FQDet decouples modality-shared and modality-specific features in infrared-visible images via wavelet-based frequency decomposition and frequency-aware query selection to achieve state-of-the-art detection performance.
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InstAP: Instance-Aware Vision-Language Pre-Train for Spatial-Temporal Understanding
InstAP introduces instance-aware pre-training with a new dual-granularity dataset InstVL that improves both fine-grained instance retrieval and global video understanding over standard VLP baselines.
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DC-TTA: Divide-and-Conquer Framework for Test-Time Adaptation of Interactive Segmentation
DC-TTA improves interactive segmentation accuracy by partitioning user clicks into subsets for independent test-time adaptation of SAM models and merging the specialized predictors.
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Sampling-Aware Quantization for Diffusion Models
A quantization technique for diffusion models that aligns sampling trajectories to preserve high-order sampler performance under quantization noise.