CLIP language prompts guide a new weighted cross-entropy loss (CLIP-CE via AME and FAME) to boost object detection performance in hazy images, outperforming image enhancement baselines on the introduced HazyCOCO dataset.
Detrs beat yolos on real-time object detection
4 Pith papers cite this work. Polarity classification is still indexing.
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TinySet-9M dataset and DEAL point-prompted framework deliver 31.4% relative AP75 gain over supervised baselines for small object detection with one click at inference and generalization to unseen categories.
Vision Mamba-based DETR with tailored FPN and token pruning achieves a better performance-efficiency balance than RT-DETR for maritime object detection.
AMIEOD combines a multi-expert enhancement module with detection-guided regression and selection losses to raise object detection accuracy in low-illumination images.
citing papers explorer
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Language Prompt vs. Image Enhancement: Boosting Object Detection With CLIP in Hazy Environments
CLIP language prompts guide a new weighted cross-entropy loss (CLIP-CE via AME and FAME) to boost object detection performance in hazy images, outperforming image enhancement baselines on the introduced HazyCOCO dataset.
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Generalized Small Object Detection:A Point-Prompted Paradigm and Benchmark
TinySet-9M dataset and DEAL point-prompted framework deliver 31.4% relative AP75 gain over supervised baselines for small object detection with one click at inference and generalization to unseen categories.
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Increasing the Efficiency of DETR for Maritime High-Resolution Images
Vision Mamba-based DETR with tailored FPN and token pruning achieves a better performance-efficiency balance than RT-DETR for maritime object detection.
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AMIEOD: Adaptive Multi-Experts Image Enhancement for Object Detection in Low-Illumination Scenes
AMIEOD combines a multi-expert enhancement module with detection-guided regression and selection losses to raise object detection accuracy in low-illumination images.