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
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 2years
2026 2representative citing papers
DHNet with patch alignment and dual hypergraph fusion reaches SOTA RGBT video object detection on VT-VOD50 and the new large-scale DVT-VOD1000 benchmark.
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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Dual-Correlation Hypergraph Network for Unaligned RGBT Video Object Detection and A Large-scale Benchmark
DHNet with patch alignment and dual hypergraph fusion reaches SOTA RGBT video object detection on VT-VOD50 and the new large-scale DVT-VOD1000 benchmark.