XWOD is a large-scale real-world benchmark for traffic object detection under seven extreme weather conditions that improves zero-shot generalization to other weather datasets.
End-to-end object detection with transformers
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Introduces the first dedicated benchmark for live multi-modal LLM task guidance with mistake detection and a streaming baseline model.
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XWOD: A Real-World Benchmark for Object Detection under Extreme Weather Conditions
XWOD is a large-scale real-world benchmark for traffic object detection under seven extreme weather conditions that improves zero-shot generalization to other weather datasets.
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Can Multi-Modal LLMs Provide Live Step-by-Step Task Guidance?
Introduces the first dedicated benchmark for live multi-modal LLM task guidance with mistake detection and a streaming baseline model.