The new VRU-Accident benchmark (1K videos, 6K QA pairs, 1K dense captions) shows the best evaluated MLLM reaches 66.9% on VRU-accident VQA versus 94.7% for human experts, with the weakest performance on causal and preventive reasoning.
Video-to-text pedestrian monitoring (vtpm): Leveraging large language models for privacy-preserve pedestrian activity monitoring at intersections
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VRU-Accident: A Vision-Language Benchmark for Video Question Answering and Dense Captioning for Accident Scene Understanding
The new VRU-Accident benchmark (1K videos, 6K QA pairs, 1K dense captions) shows the best evaluated MLLM reaches 66.9% on VRU-accident VQA versus 94.7% for human experts, with the weakest performance on causal and preventive reasoning.