STER-VLM decomposes traffic captions into spatial and temporal parts, selects a few informative frames, and adds 72B-model reference hints, yielding a small combined validation gain and a 55.655 AI City Challenge Track 2 score.
Maplm: A real-world large-scale vision-language benchmark for map and traffic scene un- derstanding
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STER-VLM: Spatio-Temporal With Enhanced Reference Vision-Language Models
STER-VLM decomposes traffic captions into spatial and temporal parts, selects a few informative frames, and adds 72B-model reference hints, yielding a small combined validation gain and a 55.655 AI City Challenge Track 2 score.