Author

Sparsh Garg

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#machine learning Jun 2026

Driving Video Retrieval for Complex Queries with Structured Grounding

STRIVE-D, a data-calibrated retrieval framework for driving videos, uses weakly labeled in-domain videos to estimate when a query rule is reliable, adapt rules that mismatch observed data, and fuse calibrated rule scores with vision-language and keyword-based retrieval signals.

Man-Yi Yao, Sparsh Garg, Christian R. Shelton et al. · arXiv.org · 1 citation

Preprint Aug 2026

CAViAR: A Causal Video Dataset for Fine-Grained Accident Reasoning in Real-World Scenarios

CAViAR exposes a practical Perception--Reasoning Gap: current VLMs may recognize salient context, but do not reliably map visible agent actions to annotated rule-relevant responsibility categories in safety-critical driving scenarios, and all models degrade sharply on accident type and responsibility reasoning.

Sparsh Garg, Yi-Wen Chen, Vijay Kumar et al. · 0 citations