Sim-to-Real Traffic Scene Understanding by Decoupling Semantics from Caption Generation with V-JEPA
Track 2 of the AI City Challenge 2026 requires both visual question answering (VQA) and traffic event description generation under a challenging synthetic-to real domain shift. Existing vision-language approaches often entangle semantic understanding with language generation, making them susceptible to hallucination and inconsistent reasoning across event phases. In this work, we propose a decoupled semantic understanding framework that first resolves predefined traffic questions into structured semantic facts and subsequently uses these facts to guide caption generation. A frozen V-JEPA encoder extracts predictive scene representations, while a lightweight Llama-based predictor produces ...
| Authors | Nguyen Hoai Thuong Bui, Thanh Nguyen Vo, Trinh Tra Giang Nguyen et al. |
|---|---|
| Primary category | cs.CV |
| Published | 16 September 2026 |
| Last updated at source | 16 September 2026 |
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