Yu Yin

Presents an efficient latent predictor for real-time online trajectory prediction in autonomous vehicles, achieving high accuracy with reduced computational overhead.

With the rapid progress of generative models, synthetic data has become a common solution to data scarcity in AI. However, is using it directly without curation ideal for visual recognition? We systematically study how data fidelity and diversity affect recognition performance and show that balancing these factors significantly improves results through a training-free curation pipeline.

Vision-Language Models (VLMs) have achieved great success but still lack spatial intelligence, and this survey provides the first unified overview of recent advances, taxonomies, and evaluations toward building spatially intelligent AI.

True intelligence relies on understanding hidden causal relations, yet current AI and vision models lack benchmarks to assess this ability. We introduce Causal3D, a comprehensive 19-dataset benchmark linking structured and visual data to evaluate causal reasoning, revealing that performance drops sharply as causal complexity increases.