Jun Yin

Time-aware diffusion in generative recommendation can boost performance by over 29%, revealing the critical role of temporal dynamics in user preferences.

Shared codebooks and fused-teacher self-distillation offer a surprisingly effective approach to multi-view multi-label classification when data is incomplete.

Achieve better video editing without retraining by dynamically locking background features based on a "hallucination metric" that detects when the diffusion model is about to go astray.