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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.
