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Yazhou Yao

An Empirical Study on Training Paradigms for Deep Supervised Hashing
CA2C: A Prior-Knowledge-Free Approach for Robust Label Noise Learning via Asymmetric Co-learning and Co-training
Cycle-Consistent Learning for Joint Layout-to-Image Generation and Object Detection
Exploiting Frequency Dynamics for Enhanced Multimodal Event-based Action Recognition
Jo-SNC: Combating Noisy Labels Through Fostering Self- and Neighbor-Consistency
Seeing What Matters: Empowering CLIP with Patch Generation-to-Selection
Tensor-aggregated LoRA in Federated Fine-tuning
UNIALIGN: Scaling Multimodal Alignment within One Unified Model

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