29 citations · 32 across the 20 of their papers we have counts for
5 papers · 1 filter
Category-Adaptive Cross-Modal Semantic Refinement and Transfer for Open-Vocabulary Multi-Label Recognition
Haijing Liu, Tao Pu, Hefeng Wu +4
Benefiting from the generalization capability of CLIP, recent vision language pre-training (VLP) models have demonstrated the ability to capture a wide range of visual concepts in…
Human-VDM: Learning Single-Image 3D Human Gaussian Splatting from Video Diffusion Models
Zhibin Liu, Haoye Dong, Aviral Chharia +1
Generating lifelike 3D humans from a single RGB image remains a challenging task in computer vision, as it requires accurate modeling of geometry, high-quality texture, and plausib…
Improving Network Interpretability via Explanation Consistency Evaluation
Hefeng Wu, Hao Jiang, Keze Wang +3
While deep neural networks have achieved remarkable performance, they tend to lack transparency in prediction. The pursuit of greater interpretability in neural networks often resu…
ID-Aligner: Enhancing Identity-Preserving Text-to-Image Generation with Reward Feedback Learning
Weifeng Chen, Jiacheng Zhang, Jie Wu +3
The rapid development of diffusion models has triggered diverse applications. Identity-preserving text-to-image generation (ID-T2I) particularly has received significant attention…
Dual-View Data Hallucination with Semantic Relation Guidance for Few-Shot Image Recognition
Hefeng Wu, Guangzhi Ye, Ziyang Zhou +3
Learning to recognize novel concepts from just a few image samples is very challenging as the learned model is easily overfitted on the few data and results in poor generalizabilit…