5 citations · 9 across the 15 of their papers we have counts for
7 papers · 1 filter
ViCToR: Improving Visual Comprehension via Token Reconstruction for Pretraining LMMs
Yin Xie, Kaicheng Yang, Peirou Liang +7
Large Multimodal Models (LMMs) often face a modality representation gap during pretraining: while language embeddings remain stable, visual representations are highly sensitive to…
CLIP-CID: Efficient CLIP Distillation via Cluster-Instance Discrimination
Kaicheng Yang, Tiancheng Gu, Xiang An +5
Contrastive Language-Image Pre-training (CLIP) has achieved excellent performance over a wide range of tasks. However, the effectiveness of CLIP heavily relies on a substantial cor…
Multi-label Cluster Discrimination for Visual Representation Learning
Xiang An, Kaicheng Yang, Xiangzi Dai +2
Contrastive Language Image Pre-training (CLIP) has recently demonstrated success across various tasks due to superior feature representation empowered by image-text contrastive lea…
High-Fidelity Facial Albedo Estimation via Texture Quantization
Zimin Ran, Xingyu Ren, Xiang An +6
Recent 3D face reconstruction methods have made significant progress in shape estimation, but high-fidelity facial albedo reconstruction remains challenging. Existing methods depen…
RWKV-CLIP: A Robust Vision-Language Representation Learner
Tiancheng Gu, Kaicheng Yang, Xiang An +4
Contrastive Language-Image Pre-training (CLIP) has significantly improved performance in various vision-language tasks by expanding the dataset with image-text pairs obtained from…
Monocular Identity-Conditioned Facial Reflectance Reconstruction
Xingyu Ren, Jiankang Deng, Yuhao Cheng +5
Recent 3D face reconstruction methods have made remarkable advancements, yet there remain huge challenges in monocular high-quality facial reflectance reconstruction. Existing meth…
IDAdapter: Learning Mixed Features for Tuning-Free Personalization of Text-to-Image Models
Siying Cui, Jia Guo, Xiang An +4
Leveraging Stable Diffusion for the generation of personalized portraits has emerged as a powerful and noteworthy tool, enabling users to create high-fidelity, custom character ava…