32 citations · 33 across the 6 of their papers we have counts for
7 papers
Efficient, Validation-Free Intrinsic Quality Estimation for Large-Scale Face Recognition Datasets
Zhichao Chen, Yongle Zhao, Kaicheng Yang +3
We propose Intrinsic Quality (IQ), a validation-free metric designed to estimate the inherent potential of face recognition (FR) datasets to produce high-performance models without…
PaCo-FR: Patch-Pixel Aligned End-to-End Codebook Learning for Facial Representation Pre-training
Yin Xie, Zhichao Chen, Zeyu Xiao +7
Facial representation pre-training is crucial for tasks like facial recognition, expression analysis, and virtual reality. However, existing methods face three key challenges: (1)…
Region-based Cluster Discrimination for Visual Representation Learning
Yin Xie, Kaicheng Yang, Xiang An +9
Learning visual representations is foundational for a broad spectrum of downstream tasks. Although recent vision-language contrastive models, such as CLIP and SigLIP, have achieved…
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…
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…
Plug-and-Play Grounding of Reasoning in Multimodal Large Language Models
Jiaxing Chen, Yuxuan Liu, Dehu Li +5
The rise of Multimodal Large Language Models (MLLMs), renowned for their advanced instruction-following and reasoning capabilities, has significantly propelled the field of visual…
Partial FC: Training 10 Million Identities on a Single Machine
Xiang An, Xuhan Zhu, Yang Xiao +7
Training face recognition models with millions of identities is challenging because classifier storage, logit memory, and computation grow linearly with the number of classes, even…