92 citations · 164 across the 11 of their papers we have counts for
7 papers · 1 filter
FaceChain-FACT: Face Adapter with Decoupled Training for Identity-preserved Personalization
Cheng Yu, Haoyu Xie, Lei Shang +4
In the field of human-centric personalized image generation, the adapter-based method obtains the ability to customize and generate portraits by text-to-image training on facial da…
FaceChain-SuDe: Building Derived Class to Inherit Category Attributes for One-shot Subject-Driven Generation
Pengchong Qiao, Lei Shang, Chang Liu +3
Subject-driven generation has garnered significant interest recently due to its ability to personalize text-to-image generation. Typical works focus on learning the new subject's p…
FaceChain: A Playground for Human-centric Artificial Intelligence Generated Content
Yang Liu, Cheng Yu, Lei Shang +17
Recent advancement in personalized image generation have unveiled the intriguing capability of pre-trained text-to-image models on learning identity information from a collection o…
Improving Training and Inference of Face Recognition Models via Random Temperature Scaling
Lei Shang, Mouxiao Huang, Wu Shi +6
Data uncertainty is commonly observed in the images for face recognition (FR). However, deep learning algorithms often make predictions with high confidence even for uncertain or i…
Boosting Novel Category Discovery Over Domains with Soft Contrastive Learning and All-in-One Classifier
Zelin Zang, Lei Shang, Senqiao Yang +4
Unsupervised domain adaptation (UDA) has proven to be highly effective in transferring knowledge from a label-rich source domain to a label-scarce target domain. However, the prese…
Architecture-Agnostic Masked Image Modeling -- From ViT back to CNN
Siyuan Li, Di Wu, Fang Wu +2
Masked image modeling, an emerging self-supervised pre-training method, has shown impressive success across numerous downstream vision tasks with Vision transformers. Its underlyin…
SoftTriple Loss: Deep Metric Learning Without Triplet Sampling
Qi Qian, Lei Shang, Baigui Sun +3
Distance metric learning (DML) is to learn the embeddings where examples from the same class are closer than examples from different classes. It can be cast as an optimization prob…