50 citations · 203 across the 22 of their papers we have counts for
22 papers
GeoMAR: Unleashing Geometrically Aligned Features for Masked Autoregressive Blind Face Restoration
Lu Gan, Hanyu Yan, Chaofeng Chen +2
Codebook-based blind face restoration (BFR) often suffers from ambiguous conditioning features and a fragile prediction mechanism under severe degradation. To address these challen…
Low-Resolution Object Recognition with Cross-Resolution Relational Contrastive Distillation
Kangkai Zhang, Shiming Ge, Ruixin Shi +1
Recognizing objects in low-resolution images is a challenging task due to the lack of informative details. Recent studies have shown that knowledge distillation approaches can effe…
Model Conversion via Differentially Private Data-Free Distillation
Bochao Liu, Pengju Wang, Shikun Li +2
While massive valuable deep models trained on large-scale data have been released to facilitate the artificial intelligence community, they may encounter attacks in deployment whic…
Scale Attention for Learning Deep Face Representation: A Study Against Visual Scale Variation
Hailin Shi, Hang Du, Yibo Hu +3
Human face images usually appear with wide range of visual scales. The existing face representations pursue the bandwidth of handling scale variation via multi-scale scheme that as…
SuperVessel: Segmenting High-resolution Vessel from Low-resolution Retinal Image
Yan Hu, Zhongxi Qiu, Dan Zeng +3
Vascular segmentation extracts blood vessels from images and serves as the basis for diagnosing various diseases, like ophthalmic diseases. Ophthalmologists often require high-reso…
Deepfake Video Detection with Spatiotemporal Dropout Transformer
Daichi Zhang, Fanzhao Lin, Yingying Hua +3
While the abuse of deepfake technology has caused serious concerns recently, how to detect deepfake videos is still a challenge due to the high photo-realistic synthesis of each fr…
Bootstrapping Multi-view Representations for Fake News Detection
Qichao Ying, Xiaoxiao Hu, Yangming Zhou +3
Previous researches on multimedia fake news detection include a series of complex feature extraction and fusion networks to gather useful information from the news. However, how cr…
Trustable Co-label Learning from Multiple Noisy Annotators
Shikun Li, Tongliang Liu, Jiyong Tan +2
Supervised deep learning depends on massive accurately annotated examples, which is usually impractical in many real-world scenarios. A typical alternative is learning from multipl…
Dual Spoof Disentanglement Generation for Face Anti-spoofing with Depth Uncertainty Learning
Hangtong Wu, Dan Zen, Yibo Hu +2
Face anti-spoofing (FAS) plays a vital role in preventing face recognition systems from presentation attacks. Existing face anti-spoofing datasets lack diversity due to the insuffi…
Combating Ambiguity for Hash-code Learning in Medical Instance Retrieval
Jiansheng Fang, Huazhu Fu, Dan Zeng +3
When encountering a dubious diagnostic case, medical instance retrieval can help radiologists make evidence-based diagnoses by finding images containing instances similar to a quer…
Multi-Agent Semi-Siamese Training for Long-tail and Shallow Face Learning
Hailin Shi, Dan Zeng, Yichun Tai +4
With the recent development of deep convolutional neural networks and large-scale datasets, deep face recognition has made remarkable progress and been widely used in various appli…
Towards NIR-VIS Masked Face Recognition
Hang Du, Hailin Shi, Yinglu Liu +2
Near-infrared to visible (NIR-VIS) face recognition is the most common case in heterogeneous face recognition, which aims to match a pair of face images captured from two different…
Student Network Learning via Evolutionary Knowledge Distillation
Kangkai Zhang, Chunhui Zhang, Shikun Li +2
Knowledge distillation provides an effective way to transfer knowledge via teacher-student learning, where most existing distillation approaches apply a fixed pre-trained model as…
The Elements of End-to-end Deep Face Recognition: A Survey of Recent Advances
Hang Du, Hailin Shi, Dan Zeng +2
Face recognition is one of the most popular and long-standing topics in computer vision. With the recent development of deep learning techniques and large-scale datasets, deep face…
Semi-Siamese Training for Shallow Face Learning
Hang Du, Hailin Shi, Yuchi Liu +4
Most existing public face datasets, such as MS-Celeb-1M and VGGFace2, provide abundant information in both breadth (large number of IDs) and depth (sufficient number of samples) fo…
NPCFace: Negative-Positive Collaborative Training for Large-scale Face Recognition
Dan Zeng, Hailin Shi, Hang Du +3
The training scheme of deep face recognition has greatly evolved in the past years, yet it encounters new challenges in the large-scale data situation where massive and diverse har…
A survey of face recognition techniques under occlusion
Dan Zeng, Raymond Veldhuis, Luuk Spreeuwers
The limited capacity to recognize faces under occlusions is a long-standing problem that presents a unique challenge for face recognition systems and even for humans. The problem r…
Robust Visual Object Tracking with Two-Stream Residual Convolutional Networks
Ning Zhang, Jingen Liu, Ke Wang +2
The current deep learning based visual tracking approaches have been very successful by learning the target classification and/or estimation model from a large amount of supervised…
Zooming into Face Forensics: A Pixel-level Analysis
Jia Li, Tong Shen, Wei Zhang +3
The stunning progress in face manipulation methods has made it possible to synthesize realistic fake face images, which poses potential threats to our society. It is urgent to have…
Theme-Matters: Fashion Compatibility Learning via Theme Attention
Jui-Hsin Lai, Bo Wu, Xin Wang +3
Fashion compatibility learning is important to many fashion markets such as outfit composition and online fashion recommendation. Unlike previous work, we argue that fashion compat…
Flexible Auto-weighted Local-coordinate Concept Factorization: A Robust Framework for Unsupervised Clustering
Zhao Zhang, Yan Zhang, Sheng Li +4
Concept Factorization (CF) and its variants may produce inaccurate representation and clustering results due to the sensitivity to noise, hard constraint on the reconstruction erro…
MetaAdvDet: Towards Robust Detection of Evolving Adversarial Attacks
Chen Ma, Chenxu Zhao, Hailin Shi +3
Deep neural networks (DNNs) are vulnerable to adversarial attack which is maliciously implemented by adding human-imperceptible perturbation to images and thus leads to incorrect p…