227 citations · 418 across the 20 of their papers we have counts for
20 papers
RaBit: Parametric Modeling of 3D Biped Cartoon Characters with a Topological-consistent Dataset
Zhongjin Luo, Shengcai Cai, Jinguo Dong +4
Assisting people in efficiently producing visually plausible 3D characters has always been a fundamental research topic in computer vision and computer graphics. Recent learning-ba…
Open Long-Tailed Recognition in a Dynamic World
Ziwei Liu, Zhongqi Miao, Xiaohang Zhan +3
Real world data often exhibits a long-tailed and open-ended (with unseen classes) distribution. A practical recognition system must balance between majority (head) and minority (ta…
Masked Frequency Modeling for Self-Supervised Visual Pre-Training
Jiahao Xie, Wei Li, Xiaohang Zhan +3
We present Masked Frequency Modeling (MFM), a unified frequency-domain-based approach for self-supervised pre-training of visual models. Instead of randomly inserting mask tokens t…
Style-ERD: Responsive and Coherent Online Motion Style Transfer
Tianxin Tao, Xiaohang Zhan, Zhongquan Chen +1
Motion style transfer is a common method for enriching character animation. Motion style transfer algorithms are often designed for offline settings where motions are processed in…
Unsupervised Object-Level Representation Learning from Scene Images
Jiahao Xie, Xiaohang Zhan, Ziwei Liu +2
Contrastive self-supervised learning has largely narrowed the gap to supervised pre-training on ImageNet. However, its success highly relies on the object-centric priors of ImageNe…
DetCo: Unsupervised Contrastive Learning for Object Detection
Enze Xie, Jian Ding, Wenhai Wang +5
Unsupervised contrastive learning achieves great success in learning image representations with CNN. Unlike most recent methods that focused on improving accuracy of image classifi…
A Unified Mixture-View Framework for Unsupervised Representation Learning
Xiangxiang Chu, Xiaohang Zhan, Bo Zhang
Recent unsupervised contrastive representation learning follows a Single Instance Multi-view (SIM) paradigm where positive pairs are usually constructed with intra-image data augme…
Delving into Inter-Image Invariance for Unsupervised Visual Representations
Jiahao Xie, Xiaohang Zhan, Ziwei Liu +2
Contrastive learning has recently shown immense potential in unsupervised visual representation learning. Existing studies in this track mainly focus on intra-image invariance lear…
Online Deep Clustering for Unsupervised Representation Learning
Xiaohang Zhan, Jiahao Xie, Ziwei Liu +2
Joint clustering and feature learning methods have shown remarkable performance in unsupervised representation learning. However, the training schedule alternating between feature…
Self-Supervised Scene De-occlusion
Xiaohang Zhan, Xingang Pan, Bo Dai +3
Natural scene understanding is a challenging task, particularly when encountering images of multiple objects that are partially occluded. This obstacle is given rise by varying obj…
Learning to Cluster Faces via Confidence and Connectivity Estimation
Lei Yang, Dapeng Chen, Xiaohang Zhan +3
Face clustering is an essential tool for exploiting the unlabeled face data, and has a wide range of applications including face annotation and retrieval. Recent works show that su…
Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation
Xingang Pan, Xiaohang Zhan, Bo Dai +3
Learning a good image prior is a long-term goal for image restoration and manipulation. While existing methods like deep image prior (DIP) capture low-level image statistics, there…
Open Compound Domain Adaptation
Ziwei Liu, Zhongqi Miao, Xingang Pan +4
A typical domain adaptation approach is to adapt models trained on the annotated data in a source domain (e.g., sunny weather) for achieving high performance on the test data in a…
Learning to Cluster Faces on an Affinity Graph
Lei Yang, Xiaohang Zhan, Dapeng Chen +3
Face recognition sees remarkable progress in recent years, and its performance has reached a very high level. Taking it to a next level requires substantially larger data, which wo…
Self-Supervised Learning via Conditional Motion Propagation
Xiaohang Zhan, Xingang Pan, Ziwei Liu +2
Intelligent agent naturally learns from motion. Various self-supervised algorithms have leveraged motion cues to learn effective visual representations. The hurdle here is that mot…
Large-Scale Long-Tailed Recognition in an Open World
Ziwei Liu, Zhongqi Miao, Xiaohang Zhan +3
Real world data often have a long-tailed and open-ended distribution. A practical recognition system must classify among majority and minority classes, generalize from a few known…
Switchable Whitening for Deep Representation Learning
Xingang Pan, Xiaohang Zhan, Jianping Shi +2
Normalization methods are essential components in convolutional neural networks (CNNs). They either standardize or whiten data using statistics estimated in predefined sets of pixe…
Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition
Xiaohang Zhan, Ziwei Liu, Junjie Yan +2
Face recognition has witnessed great progress in recent years, mainly attributed to the high-capacity model designed and the abundant labeled data collected. However, it becomes mo…
Mix-and-Match Tuning for Self-Supervised Semantic Segmentation
Xiaohang Zhan, Ziwei Liu, Ping Luo +2
Deep convolutional networks for semantic image segmentation typically require large-scale labeled data, e.g. ImageNet and MS COCO, for network pre-training. To reduce annotation ef…
Spatial As Deep: Spatial CNN for Traffic Scene Understanding
Xingang Pan, Xiaohang Zhan, Jianping Shi +3
Convolutional neural networks (CNNs) are usually built by stacking convolutional operations layer-by-layer. Although CNN has shown strong capability to extract semantics from raw p…