Search Results for author: Xiaohang Zhan

Found 22 papers, 13 papers with code

HumanGaussian: Text-Driven 3D Human Generation with Gaussian Splatting

no code implementations28 Nov 2023 Xian Liu, Xiaohang Zhan, Jiaxiang Tang, Ying Shan, Gang Zeng, Dahua Lin, Xihui Liu, Ziwei Liu

In this paper, we propose an efficient yet effective framework, HumanGaussian, that generates high-quality 3D humans with fine-grained geometry and realistic appearance.

SemanticBoost: Elevating Motion Generation with Augmented Textual Cues

no code implementations31 Oct 2023 Xin He, Shaoli Huang, Xiaohang Zhan, Chao Weng, Ying Shan

Our framework comprises a Semantic Enhancement module and a Context-Attuned Motion Denoiser (CAMD).

TapMo: Shape-aware Motion Generation of Skeleton-free Characters

no code implementations19 Oct 2023 Jiaxu Zhang, Shaoli Huang, Zhigang Tu, Xin Chen, Xiaohang Zhan, Gang Yu, Ying Shan

In this work, we present TapMo, a Text-driven Animation Pipeline for synthesizing Motion in a broad spectrum of skeleton-free 3D characters.

RaBit: Parametric Modeling of 3D Biped Cartoon Characters with a Topological-consistent Dataset

no code implementations CVPR 2023 Zhongjin Luo, Shengcai Cai, Jinguo Dong, Ruibo Ming, Liangdong Qiu, Xiaohang Zhan, Xiaoguang Han

However, none of the prior works focus on modeling 3D biped cartoon characters, which are also in great demand in gaming and filming.

Open Long-Tailed Recognition in a Dynamic World

no code implementations17 Aug 2022 Ziwei Liu, Zhongqi Miao, Xiaohang Zhan, Jiayun Wang, Boqing Gong, Stella X. Yu

A practical recognition system must balance between majority (head) and minority (tail) classes, generalize across the distribution, and acknowledge novelty upon the instances of unseen classes (open classes).

Active Learning Classification +4

Masked Frequency Modeling for Self-Supervised Visual Pre-Training

3 code implementations15 Jun 2022 Jiahao Xie, Wei Li, Xiaohang Zhan, Ziwei Liu, Yew Soon Ong, Chen Change Loy

We present Masked Frequency Modeling (MFM), a unified frequency-domain-based approach for self-supervised pre-training of visual models.

Image Classification Image Restoration +2

Unsupervised Object-Level Representation Learning from Scene Images

1 code implementation NeurIPS 2021 Jiahao Xie, Xiaohang Zhan, Ziwei Liu, Yew Soon Ong, Chen Change Loy

Extensive experiments on COCO show that ORL significantly improves the performance of self-supervised learning on scene images, even surpassing supervised ImageNet pre-training on several downstream tasks.

Object Representation Learning +2

DetCo: Unsupervised Contrastive Learning for Object Detection

2 code implementations ICCV 2021 Enze Xie, Jian Ding, Wenhai Wang, Xiaohang Zhan, Hang Xu, Peize Sun, Zhenguo Li, Ping Luo

Unlike most recent methods that focused on improving accuracy of image classification, we present a novel contrastive learning approach, named DetCo, which fully explores the contrasts between global image and local image patches to learn discriminative representations for object detection.

Contrastive Learning Image Classification +2

A Unified Mixture-View Framework for Unsupervised Representation Learning

no code implementations26 Nov 2020 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 augmentation.

Data Augmentation object-detection +2

Delving into Inter-Image Invariance for Unsupervised Visual Representations

2 code implementations26 Aug 2020 Jiahao Xie, Xiaohang Zhan, Ziwei Liu, Yew Soon Ong, Chen Change Loy

In this work, we present a comprehensive empirical study to better understand the role of inter-image invariance learning from three main constituting components: pseudo-label maintenance, sampling strategy, and decision boundary design.

Contrastive Learning Pseudo Label +1

Self-Supervised Scene De-occlusion

2 code implementations CVPR 2020 Xiaohang Zhan, Xingang Pan, Bo Dai, Ziwei Liu, Dahua Lin, Chen Change Loy

This is achieved via Partial Completion Network (PCNet)-mask (M) and -content (C), that learn to recover fractions of object masks and contents, respectively, in a self-supervised manner.

Image Manipulation Scene Understanding

Learning to Cluster Faces via Confidence and Connectivity Estimation

3 code implementations CVPR 2020 Lei Yang, Dapeng Chen, Xiaohang Zhan, Rui Zhao, Chen Change Loy, Dahua Lin

With the vertex confidence and edge connectivity, we can naturally organize more relevant vertices on the affinity graph and group them into clusters.

Clustering Connectivity Estimation +2

Open Compound Domain Adaptation

no code implementations CVPR 2020 Ziwei Liu, Zhongqi Miao, Xingang Pan, Xiaohang Zhan, Dahua Lin, Stella X. Yu, Boqing Gong

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 target domain (e. g., rainy weather).

Domain Adaptation Facial Expression Recognition +3

Switchable Whitening for Deep Representation Learning

1 code implementation ICCV 2019 Xingang Pan, Xiaohang Zhan, Jianping Shi, Xiaoou Tang, Ping Luo

Unlike existing works that design normalization techniques for specific tasks, we propose Switchable Whitening (SW), which provides a general form unifying different whitening methods as well as standardization methods.

Domain Adaptation Image Classification +4

Large-Scale Long-Tailed Recognition in an Open World

2 code implementations CVPR 2019 Ziwei Liu, Zhongqi Miao, Xiaohang Zhan, Jiayun Wang, Boqing Gong, Stella X. Yu

We define Open Long-Tailed Recognition (OLTR) as learning from such naturally distributed data and optimizing the classification accuracy over a balanced test set which include head, tail, and open classes.

Classification Few-Shot Learning +5

Learning to Cluster Faces on an Affinity Graph

3 code implementations CVPR 2019 Lei Yang, Xiaohang Zhan, Dapeng Chen, Junjie Yan, Chen Change Loy, Dahua Lin

Face recognition sees remarkable progress in recent years, and its performance has reached a very high level.

Clustering Face Recognition +1

Self-Supervised Learning via Conditional Motion Propagation

1 code implementation CVPR 2019 Xiaohang Zhan, Xingang Pan, Ziwei Liu, Dahua Lin, Chen Change Loy

Instead of explicitly modeling the motion probabilities, we design the pretext task as a conditional motion propagation problem.

Human Parsing Instance Segmentation +2

Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition

4 code implementations ECCV 2018 Xiaohang Zhan, Ziwei Liu, Junjie Yan, Dahua Lin, Chen Change Loy

Face recognition has witnessed great progress in recent years, mainly attributed to the high-capacity model designed and the abundant labeled data collected.

Face Recognition

Mix-and-Match Tuning for Self-Supervised Semantic Segmentation

no code implementations2 Dec 2017 Xiaohang Zhan, Ziwei Liu, Ping Luo, Xiaoou Tang, Chen Change Loy

The key of this new form of learning is to design a proxy task (e. g. image colorization), from which a discriminative loss can be formulated on unlabeled data.

Colorization Image Colorization +3

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