480 citations · 2.2k across the 106 of their papers we have counts for
35 papers · 1 filter
Investigating Pose Representations and Motion Contexts Modeling for 3D Motion Prediction
Zhenguang Liu, Shuang Wu, Shuyuan Jin +4
Predicting human motion from historical pose sequence is crucial for a machine to succeed in intelligent interactions with humans. One aspect that has been obviated so far, is the…
Multimodal Image Synthesis and Editing: The Generative AI Era
Fangneng Zhan, Yingchen Yu, Rongliang Wu +6
As information exists in various modalities in real world, effective interaction and fusion among multimodal information plays a key role for the creation and perception of multimo…
PTTR: Relational 3D Point Cloud Object Tracking with Transformer
Changqing Zhou, Zhipeng Luo, Yueru Luo +5
In a point cloud sequence, 3D object tracking aims to predict the location and orientation of an object in the current search point cloud given a template point cloud. Motivated by…
Music-to-Dance Generation with Optimal Transport
Shuang Wu, Shijian Lu, Li Cheng
Dance choreography for a piece of music is a challenging task, having to be creative in presenting distinctive stylistic dance elements while taking into account the musical theme…
Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Data
Jiaxing Huang, Dayan Guan, Aoran Xiao +1
Unsupervised domain adaptation aims to align a labeled source domain and an unlabeled target domain, but it requires to access the source data which often raises concerns in data p…
GenCo: Generative Co-training for Generative Adversarial Networks with Limited Data
Kaiwen Cui, Jiaxing Huang, Zhipeng Luo +3
Training effective Generative Adversarial Networks (GANs) requires large amounts of training data, without which the trained models are usually sub-optimal with discriminator over-…
Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency
Zhipeng Luo, Zhongang Cai, Changqing Zhou +7
Deep learning-based 3D object detection has achieved unprecedented success with the advent of large-scale autonomous driving datasets. However, drastic performance degradation rema…
Skeleton Cloud Colorization for Unsupervised 3D Action Representation Learning
Siyuan Yang, Jun Liu, Shijian Lu +2
Skeleton-based human action recognition has attracted increasing attention in recent years. However, most of the existing works focus on supervised learning which requiring a large…
WaveFill: A Wavelet-based Generation Network for Image Inpainting
Yingchen Yu, Fangneng Zhan, Shijian Lu +4
Image inpainting aims to complete the missing or corrupted regions of images with realistic contents. The prevalent approaches adopt a hybrid objective of reconstruction and percep…
Domain Adaptive Video Segmentation via Temporal Consistency Regularization
Dayan Guan, Jiaxing Huang, Aoran Xiao +1
Video semantic segmentation is an essential task for the analysis and understanding of videos. Recent efforts largely focus on supervised video segmentation by learning from fully…
FBC-GAN: Diverse and Flexible Image Synthesis via Foreground-Background Composition
Kaiwen Cui, Gongjie Zhang, Fangneng Zhan +2
Generative Adversarial Networks (GANs) have become the de-facto standard in image synthesis. However, without considering the foreground-background decomposition, existing GANs ten…
Learning Disentangled Representation Implicitly via Transformer for Occluded Person Re-Identification
Mengxi Jia, Xinhua Cheng, Shijian Lu +1
Person re-identification (re-ID) under various occlusions has been a long-standing challenge as person images with different types of occlusions often suffer from misalignment in i…
Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic Segmentation
Aoran Xiao, Jiaxing Huang, Dayan Guan +2
Knowledge transfer from synthetic to real data has been widely studied to mitigate data annotation constraints in various computer vision tasks such as semantic segmentation. Howev…
Bi-level Feature Alignment for Versatile Image Translation and Manipulation
Fangneng Zhan, Yingchen Yu, Rongliang Wu +5
Generative adversarial networks (GANs) have achieved great success in image translation and manipulation. However, high-fidelity image generation with faithful style control remain…
Blind Image Super-Resolution via Contrastive Representation Learning
Jiahui Zhang, Shijian Lu, Fangneng Zhan +1
Image super-resolution (SR) research has witnessed impressive progress thanks to the advance of convolutional neural networks (CNNs) in recent years. However, most existing SR meth…
Sparse Needlets for Lighting Estimation with Spherical Transport Loss
Fangneng Zhan, Changgong Zhang, Wenbo Hu +4
Accurate lighting estimation is challenging yet critical to many computer vision and computer graphics tasks such as high-dynamic-range (HDR) relighting. Existing approaches model…
Unbalanced Feature Transport for Exemplar-based Image Translation
Fangneng Zhan, Yingchen Yu, Kaiwen Cui +7
Despite the great success of GANs in images translation with different conditioned inputs such as semantic segmentation and edge maps, generating high-fidelity realistic images wit…
Domain Consistency Regularization for Unsupervised Multi-source Domain Adaptive Classification
Zhipeng Luo, Xiaobing Zhang, Shijian Lu +1
Deep learning-based multi-source unsupervised domain adaptation (MUDA) has been actively studied in recent years. Compared with single-source unsupervised domain adaptation (SUDA),…
Semi-Supervised Domain Adaptation via Adaptive and Progressive Feature Alignment
Jiaxing Huang, Dayan Guan, Aoran Xiao +1
Contemporary domain adaptive semantic segmentation aims to address data annotation challenges by assuming that target domains are completely unannotated. However, annotating a few…
Spectral Unsupervised Domain Adaptation for Visual Recognition
Jingyi Zhang, Jiaxing Huang, Zichen Tian +1
Though unsupervised domain adaptation (UDA) has achieved very impressive progress recently, it remains a great challenge due to missing target annotations and the rich discrepancy…
Category Contrast for Unsupervised Domain Adaptation in Visual Tasks
Jiaxing Huang, Dayan Guan, Aoran Xiao +2
Instance contrast for unsupervised representation learning has achieved great success in recent years. In this work, we explore the idea of instance contrastive learning in unsuper…
RDA: Robust Domain Adaptation via Fourier Adversarial Attacking
Jiaxing Huang, Dayan Guan, Aoran Xiao +1
Unsupervised domain adaptation (UDA) involves a supervised loss in a labeled source domain and an unsupervised loss in an unlabeled target domain, which often faces more severe ove…
Diverse Image Inpainting with Bidirectional and Autoregressive Transformers
Yingchen Yu, Fangneng Zhan, Rongliang Wu +6
Image inpainting is an underdetermined inverse problem, which naturally allows diverse contents to fill up the missing or corrupted regions realistically. Prevalent approaches usin…
I2C2W: Image-to-Character-to-Word Transformers for Accurate Scene Text Recognition
Chuhui Xue, Jiaxing Huang, Wenqing Zhang +3
Leveraging the advances of natural language processing, most recent scene text recognizers adopt an encoder-decoder architecture where text images are first converted to representa…