145 citations · 910 across the 56 of their papers we have counts for
59 papers
Dynamic Hypergraph Structure Learning for Traffic Flow Forecasting
Yusheng Zhao, Xiao Luo, Wei Ju +3
This paper studies the problem of traffic flow forecasting, which aims to predict future traffic conditions on the basis of road networks and traffic conditions in the past. The pr…
Invariant Training 2D-3D Joint Hard Samples for Few-Shot Point Cloud Recognition
Xuanyu Yi, Jiajun Deng, Qianru Sun +3
We tackle the data scarcity challenge in few-shot point cloud recognition of 3D objects by using a joint prediction from a conventional 3D model and a well-trained 2D model. Surpri…
Random Boxes Are Open-world Object Detectors
Yanghao Wang, Zhongqi Yue, Xian-Sheng Hua +1
We show that classifiers trained with random region proposals achieve state-of-the-art Open-world Object Detection (OWOD): they can not only maintain the accuracy of the known obje…
TGNN: A Joint Semi-supervised Framework for Graph-level Classification
Wei Ju, Xiao Luo, Meng Qu +5
This paper studies semi-supervised graph classification, a crucial task with a wide range of applications in social network analysis and bioinformatics. Recent works typically adop…
Class Is Invariant to Context and Vice Versa: On Learning Invariance for Out-Of-Distribution Generalization
Jiaxin Qi, Kaihua Tang, Qianru Sun +2
Out-Of-Distribution generalization (OOD) is all about learning invariance against environmental changes. If the context in every class is evenly distributed, OOD would be trivial b…
Towards Counterfactual Image Manipulation via CLIP
Yingchen Yu, Fangneng Zhan, Rongliang Wu +6
Leveraging StyleGAN's expressivity and its disentangled latent codes, existing methods can achieve realistic editing of different visual attributes such as age and gender of facial…
Identifying Hard Noise in Long-Tailed Sample Distribution
Xuanyu Yi, Kaihua Tang, Xian-Sheng Hua +2
Conventional de-noising methods rely on the assumption that all samples are independent and identically distributed, so the resultant classifier, though disturbed by noise, can sti…
On Non-Random Missing Labels in Semi-Supervised Learning
Xinting Hu, Yulei Niu, Chunyan Miao +2
Semi-Supervised Learning (SSL) is fundamentally a missing label problem, in which the label Missing Not At Random (MNAR) problem is more realistic and challenging, compared to the…
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
Yawei Li, Kai Zhang, Radu Timofte +108
This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-reso…
Spatial Likelihood Voting with Self-Knowledge Distillation for Weakly Supervised Object Detection
Ze Chen, Zhihang Fu, Jianqiang Huang +5
Weakly supervised object detection (WSOD), which is an effective way to train an object detection model using only image-level annotations, has attracted considerable attention fro…
Dynamic Supervisor for Cross-dataset Object Detection
Ze Chen, Zhihang Fu, Jianqiang Huang +6
The application of cross-dataset training in object detection tasks is complicated because the inconsistency in the category range across datasets transforms fully supervised learn…
Disentangled Representation Learning for Text-Video Retrieval
Qiang Wang, Yanhao Zhang, Yun Zheng +2
Cross-modality interaction is a critical component in Text-Video Retrieval (TVR), yet there has been little examination of how different influencing factors for computing interacti…
Class Re-Activation Maps for Weakly-Supervised Semantic Segmentation
Zhaozheng Chen, Tan Wang, Xiongwei Wu +3
Extracting class activation maps (CAM) is arguably the most standard step of generating pseudo masks for weakly-supervised semantic segmentation (WSSS). Yet, we find that the crux…
Offline-Online Associated Camera-Aware Proxies for Unsupervised Person Re-identification
Menglin Wang, Jiachen Li, Baisheng Lai +2
Recently, unsupervised person re-identification (Re-ID) has received increasing research attention due to its potential for label-free applications. A promising way to address unsu…
Cross-Domain Empirical Risk Minimization for Unbiased Long-tailed Classification
Beier Zhu, Yulei Niu, Xian-Sheng Hua +1
We address the overlooked unbiasedness in existing long-tailed classification methods: we find that their overall improvement is mostly attributed to the biased preference of tail…
Meta Clustering Learning for Large-scale Unsupervised Person Re-identification
Xin Jin, Tianyu He, Xu Shen +5
Unsupervised Person Re-identification (U-ReID) with pseudo labeling recently reaches a competitive performance compared to fully-supervised ReID methods based on modern clustering…
Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression
Jiabo He, Sarah Erfani, Xingjun Ma +3
Bounding box (bbox) regression is a fundamental task in computer vision. So far, the most commonly used loss functions for bbox regression are the Intersection over Union (IoU) los…
Transporting Causal Mechanisms for Unsupervised Domain Adaptation
Zhongqi Yue, Qianru Sun, Xian-Sheng Hua +1
Existing Unsupervised Domain Adaptation (UDA) literature adopts the covariate shift and conditional shift assumptions, which essentially encourage models to learn common features a…
Attention-guided Temporally Coherent Video Object Matting
Yunke Zhang, Chi Wang, Miaomiao Cui +6
This paper proposes a novel deep learning-based video object matting method that can achieve temporally coherent matting results. Its key component is an attention-based temporal a…
Graph Contrastive Clustering
Huasong Zhong, Jianlong Wu, Chong Chen +5
Recently, some contrastive learning methods have been proposed to simultaneously learn representations and clustering assignments, achieving significant improvements. However, thes…
Half-Real Half-Fake Distillation for Class-Incremental Semantic Segmentation
Zilong Huang, Wentian Hao, Xinggang Wang +4
Despite their success for semantic segmentation, convolutional neural networks are ill-equipped for incremental learning, \ie, adapting the original segmentation model as new class…
Distilling Causal Effect of Data in Class-Incremental Learning
Xinting Hu, Kaihua Tang, Chunyan Miao +2
We propose a causal framework to explain the catastrophic forgetting in Class-Incremental Learning (CIL) and then derive a novel distillation method that is orthogonal to the exist…
Cloth-Changing Person Re-identification from A Single Image with Gait Prediction and Regularization
Xin Jin, Tianyu He, Kecheng Zheng +7
Cloth-Changing person re-identification (CC-ReID) aims at matching the same person across different locations over a long-duration, e.g., over days, and therefore inevitably meets…
Dense Interaction Learning for Video-based Person Re-identification
Tianyu He, Xin Jin, Xu Shen +3
Video-based person re-identification (re-ID) aims at matching the same person across video clips. Efficiently exploiting multi-scale fine-grained features while building the struct…