84 citations · 260 across the 21 of their papers we have counts for
13 papers · 1 filter
Semi-Supervised Heterogeneous Graph Learning with Multi-level Data Augmentation
Ying Chen, Siwei Qiang, Mingming Ha +5
In recent years, semi-supervised graph learning with data augmentation (DA) is currently the most commonly used and best-performing method to enhance model robustness in sparse sce…
OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression
Wanhua Li, Xiaoke Huang, Zheng Zhu +4
This paper presents a language-powered paradigm for ordinal regression. Existing methods usually treat each rank as a category and employ a set of weights to learn these concepts.…
Divide to Adapt: Mitigating Confirmation Bias for Domain Adaptation of Black-Box Predictors
Jianfei Yang, Xiangyu Peng, Kai Wang +4
Domain Adaptation of Black-box Predictors (DABP) aims to learn a model on an unlabeled target domain supervised by a black-box predictor trained on a source domain. It does not req…
FaceMAE: Privacy-Preserving Face Recognition via Masked Autoencoders
Kai Wang, Bo Zhao, Xiangyu Peng +5
Face recognition, as one of the most successful applications in artificial intelligence, has been widely used in security, administration, advertising, and healthcare. However, the…
BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous Driving
Yunpeng Zhang, Zheng Zhu, Wenzhao Zheng +4
In this paper, we present BEVerse, a unified framework for 3D perception and prediction based on multi-camera systems. Unlike existing studies focusing on the improvement of single…
Introspective Deep Metric Learning for Image Retrieval
Wenzhao Zheng, Chengkun Wang, Jie Zhou +1
This paper proposes an introspective deep metric learning (IDML) framework for uncertainty-aware comparisons of images. Conventional deep metric learning methods produce confident…
Reliable Label Correction is a Good Booster When Learning with Extremely Noisy Labels
Kai Wang, Xiangyu Peng, Shuo Yang +4
Learning with noisy labels has aroused much research interest since data annotations, especially for large-scale datasets, may be inevitably imperfect. Recent approaches resort to…
WebFace260M: A Benchmark for Million-Scale Deep Face Recognition
Zheng Zhu, Guan Huang, Jiankang Deng +8
Face benchmarks empower the research community to train and evaluate high-performance face recognition systems. In this paper, we contribute a new million-scale recognition benchma…
MVSTER: Epipolar Transformer for Efficient Multi-View Stereo
Xiaofeng Wang, Zheng Zhu, Fangbo Qin +5
Learning-based Multi-View Stereo (MVS) methods warp source images into the reference camera frustum to form 3D volumes, which are fused as a cost volume to be regularized by subseq…
HFT: Lifting Perspective Representations via Hybrid Feature Transformation
Jiayu Zou, Junrui Xiao, Zheng Zhu +4
Autonomous driving requires accurate and detailed Bird's Eye View (BEV) semantic segmentation for decision making, which is one of the most challenging tasks for high-level scene p…
SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth Estimation
Yi Wei, Linqing Zhao, Wenzhao Zheng +5
Depth estimation from images serves as the fundamental step of 3D perception for autonomous driving and is an economical alternative to expensive depth sensors like LiDAR. The temp…
Crafting Better Contrastive Views for Siamese Representation Learning
Xiangyu Peng, Kai Wang, Zheng Zhu +2
Recent self-supervised contrastive learning methods greatly benefit from the Siamese structure that aims at minimizing distances between positive pairs. For high performance Siames…
Decoupled Multi-task Learning with Cyclical Self-Regulation for Face Parsing
Qingping Zheng, Jiankang Deng, Zheng Zhu +2
This paper probes intrinsic factors behind typical failure cases (e.g. spatial inconsistency and boundary confusion) produced by the existing state-of-the-art method in face parsin…