1.6k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Vi Technology (United States)US14 papers
- Tsinghua UniversityCN12 papers
- Peking UniversityCN10 papers
- University of Electronic Science and Technology of ChinaCN10 papers
- Chinese Academy of SciencesCN9 papers
- Huazhong University of Science and TechnologyCN8 papers
- Hong Kong University of Science and TechnologyHK7 papers
- Tongji UniversityCN5 papers
- University of Science and Technology of ChinaCN5 papers
- Chinese University of Hong KongHK4 papers
- Institute of AutomationCN4 papers
- Tencent (China)CN4 papers
30 papers · 2 filters
NBNet: Noise Basis Learning for Image Denoising with Subspace Projection
Shen Cheng, Yuzhi Wang, Haibin Huang +3
In this paper, we introduce NBNet, a novel framework for image denoising. Unlike previous works, we propose to tackle this challenging problem from a new perspective: noise reducti…
Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection
Tiancai Wang, Tong Yang, Jiale Cao +1
Object detectors usually achieve promising results with the supervision of complete instance annotations. However, their performance is far from satisfactory with sparse instance a…
EqCo: Equivalent Rules for Self-supervised Contrastive Learning
Benjin Zhu, Junqiang Huang, Zeming Li +2
In this paper, we propose EqCo (Equivalent Rules for Contrastive Learning) to make self-supervised learning irrelevant to the number of negative samples in the contrastive learning…
TP-LSD: Tri-Points Based Line Segment Detector
Siyu Huang, Fangbo Qin, Pengfei Xiong +3
This paper proposes a novel deep convolutional model, Tri-Points Based Line Segment Detector (TP-LSD), to detect line segments in an image at real-time speed. The previous related…
LabelEnc: A New Intermediate Supervision Method for Object Detection
Miao Hao, Yitao Liu, Xiangyu Zhang +1
In this paper we propose a new intermediate supervision method, named LabelEnc, to boost the training of object detection systems. The key idea is to introduce a novel label encodi…
Spherical Feature Transform for Deep Metric Learning
Yuke Zhu, Yan Bai, Yichen Wei
Data augmentation in feature space is effective to increase data diversity. Previous methods assume that different classes have the same covariance in their feature distributions.…
Prime-Aware Adaptive Distillation
Youcai Zhang, Zhonghao Lan, Yuchen Dai +4
Knowledge distillation(KD) aims to improve the performance of a student network by mimicing the knowledge from a powerful teacher network. Existing methods focus on studying what k…
Hierarchical Context Embedding for Region-based Object Detection
Zhao-Min Chen, Xin Jin, Borui Zhao +2
State-of-the-art two-stage object detectors apply a classifier to a sparse set of object proposals, relying on region-wise features extracted by RoIPool or RoIAlign as inputs. The…
Funnel Activation for Visual Recognition
Ningning Ma, Xiangyu Zhang, Jian Sun
We present a conceptually simple but effective funnel activation for image recognition tasks, called Funnel activation (FReLU), that extends ReLU and PReLU to a 2D activation by ad…
WeightNet: Revisiting the Design Space of Weight Networks
Ningning Ma, Xiangyu Zhang, Jiawei Huang +1
We present a conceptually simple, flexible and effective framework for weight generating networks. Our approach is general that unifies two current distinct and extremely effective…
CycAs: Self-supervised Cycle Association for Learning Re-identifiable Descriptions
Zhongdao Wang, Jingwei Zhang, Liang Zheng +4
This paper proposes a self-supervised learning method for the person re-identification (re-ID) problem, where existing unsupervised methods usually rely on pseudo labels, such as t…
BorderDet: Border Feature for Dense Object Detection
Han Qiu, Yuchen Ma, Zeming Li +2
Dense object detectors rely on the sliding-window paradigm that predicts the object over a regular grid of image. Meanwhile, the feature maps on the point of the grid are adopted t…
Weight-dependent Gates for Network Pruning
Yun Li, Zechun Liu, Weiqun Wu +4
In this paper, a simple yet effective network pruning framework is proposed to simultaneously address the problems of pruning indicator, pruning ratio, and efficiency constraint. T…
3DV: 3D Dynamic Voxel for Action Recognition in Depth Video
Yancheng Wang, Yang Xiao, Fu Xiong +4
To facilitate depth-based 3D action recognition, 3D dynamic voxel (3DV) is proposed as a novel 3D motion representation. With 3D space voxelization, the key idea of 3DV is to encod…
Joint Multi-Dimension Pruning via Numerical Gradient Update
Zechun Liu, Xiangyu Zhang, Zhiqiang Shen +4
We present joint multi-dimension pruning (abbreviated as JointPruning), an effective method of pruning a network on three crucial aspects: spatial, depth and channel simultaneously…
SQE: a Self Quality Evaluation Metric for Parameters Optimization in Multi-Object Tracking
Yanru Huang, Feiyu Zhu, Zheni Zeng +3
We present a novel self quality evaluation metric SQE for parameters optimization in the challenging yet critical multi-object tracking task. Current evaluation metrics all require…
Parsing-based View-aware Embedding Network for Vehicle Re-Identification
Dechao Meng, Liang Li, Xuejing Liu +6
Vehicle Re-Identification is to find images of the same vehicle from various views in the cross-camera scenario. The main challenges of this task are the large intra-instance dista…
Attentive Normalization for Conditional Image Generation
Yi Wang, Ying-Cong Chen, Xiangyu Zhang +2
Traditional convolution-based generative adversarial networks synthesize images based on hierarchical local operations, where long-range dependency relation is implicitly modeled w…
High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification
Guan'an Wang, Shuo Yang, Huanyu Liu +6
Occluded person re-identification (ReID) aims to match occluded person images to holistic ones across dis-joint cameras. In this paper, we propose a novel framework by learning hig…
Leveraging Planar Regularities for Point Line Visual-Inertial Odometry
Xin Li, Yijia He, Jinlong Lin +1
With monocular Visual-Inertial Odometry (VIO) system, 3D point cloud and camera motion can be estimated simultaneously. Because pure sparse 3D points provide a structureless repres…
DPGN: Distribution Propagation Graph Network for Few-shot Learning
Ling Yang, Liangliang Li, Zilun Zhang +3
Most graph-network-based meta-learning approaches model instance-level relation of examples. We extend this idea further to explicitly model the distribution-level relation of one…
Learning Human-Object Interaction Detection using Interaction Points
Tiancai Wang, Tong Yang, Martin Danelljan +3
Understanding interactions between humans and objects is one of the fundamental problems in visual classification and an essential step towards detailed scene understanding. Human-…
Data Uncertainty Learning in Face Recognition
Jie Chang, Zhonghao Lan, Changmao Cheng +1
Modeling data uncertainty is important for noisy images, but seldom explored for face recognition. The pioneer work, PFE, considers uncertainty by modeling each face image embeddin…
Learning Dynamic Routing for Semantic Segmentation
Yanwei Li, Lin Song, Yukang Chen +4
Recently, numerous handcrafted and searched networks have been applied for semantic segmentation. However, previous works intend to handle inputs with various scales in pre-defined…