194 citations · 426 across the 16 of their papers we have counts for
16 papers
W2N:Switching From Weak Supervision to Noisy Supervision for Object Detection
Zitong Huang, Yiping Bao, Bowen Dong +2
Weakly-supervised object detection (WSOD) aims to train an object detector only requiring the image-level annotations. Recently, some works have managed to select the accurate boxe…
Attend to Who You Are: Supervising Self-Attention for Keypoint Detection and Instance-Aware Association
Sen Yang, Zhicheng Wang, Ze Chen +7
This paper presents a new method to solve keypoint detection and instance association by using Transformer. For bottom-up multi-person pose estimation models, they need to detect k…
Guiding Query Position and Performing Similar Attention for Transformer-Based Detection Heads
Xiaohu Jiang, Ze Chen, Zhicheng Wang +2
After DETR was proposed, this novel transformer-based detection paradigm which performs several cross-attentions between object queries and feature maps for predictions has subsequ…
Adaptive Dilated Convolution For Human Pose Estimation
Zhengxiong Luo, Zhicheng Wang, Yan Huang +3
Most existing human pose estimation (HPE) methods exploit multi-scale information by fusing feature maps of four different spatial sizes, \ie , , , and of th…
Graph-MLP: Node Classification without Message Passing in Graph
Yang Hu, Haoxuan You, Zhecan Wang +3
Graph Neural Network (GNN) has been demonstrated its effectiveness in dealing with non-Euclidean structural data. Both spatial-based and spectral-based GNNs are relying on adjacenc…
General Instance Distillation for Object Detection
Xing Dai, Zeren Jiang, Zhao Wu +4
In recent years, knowledge distillation has been proved to be an effective solution for model compression. This approach can make lightweight student models acquire the knowledge e…
V2F-Net: Explicit Decomposition of Occluded Pedestrian Detection
Mingyang Shang, Dawei Xiang, Zhicheng Wang +1
Occlusion is very challenging in pedestrian detection. In this paper, we propose a simple yet effective method named V2F-Net, which explicitly decomposes occluded pedestrian detect…
TokenPose: Learning Keypoint Tokens for Human Pose Estimation
Yanjie Li, Shoukui Zhang, Zhicheng Wang +4
Human pose estimation deeply relies on visual clues and anatomical constraints between parts to locate keypoints. Most existing CNN-based methods do well in visual representation,…
Efficient Human Pose Estimation by Learning Deeply Aggregated Representations
Zhengxiong Luo, Zhicheng Wang, Yuanhao Cai +6
In this paper, we propose an efficient human pose estimation network (DANet) by learning deeply aggregated representations. Most existing models explore multi-scale information mai…
Rethinking the Heatmap Regression for Bottom-up Human Pose Estimation
Zhengxiong Luo, Zhicheng Wang, Yan Huang +2
Heatmap regression has become the most prevalent choice for nowadays human pose estimation methods. The ground-truth heatmaps are usually constructed via covering all skeletal keyp…
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…
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 Delicate Local Representations for Multi-Person Pose Estimation
Yuanhao Cai, Zhicheng Wang, Zhengxiong Luo +7
In this paper, we propose a novel method called Residual Steps Network (RSN). RSN aggregates features with the same spatial size (Intra-level features) efficiently to obtain delica…
GridFace: Face Rectification via Learning Local Homography Transformations
Erjin Zhou, Zhimin Cao, Jian Sun
In this paper, we propose a method, called GridFace, to reduce facial geometric variations and improve the recognition performance. Our method rectifies the face by local homograph…
Coarse-to-fine Face Alignment with Multi-Scale Local Patch Regression
Zhiao Huang, Erjin Zhou, Zhimin Cao
Facial landmark localization plays an important role in face recognition and analysis applications. In this paper, we give a brief introduction to a coarse-to-fine pipeline with ne…
Naive-Deep Face Recognition: Touching the Limit of LFW Benchmark or Not?
Erjin Zhou, Zhimin Cao, Qi Yin
Face recognition performance improves rapidly with the recent deep learning technique developing and underlying large training dataset accumulating. In this paper, we report our ob…