no code implementations • ICCV 2021 • Zheng Zhu, Xianda Guo, Tian Yang, JunJie Huang, Jiankang Deng, Guan Huang, Dalong Du, Jiwen Lu, Jie zhou
In this paper, we contribute a new benchmark for Gait REcognition in the Wild (GREW).
no code implementations • 21 Apr 2022 • Zheng Zhu, Guan Huang, Jiankang Deng, Yun Ye, JunJie Huang, Xinze Chen, Jiagang Zhu, Tian Yang, Dalong Du, Jiwen Lu, Jie zhou
For a comprehensive evaluation of face matchers, three recognition tasks are performed under standard, masked and unbiased settings, respectively.
1 code implementation • 15 Apr 2022 • XiaoFeng Wang, Zheng Zhu, Fangbo Qin, Yun Ye, Guan Huang, Xu Chi, Yijia He, Xingang Wang
Therefore, we present MVSTER, which leverages the proposed epipolar Transformer to learn both 2D semantics and 3D spatial associations efficiently.
1 code implementation • 11 Apr 2022 • Jiayu Zou, Junrui Xiao, Zheng Zhu, JunJie Huang, Guan Huang, Dalong Du, Xingang Wang
In order to reap the benefits and avoid the drawbacks of CBFT and CFFT, we propose a novel framework with a Hybrid Feature Transformation module (HFT).
1 code implementation • 7 Apr 2022 • Yi Wei, Linqing Zhao, Wenzhao Zheng, Zheng Zhu, Yongming Rao, Guan Huang, Jiwen Lu, Jie zhou
In this paper, we propose a SurroundDepth method to incorporate the information from multiple surrounding views to predict depth maps across cameras.
1 code implementation • 31 Mar 2022 • JunJie Huang, Guan Huang
Single frame data contains finite information which limits the performance of the existing vision-based multi-camera 3D object detection paradigms.
Ranked #78 on
3D Object Detection
on nuScenes
no code implementations • 18 Mar 2022 • Yuebing Liang, Guan Huang, Zhan Zhao
Bike sharing is an increasingly popular part of urban transportation systems.
1 code implementation • 3 Mar 2022 • Kai Wang, Bo Zhao, Xiangyu Peng, Zheng Zhu, Shuo Yang, Shuo Wang, Guan Huang, Hakan Bilen, Xinchao Wang, Yang You
Dataset condensation aims at reducing the network training effort through condensing a cumbersome training set into a compact synthetic one.
1 code implementation • 22 Dec 2021 • JunJie Huang, Guan Huang, Zheng Zhu, Yun Ye, Dalong Du
As a fast version, BEVDet-Tiny scores 31. 2% mAP and 39. 2% NDS on the nuScenes val set.
no code implementations • 15 Dec 2021 • Yuebing Liang, Guan Huang, Zhan Zhao
Despite some recent efforts, existing approaches to multimodal demand prediction are generally not flexible enough to account for multiplex networks with diverse spatial units and heterogeneous spatiotemporal correlations across different modes.
1 code implementation • 2 Dec 2021 • Yongming Rao, Wenliang Zhao, Guangyi Chen, Yansong Tang, Zheng Zhu, Guan Huang, Jie zhou, Jiwen Lu
In this work, we present a new framework for dense prediction by implicitly and explicitly leveraging the pre-trained knowledge from CLIP.
no code implementations • 27 Oct 2021 • Guan Huang, Son N. Tran, Quan Bai, Jane Alty
We have implemented a hand gesture detector to detect the gestures in the hand movement tests and our detection mAP is 0. 782 which is better than the state-of-the-art.
no code implementations • 10 Sep 2021 • Yunze Chen, JunJie Huang, Jiagang Zhu, Zheng Zhu, Tian Yang, Guan Huang, Dalong Du
The current research on this problem mainly focuses on designing an efficient Fully-connected layer (FC) to reduce GPU memory consumption caused by a large number of identities.
no code implementations • 16 Aug 2021 • Zheng Zhu, Guan Huang, Jiankang Deng, Yun Ye, JunJie Huang, Xinze Chen, Jiagang Zhu, Tian Yang, Jia Guo, Jiwen Lu, Dalong Du, Jie zhou
There are second phase of the challenge till October 1, 2021 and on-going leaderboard.
1 code implementation • CVPR 2021 • Shuai Shen, Wanhua Li, Zheng Zhu, Guan Huang, Dalong Du, Jiwen Lu, Jie zhou
To address the dilemma of large-scale training and efficient inference, we propose the STructure-AwaRe Face Clustering (STAR-FC) method.
no code implementations • 7 Jun 2021 • Yiding Liu, Guan Huang, Jiaxiang Liu, Weixue Lu, Suqi Cheng, Yukun Li, Daiting Shi, Shuaiqiang Wang, Zhicong Cheng, Dawei Yin
More importantly, we present a practical system workflow for deploying the model in web-scale retrieval.
no code implementations • 6 Apr 2021 • Jiabin Zhang, Zheng Zhu, Jiwen Lu, JunJie Huang, Guan Huang, Jie zhou
To make a better trade-off between accuracy and efficiency, we propose a novel multi-person pose estimation framework, SIngle-network with Mimicking and Point Learning for Bottom-up Human Pose Estimation (SIMPLE).
no code implementations • 24 Mar 2021 • Shuai Shen, Wanhua Li, Zheng Zhu, Guan Huang, Dalong Du, Jiwen Lu, Jie zhou
To address the dilemma of large-scale training and efficient inference, we propose the STructure-AwaRe Face Clustering (STAR-FC) method.
no code implementations • CVPR 2021 • Zheng Zhu, Guan Huang, Jiankang Deng, Yun Ye, JunJie Huang, Xinze Chen, Jiagang Zhu, Tian Yang, Jiwen Lu, Dalong Du, Jie zhou
In this paper, we contribute a new million-scale face benchmark containing noisy 4M identities/260M faces (WebFace260M) and cleaned 2M identities/42M faces (WebFace42M) training data, as well as an elaborately designed time-constrained evaluation protocol.
Ranked #1 on
Face Verification
on IJB-C
(dataset metric)
2 code implementations • 17 Aug 2020 • Junjie Huang, Zheng Zhu, Guan Huang, Dalong Du
As AID successfully pushes the performance boundary of human pose estimation problem by considerable margin and sets a new state-of-the-art, we hope AID to be a regular configuration for training human pose estimators.
Ranked #1 on
Multi-Person Pose Estimation
on COCO minival
2 code implementations • CVPR 2020 • Junjie Huang, Zheng Zhu, Feng Guo, Guan Huang, Dalong Du
Specifically, by investigating the standard data processing in state-of-the-art approaches mainly including coordinate system transformation and keypoint format transformation (i. e., encoding and decoding), we find that the results obtained by common flipping strategy are unaligned with the original ones in inference.
Ranked #10 on
Pose Estimation
on COCO test-dev
no code implementations • 14 Oct 2019 • Junjie Huang, Zheng Zhu, Guan Huang
Human pose estimation are of importance for visual understanding tasks such as action recognition and human-computer interaction.
no code implementations • 26 Aug 2019 • Zheng Zhu, Wei Zou, Guan Huang, Dalong Du, Chang Huang
In this paper, we propose an end-to-end framework to learn the convolutional features and perform the tracking process simultaneously, namely, a unified convolutional tracker (UCT).
no code implementations • 20 Aug 2019 • Zewen He, He Huang, Yudong Wu, Guan Huang, Wensheng Zhang
Scale variation remains a challenging problem for object detection.
no code implementations • 15 Aug 2019 • Jiabin Zhang, Zheng Zhu, Wei Zou, Peng Li, Yanwei Li, Hu Su, Guan Huang
Given the results of MTN, we adopt an occlusion-aware Re-ID feature strategy in the pose tracking module, where pose information is utilized to infer the occlusion state to make better use of Re-ID feature.
no code implementations • 4 Jun 2019 • Rui Zhang, Zheng Zhu, Peng Li, Rui Wu, Chaoxu Guo, Guan Huang, Hailun Xia
Human pose estimation has witnessed a significant advance thanks to the development of deep learning.
no code implementations • 4 Jun 2019 • Peng Li, Jiabin Zhang, Zheng Zhu, Yanwei Li, Lu Jiang, Guan Huang
Multi-target Multi-camera Tracking (MTMCT) aims to extract the trajectories from videos captured by a set of cameras.
3 code implementations • 29 Dec 2018 • Houjing Huang, Wenjie Yang, Xiaotang Chen, Xin Zhao, Kaiqi Huang, Jinbin Lin, Guan Huang, Dalong Du
Person re-identification (ReID) has achieved significant improvement under the single-domain setting.
no code implementations • 14 Dec 2018 • Jiagang Zhu, Wei Zou, Liang Xu, Yiming Hu, Zheng Zhu, Manyu Chang, Jun-Jie Huang, Guan Huang, Dalong Du
On NTU RGB-D, Action Machine achieves the state-of-the-art performance with top-1 accuracies of 97. 2% and 94. 3% on cross-view and cross-subject respectively.
Ranked #1 on
Action Recognition
on UTD-MHAD
no code implementations • CVPR 2019 • Yanwei Li, Xinze Chen, Zheng Zhu, Lingxi Xie, Guan Huang, Dalong Du, Xingang Wang
This paper studies panoptic segmentation, a recently proposed task which segments foreground (FG) objects at the instance level as well as background (BG) contents at the semantic level.
Ranked #16 on
Panoptic Segmentation
on Cityscapes val
4 code implementations • 21 Apr 2018 • Xiao Ma, Liqin Zhao, Guan Huang, Zhi Wang, Zelin Hu, Xiaoqiang Zhu, Kun Gai
To the best of our knowledge, this is the first public dataset which contains samples with sequential dependence of click and conversion labels for CVR modeling.
no code implementations • 10 Nov 2017 • Zheng Zhu, Guan Huang, Wei Zou, Dalong Du, Chang Huang
Convolutional neural networks (CNN) based tracking approaches have shown favorable performance in recent benchmarks.
no code implementations • 30 Jul 2015 • Shuangyin Li, Jiefei Li, Guan Huang, Ruiyang Tan, Rong Pan
We propose a novel method to model the SSDs by a so-called Tag-Weighted Topic Model (TWTM).