no code implementations • 29 Mar 2023 • Xuechao Zou, Kai Li, Junliang Xing, Pin Tao, Yachao Cui
The cloud detection backbone uses cloud masks to reinforce cloudy areas to prompt the cloud removal module.
no code implementations • 17 Mar 2023 • Haozhe Wu, Jia Jia, Junliang Xing, Hongwei Xu, Xiangyuan Wang, Jelo Wang
Audio-Driven Face Animation is an eagerly anticipated technique for applications such as VR/AR, games, and movie making.
no code implementations • 14 Oct 2022 • Ziqi Gao, Jianguo Chen, Junliang Xing, Shwetak Patel, Yuanchun Shi, Xin Liu, Yuntao Wang
In this paper, we propose a new novel multimodal neural architecture based on RGB and IMU wearable sensors (e. g., accelerometer, gyroscope) for human activity recognition called Multimodal Temporal Segment Attention Network (MMTSA).
no code implementations • 7 Nov 2021 • Pengfei Zhang, Cuiling Lan, Wenjun Zeng, Junliang Xing, Jianru Xue, Nanning Zheng
Skeleton data is of low dimension.
no code implementations • ICLR 2022 • Haobo Fu, Weiming Liu, Shuang Wu, Yijia Wang, Tao Yang, Kai Li, Junliang Xing, Bin Li, Bo Ma, Qiang Fu, Yang Wei
The deep policy gradient method has demonstrated promising results in many large-scale games, where the agent learns purely from its own experience.
no code implementations • 8 Sep 2021 • Yekun Chai, Shuo Jin, Junliang Xing
Automatically translating images to texts involves image scene understanding and language modeling.
Ranked #27 on Image Captioning on COCO Captions
1 code implementation • NeurIPS 2021 • Yifan Zang, Jinmin He, Kai Li, Lily Cao, Haobo Fu, Qiang Fu, Junliang Xing
In this paper, we propose a cooperative MARL method with sequential credit assignment (SeCA) that deduces each agent's contribution to the team's success one by one to learn better cooperation.
Multi-agent Reinforcement Learning reinforcement-learning +3
no code implementations • 13 Apr 2021 • Ajian Liu, Chenxu Zhao, Zitong Yu, Jun Wan, Anyang Su, Xing Liu, Zichang Tan, Sergio Escalera, Junliang Xing, Yanyan Liang, Guodong Guo, Zhen Lei, Stan Z. Li, Du Zhang
To bridge the gap to real-world applications, we introduce a largescale High-Fidelity Mask dataset, namely CASIA-SURF HiFiMask (briefly HiFiMask).
no code implementations • 18 Feb 2021 • Zhe Wu, Kai Li, Enmin Zhao, Hang Xu, Meng Zhang, Haobo Fu, Bo An, Junliang Xing
In this work, we propose a novel Learning to Exploit (L2E) framework for implicit opponent modeling.
1 code implementation • 21 Jan 2021 • Nan Jiang, Kuiran Wang, Xiaoke Peng, Xuehui Yu, Qiang Wang, Junliang Xing, Guorong Li, Jian Zhao, Guodong Guo, Zhenjun Han
The releasing of such a large-scale dataset could be a useful initial step in research of tracking UAVs.
no code implementations • 1 Jan 2021 • Enmin Zhao, Kai Li, Junliang Xing
Regret matching (RM) plays a crucial role in CFR and its variants to approach Nash equilibrium.
no code implementations • 11 Dec 2020 • Kai Li, Hang Xu, Enmin Zhao, Zhe Wu, Junliang Xing
Owning to the unremitting efforts by a few institutes, significant progress has recently been made in designing superhuman AIs in No-limit Texas Hold'em (NLTH), the primary testbed for large-scale imperfect-information game research.
no code implementations • 12 Nov 2020 • Jiangtao Kong, Yu Cheng, Benjia Zhou, Kai Li, Junliang Xing
To obtain a high-performance vehicle ReID model, we present a novel Distance Shrinking with Angular Marginalizing (DSAM) loss function to perform hybrid learning in both the Original Feature Space (OFS) and the Feature Angular Space (FAS) using the local verification and the global identification information.
1 code implementation • 11 Dec 2019 • Shaoru Wang, Yongchao Gong, Junliang Xing, Lichao Huang, Chang Huang, Weiming Hu
To reciprocate these two tasks, we design a two-stream structure to learn features on both the object level (i. e., bounding boxes) and the pixel level (i. e., instance masks) jointly.
Ranked #90 on Instance Segmentation on COCO test-dev
no code implementations • 24 Sep 2019 • Cheng Chi, Shifeng Zhang, Junliang Xing, Zhen Lei, Stan Z. Li, Xudong Zou
Head and human detection have been rapidly improved with the development of deep convolutional neural networks.
no code implementations • 15 Sep 2019 • Cheng Chi, Shifeng Zhang, Junliang Xing, Zhen Lei, Stan Z. Li, Xudong Zou
Pedestrian detection in crowded scenes is a challenging problem, because occlusion happens frequently among different pedestrians.
1 code implementation • 26 May 2019 • Hanyang Kong, Jian Zhao, Xiaoguang Tu, Junliang Xing, ShengMei Shen, Jiashi Feng
Recent deep learning based face recognition methods have achieved great performance, but it still remains challenging to recognize very low-resolution query face like 28x28 pixels when CCTV camera is far from the captured subject.
no code implementations • 25 May 2019 • Yangru Huang, Peixi Peng, Yi Jin, Junliang Xing, Congyan Lang, Songhe Feng
To reduce domain divergence caused by that the source and target datasets are collected from different environments, we force to project the DSH feature maps from different domains to a new nominal domain, and a novel domain similarity loss is proposed based on one-class classification.
2 code implementations • CVPR 2020 • Pengfei Zhang, Cuiling Lan, Wen-Jun Zeng, Junliang Xing, Jianru Xue, Nanning Zheng
Skeleton-based human action recognition has attracted great interest thanks to the easy accessibility of the human skeleton data.
Ranked #1 on Skeleton Based Action Recognition on SYSU 3D
no code implementations • 13 Feb 2019 • Jian Zhao, Jianshu Li, Xiaoguang Tu, Fang Zhao, Yuan Xin, Junliang Xing, Hengzhu Liu, Shuicheng Yan, Jiashi Feng
In this paper, we study the challenging unconstrained set-based face recognition problem where each subject face is instantiated by a set of media (images and videos) instead of a single image.
9 code implementations • CVPR 2019 • Bo Li, Wei Wu, Qiang Wang, Fangyi Zhang, Junliang Xing, Junjie Yan
Moreover, we propose a new model architecture to perform depth-wise and layer-wise aggregations, which not only further improves the accuracy but also reduces the model size.
Ranked #4 on Visual Object Tracking on VOT2017/18
no code implementations • 5 Dec 2018 • Peixi Peng, Junliang Xing
To learn the multi-agent cooperation effectively and tackle the sub-optimality of demonstration, a self-improving learning method is proposed: On the one hand, the centralized state-action values are initialized by the demonstration and updated by the learned decentralized policy to improve the sub-optimality.
no code implementations • 19 Nov 2018 • Yunxiao Qin, Chenxu Zhao, Zezheng Wang, Junliang Xing, Jun Wan, Zhen Lei
The method RAML aims to give the Meta learner the ability of leveraging the past learned knowledge to reduce the dimension of the original input data by expressing it into high representations, and help the Meta learner to perform well.
no code implementations • 11 Sep 2018 • Xiaolin Song, Cuiling Lan, Wen-Jun Zeng, Junliang Xing, Jingyu Yang, Xiaoyan Sun
We propose a video level 2D feature representation by transforming the convolutional features of all frames to a 2D feature map, referred to as VideoMap.
Ranked #47 on Action Recognition on UCF101
3 code implementations • 7 Sep 2018 • Cheng Chi, Shifeng Zhang, Junliang Xing, Zhen Lei, Stan Z. Li, Xudong Zou
In particular, the SRN consists of two modules: the Selective Two-step Classification (STC) module and the Selective Two-step Regression (STR) module.
Ranked #1 on Face Detection on PASCAL Face
1 code implementation • 2 Sep 2018 • Jian Zhao, Yu Cheng, Yi Cheng, Yang Yang, Haochong Lan, Fang Zhao, Lin Xiong, Yan Xu, Jianshu Li, Sugiri Pranata, ShengMei Shen, Junliang Xing, Hengzhu Liu, Shuicheng Yan, Jiashi Feng
Benchmarking our model on one of the most popular unconstrained face recognition datasets IJB-C additionally verifies the promising generalizability of AIM in recognizing faces in the wild.
Ranked #1 on Age-Invariant Face Recognition on MORPH Album2
no code implementations • ECCV 2018 • Mengdan Zhang, Qiang Wang, Junliang Xing, Jin Gao, Peixi Peng, Weiming Hu, Steve Maybank
Correlation filters based trackers rely on a periodic assumption of the search sample to efficiently distinguish the target from the background.
no code implementations • ECCV 2018 • Xuecheng Nie, Jiashi Feng, Junliang Xing, Shuicheng Yan
This paper proposes a novel Pose Partition Network (PPN) to address the challenging multi-person pose estimation problem.
no code implementations • CVPR 2018 • Kai Li, Junliang Xing, Chi Su, Weiming Hu, Yundong Zhang, Stephen Maybank
First, a novel cost-sensitive multi-task loss function is designed to learn transferable aging features by training on the source population.
no code implementations • CVPR 2018 • Jian Zhao, Yu Cheng, Yan Xu, Lin Xiong, Jianshu Li, Fang Zhao, Karlekar Jayashree, Sugiri Pranata, ShengMei Shen, Junliang Xing, Shuicheng Yan, Jiashi Feng
To this end, we propose a Pose Invariant Model (PIM) for face recognition in the wild, with three distinct novelties.
2 code implementations • CVPR 2018 • Qiang Wang, Zhu Teng, Junliang Xing, Jin Gao, Weiming Hu, Stephen Maybank
The RASNet model reformulates the correlation filter within a Siamese tracking framework, and introduces different kinds of the attention mechanisms to adapt the model without updating the model online.
Ranked #3 on Visual Object Tracking on OTB-2013
2 code implementations • 20 Apr 2018 • Pengfei Zhang, Cuiling Lan, Junliang Xing, Wen-Jun Zeng, Jianru Xue, Nanning Zheng
In order to alleviate the effects of view variations, this paper introduces a novel view adaptation scheme, which automatically determines the virtual observation viewpoints in a learning based data driven manner.
Ranked #1 on Skeleton Based Action Recognition on UWA3D
no code implementations • ICCV 2017 • Zhu Teng, Junliang Xing, Qiang Wang, Congyan Lang, Songhe Feng, Yi Jin
Our deep architecture contains three networks, a Feature Net, a Temporal Net, and a Spatial Net.
no code implementations • ICCV 2017 • Chi Su, Jianing Li, Shiliang Zhang, Junliang Xing, Wen Gao, Qi Tian
Our deep architecture explicitly leverages the human part cues to alleviate the pose variations and learn robust feature representations from both the global image and different local parts.
Ranked #94 on Person Re-Identification on Market-1501
no code implementations • ICCV 2017 • Ke Sun, Cuiling Lan, Junliang Xing, Wen-Jun Zeng, Dong Liu, Jingdong Wang
We present a two-stage normalization scheme, human body normalization and limb normalization, to make the distribution of the relative joint locations compact, resulting in easier learning of convolutional spatial models and more accurate pose estimation.
1 code implementation • CVPR 2017 • Jiangjing Lv, Xiaohu Shao, Junliang Xing, Cheng Cheng, Xi Zhou
At the global stage, given an image with a rough face detection result, the full face region is firstly re-initialized by a supervised spatial transformer network to a canonical shape state and then trained to regress a coarse landmark estimation.
1 code implementation • 21 May 2017 • Xuecheng Nie, Jiashi Feng, Junliang Xing, Shuicheng Yan
This paper proposes a new Generative Partition Network (GPN) to address the challenging multi-person pose estimation problem.
Ranked #1 on Multi-Person Pose Estimation on WAF (AP metric)
5 code implementations • 13 Apr 2017 • Qiang Wang, Jin Gao, Junliang Xing, Mengdan Zhang, Weiming Hu
In this work, we present an end-to-end lightweight network architecture, namely DCFNet, to learn the convolutional features and perform the correlation tracking process simultaneously.
1 code implementation • ICCV 2017 • Pengfei Zhang, Cuiling Lan, Junliang Xing, Wen-Jun Zeng, Jianru Xue, Nanning Zheng
Rather than re-positioning the skeletons based on a human defined prior criterion, we design a view adaptive recurrent neural network (RNN) with LSTM architecture, which enables the network itself to adapt to the most suitable observation viewpoints from end to end.
Ranked #6 on Skeleton Based Action Recognition on SYSU 3D
no code implementations • 14 Feb 2017 • Shan Gao, Xiaogang Chen, Qixiang Ye, Junliang Xing, Arjan Kuijper, Xiangyang Ji
Inspired with the social affinity property of moving objects, we propose a Graphical Social Topology (GST) model, which estimates the group dynamics by jointly modeling the group structure and the states of objects using a topological representation.
no code implementations • 18 Nov 2016 • Sijie Song, Cuiling Lan, Junliang Xing, Wen-Jun Zeng, Jiaying Liu
In this work, we propose an end-to-end spatial and temporal attention model for human action recognition from skeleton data.
Ranked #88 on Skeleton Based Action Recognition on NTU RGB+D
no code implementations • CVPR 2016 • Xinchu Shi, Haibin Ling, Weiming Hu, Junliang Xing, Yanning Zhang
Due to its wide range of applications, matching between two graphs has been extensively studied and remains an active topic.
no code implementations • 11 May 2016 • Chi Su, Shiliang Zhang, Junliang Xing, Wen Gao, Qi Tian
And we propose a semi-supervised attribute learning framework which progressively boosts the accuracy of attributes only using a limited number of labeled data.
1 code implementation • 19 Apr 2016 • Yanghao Li, Cuiling Lan, Junliang Xing, Wen-Jun Zeng, Chunfeng Yuan, Jiaying Liu
In this paper, we study the problem of online action detection from streaming skeleton data.
no code implementations • 24 Mar 2016 • Wentao Zhu, Cuiling Lan, Junliang Xing, Wen-Jun Zeng, Yanghao Li, Li Shen, Xiaohui Xie
Skeleton based action recognition distinguishes human actions using the trajectories of skeleton joints, which provide a very good representation for describing actions.
no code implementations • ICCV 2015 • Lin Ma, Xiaoqin Zhang, Weiming Hu, Junliang Xing, Jiwen Lu, Jie zhou
To address this, this paper presents a local subspace collaborative tracking method for robust visual tracking, where multiple linear and nonlinear subspaces are learned to better model the nonlinear relationship of object appearances.
no code implementations • CVPR 2015 • Shaoxin Li, Junliang Xing, Zhiheng Niu, Shiguang Shan, Shuicheng Yan
Comprehensive experiments on WebFace, Morph II and MultiPIE databases well validate the effectiveness of the proposed kernel adaptation method and tree-structured convolutional architecture for facial traits recognition tasks, including identity, age and gender classification.
no code implementations • 26 Sep 2014 • Wenhan Luo, Junliang Xing, Anton Milan, Xiaoqin Zhang, Wei Liu, Tae-Kyun Kim
We inspect the recent advances in various aspects and propose some interesting directions for future research.
no code implementations • CVPR 2014 • Junliang Xing, Zhiheng Niu, Junshi Huang, Weiming Hu, Shuicheng Yan
During each training stage, the SRD model learns a relational dictionary to capture consistent relationships between face appearance and shape, which are respectively modeled by the pose-indexed image features and the shape displacements for current estimated landmarks.
no code implementations • CVPR 2014 • Xinchu Shi, Haibin Ling, Weiming Hu, Chunfeng Yuan, Junliang Xing
In this paper, we model interactions between neighbor targets by pair-wise motion context, and further encode such context into the global association optimization.