1 code implementation • 17 Apr 2022 • Xu Shen, Matthew Lacayo, Nidhir Guggilla, Francesco Borrelli
The problem of multimodal intent and trajectory prediction for human-driven vehicles in parking lots is addressed in this paper.
no code implementations • 19 Nov 2021 • Xin Jin, Tianyu He, Zhiheng Yin, Xu Shen, Tongliang Liu, Xinchao Wang, Jianqiang Huang, Xian-Sheng Hua, Zhibo Chen
Unsupervised Person Re-identification (U-ReID) with pseudo labeling recently reaches a competitive performance compared to fully-supervised ReID methods based on modern clustering algorithms.
no code implementations • 29 Sep 2021 • Xin Jin, Tianyu He, Xu Shen, Songhua Wu, Tongliang Liu, Xinchao Wang, Jianqiang Huang, Zhibo Chen, Xian-Sheng Hua
In this paper, we propose an embarrassing simple yet highly effective adversarial domain adaptation (ADA) method for effectively training models for alignment.
1 code implementation • CVPR 2021 • Zhen Huang, Xu Shen, Jun Xing, Tongliang Liu, Xinmei Tian, Houqiang Li, Bing Deng, Jianqiang Huang, Xian-Sheng Hua
The inheritance part is learned with a similarity loss to transfer the existing learned knowledge from the teacher model to the student model, while the exploration part is encouraged to learn representations different from the inherited ones with a dis-similarity loss.
no code implementations • CVPR 2021 • Tianyu He, Xu Shen, Jianqiang Huang, Zhibo Chen, Xian-Sheng Hua
Driven by the success of deep learning, the last decade has seen rapid advances in person re-identification (re-ID).
1 code implementation • 29 Mar 2021 • Xin Jin, Tianyu He, Kecheng Zheng, Zhiheng Yin, Xu Shen, Zhen Huang, Ruoyu Feng, Jianqiang Huang, Xian-Sheng Hua, Zhibo Chen
Specifically, we introduce Gait recognition as an auxiliary task to drive the Image ReID model to learn cloth-agnostic representations by leveraging personal unique and cloth-independent gait information, we name this framework as GI-ReID.
no code implementations • ICCV 2021 • Tianyu He, Xin Jin, Xu Shen, Jianqiang Huang, Zhibo Chen, Xian-Sheng Hua
The CNN encoder is responsible for efficiently extracting discriminative spatial features while the DI decoder is designed to densely model spatial-temporal inherent interaction across frames.
Ranked #1 on
Person Re-Identification
on DukeMTMC-reID
1 code implementation • ICCV 2021 • Shuxian Liang, Xu Shen, Jianqiang Huang, Xian-Sheng Hua
In this paper, we propose a novel solution for object-matching based semi-supervised video object segmentation, where the target object masks in the first frame are provided.
1 code implementation • ICCV 2021 • Zhen Huang, Dixiu Xue, Xu Shen, Xinmei Tian, Houqiang Li, Jianqiang Huang, Xian-Sheng Hua
Second, different body parts possess different scales, and even the same part in different frames can appear at different locations and scales.
1 code implementation • 26 Nov 2020 • Zhen Huang, Xu Shen, Xinmei Tian, Houqiang Li, Jianqiang Huang, Xian-Sheng Hua
The topology of the adjacency graph is a key factor for modeling the correlations of the input skeletons.
no code implementations • 21 Apr 2020 • Xu Shen, Ivo Batkovic, Vijay Govindarajan, Paolo Falcone, Trevor Darrell, Francesco Borrelli
We investigate the problem of predicting driver behavior in parking lots, an environment which is less structured than typical road networks and features complex, interactive maneuvers in a compact space.
1 code implementation • 28 Nov 2019 • Xu Shen, Xinmei Tian, Anfeng He, Shaoyan Sun, DaCheng Tao
In this paper, we propose randomly transforming (rotation, scale, and translation) feature maps of CNNs during the training stage.
1 code implementation • 28 Nov 2019 • Xu Shen, Xinmei Tian, Tongliang Liu, Fang Xu, DaCheng Tao
On the one hand, continuous dropout is considerably closer to the activation characteristics of neurons in the human brain than traditional binary dropout.
no code implementations • 28 Nov 2019 • Xu Shen, Xinmei Tian, Shaoyan Sun, DaCheng Tao
Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance on many visual recognition tasks.
1 code implementation • CVPR 2019 • Jiwei Yang, Xu Shen, Jun Xing, Xinmei Tian, Houqiang Li, Bing Deng, Jianqiang Huang, Xian-Sheng Hua
The proposed quantization function can be learned in a lossless and end-to-end manner and works for any weights and activations of neural networks in a simple and uniform way.