no code implementations • 2 Apr 2022 • Ya-Li Li, Shengjin Wang
In this paper, we propose a novel approach to combine decision trees and deep neural networks in an end-to-end learning manner for object detection.
1 code implementation • 28 Dec 2021 • Jian Han, Ya-Li Li, Shengjin Wang
With the uncertainty-guided alternative optimization, we balance between the exploration of target domain data and the negative effects of noisy labeling.
Domain Adaptive Person Re-Identification Person Re-Identification
1 code implementation • NeurIPS 2021 • Zhongdao Wang, Hengshuang Zhao, Ya-Li Li, Shengjin Wang, Philip H. S. Torr, Luca Bertinetto
We show how most tracking tasks can be solved within this framework, and that the same appearance model can be successfully used to obtain results that are competitive against specialised methods for most of the tasks considered.
Ranked #2 on Video Object Segmentation on DAVIS 2017 (mIoU metric)
Multi-Object Tracking Multi-Object Tracking and Segmentation +10
1 code implementation • CVPR 2020 • Takashi Isobe, Songjiang Li, Xu Jia, Shanxin Yuan, Gregory Slabaugh, Chunjing Xu, Ya-Li Li, Shengjin Wang, Qi Tian
Video super-resolution, which aims at producing a high-resolution video from its corresponding low-resolution version, has recently drawn increasing attention.
no code implementations • ECCV 2020 • Zhongdao Wang, Jingwei Zhang, Liang Zheng, Yixuan Liu, Yifan Sun, Ya-Li Li, Shengjin Wang
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 those from video tracklets or clustering.
12 code implementations • ECCV 2020 • Zhongdao Wang, Liang Zheng, Yixuan Liu, Ya-Li Li, Shengjin Wang
In this paper, we propose an MOT system that allows target detection and appearance embedding to be learned in a shared model.
Ranked #4 on Multi-Object Tracking on HiEve
no code implementations • 4 Aug 2019 • Lanqing He, Zhongdao Wang, Ya-Li Li, Shengjin Wang
The softmax loss and its variants are widely used as objectives for embedding learning, especially in applications like face recognition.
no code implementations • 30 May 2019 • Ye Guo, Ya-Li Li, Shengjin Wang
Generic object detection is one of the most fundamental problems in computer vision, yet it is difficult to provide all the bounding-box-level annotations aiming at large-scale object detection for thousands of categories.
no code implementations • 5 May 2019 • Takashi Isobe, Jian Han, Fang Zhu, Ya-Li Li, Shengjin Wang
Video-based person re-identification has drawn massive attention in recent years due to its extensive applications in video surveillance.
no code implementations • 25 Apr 2019 • Ya-Li Li, Shengjin Wang
First, we present the modules of spatial attention, channel attention and aligned attention for single-stage object detection.
1 code implementation • CVPR 2019 • Yifan Sun, Qin Xu, Ya-Li Li, Chi Zhang, Yikang Li, Shengjin Wang, Jian Sun
The visibility awareness allows VPM to extract region-level features and compare two images with focus on their shared regions (which are visible on both images).
Ranked #14 on Person Re-Identification on Market-1501-C
4 code implementations • CVPR 2019 • Zhongdao Wang, Liang Zheng, Ya-Li Li, Shengjin Wang
The key idea is that we find the local context in the feature space around an instance (face) contains rich information about the linkage relationship between this instance and its neighbors.
no code implementations • CVPR 2019 • Yue Zheng, Ya-Li Li, Shengjin Wang
In this paper, we propose a novel approach for generating image captions with guiding objects (CGO).
no code implementations • 27 Nov 2017 • Lingxiao Wang, Ya-Li Li, Shengjin Wang
Comprehensive experiments demonstrate that our proposed method can handle various blur kenels and achieve state-of-the-art results for small size blurry face images restoration.
1 code implementation • 12 Oct 2017 • Dong Li, Jia-Bin Huang, Ya-Li Li, Shengjin Wang, Ming-Hsuan Yang
In classification adaptation, we transfer a pre-trained network to a multi-label classification task for recognizing the presence of a certain object in an image.
no code implementations • CVPR 2016 • Dong Li, Jia-Bin Huang, Ya-Li Li, Shengjin Wang, Ming-Hsuan Yang
In this paper, we address this problem by progressive domain adaptation with two main steps: classification adaptation and detection adaptation.