Search Results for author: Zhongdao Wang

Found 12 papers, 5 papers with code

Adaptive Affinity for Associations in Multi-Target Multi-Camera Tracking

no code implementations14 Dec 2021 Yunzhong Hou, Zhongdao Wang, Shengjin Wang, Liang Zheng

In this paper, we design experiments to verify such misfit between global re-ID feature distances and local matching in tracking, and propose a simple yet effective approach to adapt affinity estimations to corresponding matching scopes in MTMCT.

Action Units That Constitute Trainable Micro-expressions (and A Large-scale Synthetic Dataset)

no code implementations3 Dec 2021 Yuchi Liu, Zhongdao Wang, Tom Gedeon, Liang Zheng

Because of the expensive data collection process, micro-expression (MiE) datasets are generally much smaller in scale than those in other computer vision fields, rendering large-scale training less feasible.

Face Generation Micro-Expression Recognition

Do Different Tracking Tasks Require Different Appearance Models?

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.

Multi-Object Tracking Multi-Object Tracking and Segmentation +10

Synthetic Data Are as Good as the Real for Association Knowledge Learning in Multi-object Tracking

no code implementations30 Jun 2021 Yuchi Liu, Zhongdao Wang, Xiangxin Zhou, Liang Zheng

We show that compared with real data, association knowledge obtained from synthetic data can achieve very similar performance on real-world test sets without domain adaption techniques.

Domain Adaptation Multi-Object Tracking

CycAs: Self-supervised Cycle Association for Learning Re-identifiable Descriptions

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.

Multi-Object Tracking Person Re-Identification +1

Circle Loss: A Unified Perspective of Pair Similarity Optimization

10 code implementations CVPR 2020 Yifan Sun, Changmao Cheng, Yuhan Zhang, Chi Zhang, Liang Zheng, Zhongdao Wang, Yichen Wei

This paper provides a pair similarity optimization viewpoint on deep feature learning, aiming to maximize the within-class similarity $s_p$ and minimize the between-class similarity $s_n$.

 Ranked #1 on Face Verification on IJB-C (dataset metric)

Face Recognition Face Verification +3

Locality Aware Appearance Metric for Multi-Target Multi-Camera Tracking

1 code implementation27 Nov 2019 Yunzhong Hou, Liang Zheng, Zhongdao Wang, Shengjin Wang

Due to the continuity of target trajectories, tracking systems usually restrict their data association within a local neighborhood.

Multi-Object Tracking

Towards Real-Time Multi-Object Tracking

11 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 #12 on Multi-Object Tracking on MOT16 (using extra training data)

Multiple Object Tracking Multi-Task Learning +1

Softmax Dissection: Towards Understanding Intra- and Inter-class Objective for Embedding Learning

no code implementations4 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.

Face Recognition Face Verification

Linkage Based Face Clustering via Graph Convolution Network

3 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.

Face Clustering Link Prediction

Query Adaptive Late Fusion for Image Retrieval

no code implementations31 Oct 2018 Zhongdao Wang, Liang Zheng, Shengjin Wang

That is to say, for some queries, a feature may be neither discriminative nor complementary to existing ones, while for other queries, the feature suffices.

Image Retrieval Person Recognition +1

Cannot find the paper you are looking for? You can Submit a new open access paper.