Heterogeneous Diversity Driven Active Learning for Multi-Object Tracking-Reference-Cited by-同舟云学术

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Heterogeneous Diversity Driven Active Learning for Multi-Object Tracking

Published:2023-10-01 Issue: Volume: Page:9898-9907
ISSN:
Container-title:2023 IEEE/CVF International Conference on Computer Vision (ICCV)
language:
Short-container-title:

Author:

Li Rui¹,Zhang Baopeng¹,Liu Jun²,Liu Wei¹,Zhao Jian³,Teng Zhu¹

Affiliation:

1. Beijing Jiaotong University

2. Singapore University of Technology and Design

3. Institute of North Electronic Equipment

Funder

Fundamental Research Funds for the Central Universities

Beijing Municipal Natural Science Foundation

China Association for Science and Technology

Beijing Association for Science and Technology

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10376473/10376477/10377667.pdf?arnumber=10377667

Reference53 articles.

1. Contextual Diversity for Active Learning

2. Self-supervised multi-object tracking with cross-input consistency;Bastani;NeurIPS,2021

3. The Power of Ensembles for Active Learning in Image Classification

4. Tracking Without Bells and Whistles

5. Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics

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