Learn to Match: Automatic Matching Network Design for Visual Tracking-Reference-Cited by-同舟云学术

Learn to Match: Automatic Matching Network Design for Visual Tracking

Published:2021-10 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE/CVF International Conference on Computer Vision (ICCV)
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Author:

Zhang Zhipeng¹,Liu Yihao²,Wang Xiao³,Li Bing¹,Hu Weiming¹

Affiliation:

1. Chinese Academy of Sciences,National Laboratory of Pattern Recognition, Institute of Automation

2. University of Chinese Academy of Sciences,School of AI

3. Peng Cheng Laboratory

Funder

National Key Research and Development Program of China

China Postdoctoral Science Foundation

Youth Innovation Promotion Association

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9709627/9709628/09710403.pdf?arnumber=9710403

Reference47 articles.

1. Towards More Flexible and Accurate Object Tracking with Natural Language: Algorithms and Benchmark

2. Tracking by Instance Detection: A Meta-Learning Approach

3. Afshin Dehghan, and Mubarak Shah. Visual tracking: An experimental survey;smeulders;IEEE Transactions on Pattern Analysis and Machine Intelligence,2013

4. ImageNet Large Scale Visual Recognition Challenge

5. YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video

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