Large Loss Matters in Weakly Supervised Multi-Label Classification-Reference-Cited by-同舟云学术

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Large Loss Matters in Weakly Supervised Multi-Label Classification

Published:2022-06 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
language:
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Author:

Kim Youngwook¹,Kim Jae Myung²,Akata Zeynep²,Lee Jungwoo¹

Affiliation:

1. Seoul National University

2. University of Tübingen

Funder

National Research Foundation of Korea

Institute of Information & communications Technology Planning & Evaluation (IITP)

DFG

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9878378/9878366/09879685.pdf?arnumber=9879685

Reference58 articles.

1. Joint Optimization Framework for Learning with Noisy Labels

2. Multilabel Classification with Principal Label Space Transformation

3. Evaluating machine accuracy on imagenet;shankar;ICML,0

4. Asymmetric Loss For Multi-Label Classification

5. Classifier chains for multi-label classification

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