Class-level Structural Relation Modeling and Smoothing for Visual Representation Learning-Reference-Cited by-同舟云学术

Class-level Structural Relation Modeling and Smoothing for Visual Representation Learning

Published:2023-10-26 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 31st ACM International Conference on Multimedia
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Author:

Chen Zitan¹^ORCID,Qi Zhuang¹^ORCID,Cao Xiao²^ORCID,Li Xiangxian¹^ORCID,Meng Xiangxu¹^ORCID,Meng Lei³^ORCID

Affiliation:

1. Shandong University, Jinan, China

2. National University of Singapore, Singapore, Singapore

3. Shandong University & Shandong Research Institute of Industrial Technology, Jinan, China

Funder

TaiShan Scholars Program

Excellent Youth Scholars Program of Shandong Province

National Natural Science Foundation of China

National Key R\&D Program of China

Oversea Innovation Team Project of the 20 Regulations for New Universities funding program of Jinan

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3581783.3612511

Reference61 articles.

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2. Mathilde Caron et al. 2020. Unsupervised learning of visual features by contrasting cluster assignments. NeurIPS (2020). Mathilde Caron et al. 2020. Unsupervised learning of visual features by contrasting cluster assignments. NeurIPS (2020).

3. Jingjing Chen and Chong-Wah Ngo. 2016. Deep-based ingredient recognition for cooking recipe retrieval. In ACM MM. Jingjing Chen and Chong-Wah Ngo. 2016. Deep-based ingredient recognition for cooking recipe retrieval. In ACM MM.

4. Zhao-Min Chen Xiu-Shen Wei etal 2019. Multi-Label Image Recognition with Joint Class-Aware Map Disentangling and Label Correlation Embedding. In IEEE. Zhao-Min Chen Xiu-Shen Wei et al. 2019. Multi-Label Image Recognition with Joint Class-Aware Map Disentangling and Label Correlation Embedding. In IEEE.

5. Gong Cheng Pujian Lai etal 2023. Class attention network for image recognition. Science China Information Sciences (2023). Gong Cheng Pujian Lai et al. 2023. Class attention network for image recognition. Science China Information Sciences (2023).

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