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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 2: AAAI-22 Technical Tracks 2

Self-Supervised Object Localization with Joint Graph Partition

February 1, 2023

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Authors

Yukun Su

School of Software and Engineering, South China University of Technology Nanyang Technological University


Guosheng Lin

Nanyang Technological University


Yun Hao

School of Software and Engineering, South China University of Technology Key Laboratory of Big Data and Intelligent Robot, Ministry of Education


Yiwen Cao

School of Software and Engineering, South China University of Technology Key Laboratory of Big Data and Intelligent Robot, Ministry of Education


Wenjun Wang

School of Software and Engineering, South China University of Technology Key Laboratory of Big Data and Intelligent Robot, Ministry of Education


Qingyao Wu

School of Software and Engineering, South China University of Technology Pazhou Lab, Guangzhou, China


DOI:

10.1609/aaai.v36i2.20127


Abstract:

Object localization aims to generate a tight bounding box for the target object, which is a challenging problem that has been deeply studied in recent years. Since collecting bounding-box labels is time-consuming and laborious, many researchers focus on weakly supervised object localization (WSOL). As the recent appealing self-supervised learning technique shows its powerful function in visual tasks, in this paper, we take the early attempt to explore unsupervised object localization by self-supervision. Specifically, we adopt different geometric transformations to image and utilize their parameters as pseudo labels for self-supervised learning. Then, the class-agnostic activation map (CAAM) is used to highlight the target object potential regions. However, such attention maps merely focus on the most discriminative part of the objects, which will affect the quality of the predicted bounding box. Based on the motivation that the activation maps of different transformations of the same image should be equivariant, we further design a siamese network that encodes the paired images and propose a joint graph cluster partition mechanism in an unsupervised manner to enhance the object co-occurrent regions. To validate the effectiveness of the proposed method, extensive experiments are conducted on CUB-200-2011, Stanford Cars and FGVC-Aircraft datasets. Experimental results show that our method outperforms state-of-the-art methods using the same level of supervision, even outperforms some weakly-supervised methods.

Topics: AAAI

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HOW TO CITE:

Yukun Su||Guosheng Lin||Yun Hao||Yiwen Cao||Wenjun Wang||Qingyao Wu Self-Supervised Object Localization with Joint Graph Partition Proceedings of the AAAI Conference on Artificial Intelligence (2022) 2289-2297.

Yukun Su||Guosheng Lin||Yun Hao||Yiwen Cao||Wenjun Wang||Qingyao Wu Self-Supervised Object Localization with Joint Graph Partition AAAI 2022, 2289-2297.

Yukun Su||Guosheng Lin||Yun Hao||Yiwen Cao||Wenjun Wang||Qingyao Wu (2022). Self-Supervised Object Localization with Joint Graph Partition. Proceedings of the AAAI Conference on Artificial Intelligence, 2289-2297.

Yukun Su||Guosheng Lin||Yun Hao||Yiwen Cao||Wenjun Wang||Qingyao Wu. Self-Supervised Object Localization with Joint Graph Partition. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.2289-2297.

Yukun Su||Guosheng Lin||Yun Hao||Yiwen Cao||Wenjun Wang||Qingyao Wu. 2022. Self-Supervised Object Localization with Joint Graph Partition. "Proceedings of the AAAI Conference on Artificial Intelligence". 2289-2297.

Yukun Su||Guosheng Lin||Yun Hao||Yiwen Cao||Wenjun Wang||Qingyao Wu. (2022) "Self-Supervised Object Localization with Joint Graph Partition", Proceedings of the AAAI Conference on Artificial Intelligence, p.2289-2297

Yukun Su||Guosheng Lin||Yun Hao||Yiwen Cao||Wenjun Wang||Qingyao Wu, "Self-Supervised Object Localization with Joint Graph Partition", AAAI, p.2289-2297, 2022.

Yukun Su||Guosheng Lin||Yun Hao||Yiwen Cao||Wenjun Wang||Qingyao Wu. "Self-Supervised Object Localization with Joint Graph Partition". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.2289-2297.

Yukun Su||Guosheng Lin||Yun Hao||Yiwen Cao||Wenjun Wang||Qingyao Wu. "Self-Supervised Object Localization with Joint Graph Partition". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 2289-2297.

Yukun Su||Guosheng Lin||Yun Hao||Yiwen Cao||Wenjun Wang||Qingyao Wu. Self-Supervised Object Localization with Joint Graph Partition. AAAI[Internet]. 2022[cited 2023]; 2289-2297.


ISSN: 2374-3468


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