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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 12: AAAI-21 Technical Tracks 12

Distilling Localization for Self-Supervised Representation Learning

February 1, 2023

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Authors

Nanxuan Zhao

City University of Hong Kong


Zhirong Wu

Microsoft Research


Rynson W.H. Lau

City University of Hong Kong


Stephen Lin

Microsoft Research


DOI:

10.1609/aaai.v35i12.17312


Abstract:

Recent progress in contrastive learning has revolutionized unsupervised representation learning. Concretely, multiple views (augmentations) from the same image are encouraged to map to close embeddings, while views from different images are pulled apart.In this paper, through visualizing and diagnosing classification errors, we observe that current contrastive models are ineffective at localizing the foreground object, limiting their ability to extract discriminative high-level features. This is due to the fact that view generation process considers pixels in an image uniformly.To address this problem, we propose a data-driven approach for learning invariance to backgrounds. It first estimates foreground saliency in images and then creates augmentations by copy-and-pasting the foreground onto a variety of back-grounds. The learning still follows an instance discrimination approach, so that the representation is trained to disregard background content and focus on the foreground. We study a variety of saliency estimation methods, and find that most methods lead to improvements for contrastive learning. With this approach, significant performance is achieved for self-supervised learning on ImageNet classification, and also for object detection on PASCAL VOC and MSCOCO.

Topics: AAAI

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HOW TO CITE:

Nanxuan Zhao||Zhirong Wu||Rynson W.H. Lau||Stephen Lin Distilling Localization for Self-Supervised Representation Learning Proceedings of the AAAI Conference on Artificial Intelligence (2021) 10990-10998.

Nanxuan Zhao||Zhirong Wu||Rynson W.H. Lau||Stephen Lin Distilling Localization for Self-Supervised Representation Learning AAAI 2021, 10990-10998.

Nanxuan Zhao||Zhirong Wu||Rynson W.H. Lau||Stephen Lin (2021). Distilling Localization for Self-Supervised Representation Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 10990-10998.

Nanxuan Zhao||Zhirong Wu||Rynson W.H. Lau||Stephen Lin. Distilling Localization for Self-Supervised Representation Learning. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.10990-10998.

Nanxuan Zhao||Zhirong Wu||Rynson W.H. Lau||Stephen Lin. 2021. Distilling Localization for Self-Supervised Representation Learning. "Proceedings of the AAAI Conference on Artificial Intelligence". 10990-10998.

Nanxuan Zhao||Zhirong Wu||Rynson W.H. Lau||Stephen Lin. (2021) "Distilling Localization for Self-Supervised Representation Learning", Proceedings of the AAAI Conference on Artificial Intelligence, p.10990-10998

Nanxuan Zhao||Zhirong Wu||Rynson W.H. Lau||Stephen Lin, "Distilling Localization for Self-Supervised Representation Learning", AAAI, p.10990-10998, 2021.

Nanxuan Zhao||Zhirong Wu||Rynson W.H. Lau||Stephen Lin. "Distilling Localization for Self-Supervised Representation Learning". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.10990-10998.

Nanxuan Zhao||Zhirong Wu||Rynson W.H. Lau||Stephen Lin. "Distilling Localization for Self-Supervised Representation Learning". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 10990-10998.

Nanxuan Zhao||Zhirong Wu||Rynson W.H. Lau||Stephen Lin. Distilling Localization for Self-Supervised Representation Learning. AAAI[Internet]. 2021[cited 2023]; 10990-10998.


ISSN: 2374-3468


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