Understanding Self-Supervised Pretraining with Part-Aware Representation Learning

In this paper, we are interested in understanding self-supervised pretraining through studying the capability that self-supervised methods learn part-aware representations. The study is mainly motivated by that random views, used in contrastive learning, and random masked (visible) patches, used in masked image modeling, are often about object parts. We explain that contrastive learning is a part-to-whole task: the projection layer hallucinates the whole object representation from the object part representation learned from the encoder, and that masked image modeling is a part-to-part task: the masked patches of the object are hallucinated from the visible patches. The explanation suggests that the self-supervised pretrained encoder leans toward understanding the object part. We empirically compare the off-the-shelf encoders pretrained with several representative methods on object-level recognition and part-level recognition. The results show that the fully-supervised model outperforms self-supervised models for object-level recognition, and most self-supervised contrastive learning and masked image modeling methods outperform the fully-supervised method for part-level recognition. It is observed that the combination of contrastive learning and masked image modeling further improves the performance.

Authors

• Jie Zhu School of Artificial Intelligence and Computer Science, Jiangnan University
• Jiyang Qi
• Mingyu Ding
• Xiaokang Chen
• Ping Luo The University of Hong Kong
• Xinggang Wang Institute of AI, School of EIC, Huazhong University of Science and Technology
• Wenyu Liu
• Leye Wang
• Jingdong Wang Baidu

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