Semantic-aware auto-encoders for self-supervised representation learning

Conference item

The resurgence of unsupervised learning can be attributed to the remarkable progress of self-supervised learning, which includes generative $(\mathcal{G})$ and discriminative $(\mathcal{D})$ models. In computer vision, the mainstream self-supervised learning algorithms are $\mathcal{D}$ models. However, designing a $\mathcal{D}$ model could be over-complicated; also, some studies hinted that a $\mathcal{D}$ model might not be as general and interpretable as a $\mathcal{G}$ model. In this paper, we switch from $\mathcal{D}$ models to $\mathcal{G}$ models using the classical auto-encoder $(AE)$ . Note that a vanilla $\mathcal{G}$ model was far less efficient than a $\mathcal{D}$ model in self-supervised computer vision tasks, as it wastes model capability on overfitting semantic-agnostic high-frequency details. Inspired by perceptual learning that could use cross-view learning to perceive concepts and semantics 1 1 Following [26], we refer to semantics as visual concepts, e.g., a semantic-ware model indicates the model can perceive visual concepts, and the learned features are efficient in object recognition, detection, etc., we propose a novel $AE$ that could learn semantic-aware representation via cross-view image reconstruction. We use one view of an image as the input and another view of the same image as the reconstruction target. This kind of $AE$ has rarely been studied before, and the optimization is very difficult. To enhance learning ability and find a feasible solution, we propose a semantic aligner that uses geometric transformation knowledge to align the hidden code of $AE$ to help optimization. These techniques significantly improve the representation learning ability of $AE$ and make selfsupervised learning with $\mathcal{G}$ models possible. Extensive experiments on many large-scale benchmarks (e.g., ImageNet, COCO 2017, and SYSU-30k) demonstrate the effectiveness of our methods. Code is available at https://github.com/wanggrun/Semantic-Aware-AE.

Authors: Wang, G; Tang, Y; Lin, L; Torr, PHS

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Host title: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
• Pages: 9654-9665
• Publication date: 2022-09-27
• Acceptance date: 2022-03-02
• Event title: IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR 2022)
• Event location: New Orleans, Louisiana
• Event website: https://cvpr2022.thecvf.com/
• Event start date: 2022-06-21
• Event end date: 2022-06-24
• DOI: 10.1109/cvpr52688.2022.00944
• EISSN: 2575-7075
• ISSN: 1063-6919
• EISBN: 9781665469463
• ISBN: 9781665469470
• Language: English
• Keywords: FFR