ViC-MAE: Self-Supervised Representation Learning from Images and Video with Contrastive Masked Autoencoders

• URL: http://arxiv.org/abs/2303.12001v3
• Date: Wed, 02 Oct 2024 21:58:04 GMT
• Title: ViC-MAE: Self-Supervised Representation Learning from Images and Video with Contrastive Masked Autoencoders
• Authors: Jefferson Hernandez, Ruben Villegas, Vicente Ordonez,
• Abstract summary: ViC-MAE is a model that combines Masked AutoEncoders (MAE) and contrastive learning. We show that visual representations learned under ViC-MAE generalize well to both video and image classification tasks.
• Score: 11.727612242016871
• License:
• Abstract: We propose ViC-MAE, a model that combines both Masked AutoEncoders (MAE) and contrastive learning. ViC-MAE is trained using a global featured obtained by pooling the local representations learned under an MAE reconstruction loss and leveraging this representation under a contrastive objective across images and video frames. We show that visual representations learned under ViC-MAE generalize well to both video and image classification tasks. Particularly, ViC-MAE obtains state-of-the-art transfer learning performance from video to images on Imagenet-1k compared to the recently proposed OmniMAE by achieving a top-1 accuracy of 86% (+1.3% absolute improvement) when trained on the same data and 87.1% (+2.4% absolute improvement) when training on extra data. At the same time ViC-MAE outperforms most other methods on video benchmarks by obtaining 75.9% top-1 accuracy on the challenging Something something-v2 video benchmark . When training on videos and images from a diverse combination of datasets, our method maintains a balanced transfer-learning performance between video and image classification benchmarks, coming only as a close second to the best supervised method.

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