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Title: ViC-MAE: Self-supervised Representation Learning from Images and Video with Contrastive Masked Autoencoders
We propose ViC-MAE, a model that combines both Masked AutoEncoders (MAE) and contrastive learning. ViC-MAE is trained using a global representation obtained by pooling the local features learned under an MAE reconstruction loss and using this representation under a contrastive objective across images and video frames. We show that visual representations learned under ViC-MAE generalize well to 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 diverse 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.  more » « less
Award ID(s):
2201710
PAR ID:
10630373
Author(s) / Creator(s):
; ;
Publisher / Repository:
European Conference on Computer Vision (ECCV), Springer, Cham
Date Published:
ISBN:
978-3-031-73234-8
Format(s):
Medium: X
Location:
Milan, Italy
Sponsoring Org:
National Science Foundation
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