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Status: Unchecked ×Keyword: video action recognition ×Clear all

3 claims from 1 paper

1. Computer Science › Multimodal Machine Learning Applications

   EVA: Exploring the Limits of Masked Visual Representation Learning at Scale

   Fang, Wang, Xie et al. · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 2023

   EVA is a billion-parameter vision model pre-trained by masked feature reconstruction on public data; it sets records on many vision tasks and can also be used to start CLIP training.

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   1. Unchecked“Via this pretext task, we can efficiently scale up EVA to one billion parameters, and sets new records on a broad range of representative vision downstream tasks, such as image recognition, video action recognition, object detection, instance segmentation an…
   2. Unchecked“For instance, EVA takes a great leap in the challenging large vocabulary instance segmentation task: our model achieves almost the same state-of-the-art performance on LVIS dataset with over a thousand categories and COCO dataset with only eighty categories.”
   3. UncheckedThe paper finds that starting a giant CLIP's vision tower from EVA stabilises training and beats training from scratch, using fewer samples and less compute.“We find initializing the vision tower of a giant CLIP from EVA can greatly stabilize the training and outperform the training from scratch counterpart with much fewer samples and less compute, providing a new direction for scaling up and accelerating the costly training of multi-modal foundation mo…”