Self-supervised representation learning on videos
Everything you need to know about video-based approaches on self-supervised learning
self-supervised-learning video article

Nowadays, transfer learning from pretrained models on Imagenet is the ultimate standard in computer vision. Self-supervised learning dominates natural language processing, but this doesn’t mean that there are no significant use-cases for computer vision that it should be considered. There are indeed a lot of cool self-supervised tasks that one can devise when she/he is dealing with images, such as jigsaw puzzles [6], image colorization, image inpainting, or even unsupervised image synthesis.

But what happens when the time dimension comes into play? How can you approach the video-based tasks that you would like to solve?

So, let’s start from the beginning, one concept at a time. What is self-supervised learning? And how is it different from transfer learning? What is a pretext task?

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"The statements of science are not of what is true and what is not true, but statements of what is known with different degrees of certainty." - Richard Feynman
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