Image-based pretraining learns video representations with fewer training epochs
A method that uses image data to train on videos shows strong results with less video pretraining time; real-world gains depend on data.
- Publication
- arXiv
- Stage
- Preprint
- What we read
- Summary of the paper
- Authors
- Owais Iqbal, Sudipta Sarkar, Shyam Marjit, Omprakash Chakraborty, Anirban Chakraborty, Abir Das
- Universities and research institutions
- Not yet supplied in verified metadata; the Brief does not guess.
What they did and found
Researchers treat a video as a grid of frames and train an image model by masking some views. They achieved strong results with far fewer video pretraining epochs than older methods, though a few datasets show small gaps.
Why it matters
You can reuse existing image models for video tasks with less compute, but results depend on how similar your data are to tested cases.
By arranging frames into a grid, this method avoids heavy 3D models while still catching motion. It keeps training light and aims to stabilize video representations.
Starting from an image-trained backbone, you run short, focused video pretraining steps. Gains vary by dataset and how actions appear in your footage.
What remains uncertain
Results depend on the dataset and may not transfer to very different video types; vary with backbone choices.
Original sources · 1
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