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I Have a Stream: Making Self-Supervised Learning Work on Continuous Video

Paper recorded by Signals 4 on 2026-09-30 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-30 on arXiv · recorded by Signals 4 on 2026-10-01

Category: cs.CV · 计算机视觉 · first seen 2026-10-01

Abstract

Self-supervised learning draws inspiration from infant visual development, yet standard training pipelines bear little resemblance to it: images are independently sampled and globally shuffled across epochs. We study self-supervised learning from continuous video streams, where frames are consumed in temporal order using strict sliding-window batches, without global reshuffling or multi-epoch repl

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#17 most recent of 380 cs.CV papers we have recorded · ↑ newer: Ego4WAM: What Matters When Scaling Egocentric Human Data for Robot Lea · ↓ older: Atomizer-IO: Beyond Pixels, Patches and Grids
Cite this page: I Have a Stream: Making Self-Supervised Learning Work on Continuous Video: the #17 most recent of 380 cs.CV papers we have recorded (as of 2026-09-30). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/i-have-a-stream-making-self-supervised-learning-work-on-continuous-video.html
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