FRAME distills DINO and CLIP features into a compact video encoder with a memory module and future-frame prediction, outperforming image-based and self-supervised video baselines on dense video tasks.
A Large-Scale Study on Unsupervised Spatiotemporal Representation Learning
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
We present a large-scale study on unsupervised spatiotemporal representation learning from videos. With a unified perspective on four recent image-based frameworks, we study a simple objective that can easily generalize all these methods to space-time. Our objective encourages temporally-persistent features in the same video, and in spite of its simplicity, it works surprisingly well across: (i) different unsupervised frameworks, (ii) pre-training datasets, (iii) downstream datasets, and (iv) backbone architectures. We draw a series of intriguing observations from this study, e.g., we discover that encouraging long-spanned persistency can be effective even if the timespan is 60 seconds. In addition to state-of-the-art results in multiple benchmarks, we report a few promising cases in which unsupervised pre-training can outperform its supervised counterpart. Code is made available at https://github.com/facebookresearch/SlowFast
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
FRAME: Pre-Training Video Feature Representations via Anticipation and Memory
FRAME distills DINO and CLIP features into a compact video encoder with a memory module and future-frame prediction, outperforming image-based and self-supervised video baselines on dense video tasks.