PooDLe🐩: Pooled and dense self-supervised learning from naturalistic videos
Alex N Wang Christopher Hoang Yuwen Xiong Yann LeCun Mengye Ren
Self-supervised learning has driven significant progress in learning from single-subject, _iconic_ images.
However, there are still unanswered questions about the use of minimally-curated, naturalistic video data, which contain _dense_ scenes with many independent objects, imbalanced class distributions, and varying object sizes.
In this paper, we propose PooDLe, a self-supervised learning method that combines an invariance-based objective on pooled representations with a dense SSL objective that enforces equivariance to optical flow warping.
Our results show that a unified objective applied at multiple feature scales is essential for learning effective image representations from naturalistic videos.
We validate our method with experiments on the BDD100K driving video dataset and the Walking Tours first-person video dataset, demonstrating its ability to capture spatial understanding from a dense objective and semantic understanding via a pooled representation objective.
computer vision, representation learning, self-supervised learning, egocentric video, visual representation
We uncover challenges of applying self-supervised learning to naturalistic, dense video data and propose a unified dense and pooled objective alongside architectural improvements to learn an effective visual representation.