# Representation learning from videos in-the-wild: An object-centric approach

arXiv: https://arxiv.org/abs/2010.02808 · HTML: https://mlsanity.com/paper/2010.02808/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2020.

## Built on by (most cited first, 6 of 6)

- [Generalization and Robustness Implications in Object-Centric Learning](https://mlsanity.com/paper/2107.00637/) (2021)
- [Ranking Info Noise Contrastive Estimation: Boosting Contrastive Learning via Ranked Positives](https://mlsanity.com/paper/2201.11736/) (2022)
- [Object-aware Contrastive Learning for Debiased Scene Representation](https://mlsanity.com/paper/2108.00049/) (2021)
- [Federated Self-supervised Learning for Video Understanding](https://mlsanity.com/paper/2207.01975/) (2022)
- [Self-supervised Video Representation Learning with Cross-Stream Prototypical Contrasting](https://mlsanity.com/paper/2106.10137/) (2021)
- [Pixel-level Correspondence for Self-Supervised Learning from Video](https://mlsanity.com/paper/2207.03866/) (2022)

## Abstract (arXiv)

We propose a method to learn image representations from uncurated videos. We combine a supervised loss from off-the-shelf object detectors and self-supervised losses which naturally arise from the video-shot-frame-object hierarchy present in each video. We report competitive results on 19 transfer learning tasks of the Visual Task Adaptation Benchmark (VTAB), and on 8 out-of-distribution-generalization tasks, and discuss the benefits and shortcomings of the proposed approach. In particular, it improves over the baseline on all 18/19 few-shot learning tasks and 8/8 out-of-distribution generalization tasks. Finally, we perform several ablation studies and analyze the impact of the pretrained object detector on the performance across this suite of tasks.