# Learning to Learn from Noisy Web Videos

arXiv: https://arxiv.org/abs/1706.02884 · HTML: https://mlsanity.com/paper/1706.02884/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2017.

## Built on by (most cited first, 5 of 5)

- [Imitation Learning from Imperfect Demonstration](https://mlsanity.com/paper/1901.09387/) (2019)
- [Zero-shot Natural Language Video Localization](https://mlsanity.com/paper/2110.00428/) (2021)
- [ProSelfLC: Progressive Self Label Correction Towards A Low-Temperature Entropy State](https://mlsanity.com/paper/2207.00118/) (2022)
- [An Action Is Worth Multiple Words: Handling Ambiguity in Action Recognition](https://mlsanity.com/paper/2210.04933/) (2022)
- [Learning from Weakly-labeled Web Videos via Exploring Sub-Concepts](https://mlsanity.com/paper/2101.03713/) (2021)

## Abstract (arXiv)

Understanding the simultaneously very diverse and intricately fine-grained set of possible human actions is a critical open problem in computer vision. Manually labeling training videos is feasible for some action classes but doesn't scale to the full long-tailed distribution of actions. A promising way to address this is to leverage noisy data from web queries to learn new actions, using semi-supervised or "webly-supervised" approaches. However, these methods typically do not learn domain-specific knowledge, or rely on iterative hand-tuned data labeling policies. In this work, we instead propose a reinforcement learning-based formulation for selecting the right examples for training a classifier from noisy web search results. Our method uses Q-learning to learn a data labeling policy on a small labeled training dataset, and then uses this to automatically label noisy web data for new visual concepts. Experiments on the challenging Sports-1M action recognition benchmark as well as on additional fine-grained and newly emerging action classes demonstrate that our method is able to learn good labeling policies for noisy data and use this to learn accurate visual concept classifiers.