# Meta Learning with Differentiable Closed-form Solver for Fast Video Object Segmentation

arXiv: https://arxiv.org/abs/1909.13046 · HTML: https://mlsanity.com/paper/1909.13046/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2019.

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

- [Joint Inductive and Transductive Learning for Video Object Segmentation](https://mlsanity.com/paper/2108.03679/) (2021)
- [Meta-Forecasting by combining Global Deep Representations with Local Adaptation](https://mlsanity.com/paper/2111.03418/) (2021)
- [CLVOS23: A Long Video Object Segmentation Dataset for Continual Learning](https://mlsanity.com/paper/2304.04259/) (2023)
- [Preserving Fine-Grain Feature Information in Classification via Entropic Regularization](https://mlsanity.com/paper/2208.03684/) (2022)
- [Memory-Efficient Continual Learning Object Segmentation for Long Video](https://mlsanity.com/paper/2309.15274/) (2023)
- [On Transfer in Classification: How Well do Subsets of Classes Generalize?](https://mlsanity.com/paper/2403.03569/) (2024)

## Abstract (arXiv)

This paper tackles the problem of video object segmentation. We are specifically concerned with the task of segmenting all pixels of a target object in all frames, given the annotation mask in the first frame. Even when such annotation is available this remains a challenging problem because of the changing appearance and shape of the object over time. In this paper, we tackle this task by formulating it as a meta-learning problem, where the base learner grasping the semantic scene understanding for a general type of objects, and the meta learner quickly adapting the appearance of the target object with a few examples. Our proposed meta-learning method uses a closed form optimizer, the so-called "ridge regression", which has been shown to be conducive for fast and better training convergence. Moreover, we propose a mechanism, named "block splitting", to further speed up the training process as well as to reduce the number of learning parameters. In comparison with the-state-of-the art methods, our proposed framework achieves significant boost up in processing speed, while having very competitive performance compared to the best performing methods on the widely used datasets.