# Self-reinforcing Unsupervised Matching

arXiv: https://arxiv.org/abs/1909.04138 · HTML: https://mlsanity.com/paper/1909.04138/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Aug 2019.

## Built on by (most cited first, 1 of 1)

- [Video Relationship Detection Using Mixture of Experts](https://mlsanity.com/paper/2403.03994/) (2024)

## Abstract (arXiv)

Remarkable gains in deep learning usually rely on tremendous supervised data. Ensuring the modality diversity for one object in training set is critical for the generalization of cutting-edge deep models, but it burdens human with heavy manual labor on data collection and annotation. In addition, some rare or unexpected modalities are new for the current model, causing reduced performance under such emerging modalities. Inspired by the achievements in speech recognition, psychology and behavioristics, we present a practical solution, self-reinforcing unsupervised matching (SUM), to annotate the images with 2D structure-preserving property in an emerging modality by cross-modality matching. This approach requires no any supervision in emerging modality and only one template in seen modality, providing a possible route towards continual learning.