# Semi-Supervised Semantic Matching

arXiv: https://arxiv.org/abs/1901.08339 · HTML: https://mlsanity.com/paper/1901.08339/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2019.

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

- [Match me if you can: Semi-Supervised Semantic Correspondence Learning with Unpaired Images](https://mlsanity.com/paper/2311.18540/) (2023)

## Abstract (arXiv)

Convolutional neural networks (CNNs) have been successfully applied to solve the problem of correspondence estimation between semantically related images. Due to non-availability of large training datasets, existing methods resort to self-supervised or unsupervised training paradigm. In this paper we propose a semi-supervised learning framework that imposes cyclic consistency constraint on unlabeled image pairs. Together with the supervised loss the proposed model achieves state-of-the-art on a benchmark semantic matching dataset.