# Semi-supervised Contrastive Regression for Estimation of Eye Gaze

arXiv: https://arxiv.org/abs/2308.02784 · HTML: https://mlsanity.com/paper/2308.02784/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 5 Aug 2023.

## Builds on (most cited first, 8 of 8)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://mlsanity.com/paper/2103.03230/) (2021)
- [ETH-XGaze: A Large Scale Dataset for Gaze Estimation under Extreme Head Pose and Gaze Variation](https://mlsanity.com/paper/2007.15837/) (2020)
- [Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark](https://mlsanity.com/paper/2104.12668/) (2021)
- [Detecting Attended Visual Targets in Video](https://mlsanity.com/paper/2003.02501/) (2020)
- [Gaze-Contingent Ocular Parallax Rendering for Virtual Reality](https://mlsanity.com/paper/1906.09740/) (2019)
- [Gaze-Net: Appearance-Based Gaze Estimation using Capsule Networks](https://mlsanity.com/paper/2004.07777/) (2020)

## Abstract (arXiv)

With the escalated demand of human-machine interfaces for intelligent systems, development of gaze controlled system have become a necessity. Gaze, being the non-intrusive form of human interaction, is one of the best suited approach. Appearance based deep learning models are the most widely used for gaze estimation. But the performance of these models is entirely influenced by the size of labeled gaze dataset and in effect affects generalization in performance. This paper aims to develop a semi-supervised contrastive learning framework for estimation of gaze direction. With a small labeled gaze dataset, the framework is able to find a generalized solution even for unseen face images. In this paper, we have proposed a new contrastive loss paradigm that maximizes the similarity agreement between similar images and at the same time reduces the redundancy in embedding representations. Our contrastive regression framework shows good performance in comparison to several state of the art contrastive learning techniques used for gaze estimation.