GAZE 2024: Gaze Estimation and Prediction in the Wild

Image credit to DALL·E

Introduction

The 6th International Workshop on Gaze Estimation and Prediction in the Wild (GAZE 2024) at CVPR 2024 aims to encourage and highlight novel strategies for eye gaze estimation and prediction. The workshop topics include (but are not limited to):

Enhancing eye image segmentation, landmark localization, gaze estimation and other tasks in mixed and augmented reality (XR / AR) settings.
Novel multi-modal systems for incorporating gaze information to improve visual recognition tasks.
Improving eye detection, gaze estimation, and gaze prediction pipelines in various ways, such as by applying geometric and anatomical constraints, leveraging additional cues such as head pose, scene content, or considering multi-modal inputs.
Developing adversarial or domain generalization methods to improve cross-dataset performance or to deal with conditions where current methods fail (illumination, appearance, etc.).
Exploring attention mechanisms and temporal information to predict the point of regard.
Novel methods for temporal gaze estimation and prediction including Bayesian methods.
Personalization of gaze estimators with methods such as few-shot learning.
Semi-/weak-/un-/self- supervised learning methods, domain adaptation methods, and other novel methods towards improved representation learning from eye/face region images or gaze target region images.

We will be hosting 2 invited speakers and will also be accepting the submission of full unpublished papers as done in previous versions of the workshop. These papers will be peer-reviewed via a double-blind process, and will be published in the official workshop proceedings and be presented at the workshop itself.

Call for Contributions

Full Workshop Papers

Submission: We invite authors to submit unpublished papers (8-page CVPR format) to our workshop, to be presented at a poster session upon acceptance. All submissions will go through a double-blind review process. All contributions must be submitted (along with supplementary materials, if any) at this CMT link.

Accepted papers will be published in the official CVPR Workshops proceedings and the Computer Vision Foundation (CVF) Open Access archive.

Note: Authors of previously rejected main conference submissions are also welcome to submit their work to our workshop. When doing so, you must submit the previous reviewers' comments (named as previous_reviews.pdf) and a letter of changes (named as letter_of_changes.pdf) as part of your supplementary materials to clearly demonstrate the changes made to address the comments made by previous reviewers.