{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/cross-encoder-for-unsupervised-gaze","title":"Cross-Encoder for Unsupervised Gaze Representation Learning","arxiv_id":null,"date":"2021-01-01","proceeding":"ICCV 2021 10","authors":["Yunjia Sun","Jiabei Zeng","Shiguang Shan","Xilin Chen"],"abstract":"    In order to train 3D gaze estimators without too many annotations, we propose an unsupervised learning framework, Cross-Encoder, to leverage the unlabeled data to learn suitable representation for gaze estimation. To address the issue that the feature of gaze is always intertwined with the appearance of the eye, Cross-Encoder disentangles the features using a latent-code-swapping mechanism on eye-consistent image pairs and gaze-similar ones. Specifically, each image is encoded as a gaze feature and an eye feature. Cross-Encoder is trained to reconstruct each image in the eye-consistent pair according to its gaze feature and the other's eye feature, but to reconstruct each image in the gaze-similar pair according to its eye feature and the other's gaze feature. Experimental results show the validity of our work. First, using the Cross-Encoder-learned gaze representation, the gaze estimator trained with very few samples outperforms the ones using other unsupervised learning methods, under both within-dataset and cross-dataset protocol. Second, ResNet18 pretrained by Cross-Encoder is competitive with state-of-the-art gaze estimation methods. Third, ablation study shows that Cross-Encoder disentangles the gaze feature and eye feature.    ","url_abs":"http://openaccess.thecvf.com//content/ICCV2021/html/Sun_Cross-Encoder_for_Unsupervised_Gaze_Representation_Learning_ICCV_2021_paper.html","url_pdf":"http://openaccess.thecvf.com//content/ICCV2021/papers/Sun_Cross-Encoder_for_Unsupervised_Gaze_Representation_Learning_ICCV_2021_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"cross-encoder-for-unsupervised-gaze","repo_url":"https://github.com/sunyunjia96/Cross-Encoder","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"gaze-estimation","task_name":"Gaze Estimation"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}