{"type":"ScholarlyArticle","title":"Depth-Relative Self Attention for Monocular Depth Estimation","authors":[{"name":"Kyuhong Shim","slug":"kyuhong-shim","given":"Kyuhong","family":"Shim","affiliation":"Department of Electrical and Computer Engineering , Seoul National University , Korea"},{"name":"Jiyoung Kim","slug":"jiyoung-kim","given":"Jiyoung","family":"Kim","affiliation":"Department of Electrical and Computer Engineering , Seoul National University , Korea"},{"name":"Gusang Lee","slug":"gusang-lee","given":"Gusang","family":"Lee","affiliation":"Department of Electrical and Computer Engineering , Seoul National University , Korea"},{"name":"Byonghyo Shim","slug":"byonghyo-shim","given":"Byonghyo","family":"Shim","affiliation":"Department of Electrical and Computer Engineering , Seoul National University , Korea"}],"abstract":"Monocular depth estimation is very challenging because clues to the exact depth are incomplete in a single RGB image. To overcome the limitation, deep neural networks rely on various visual hints such as size, shade, and texture extracted from RGB information. However, we observe that if such hints are overly exploited, the network can be biased on RGB information without considering the comprehensive view. We propose a novel depth estimation model named RElative Depth Transformer (RED-T) that uses relative depth as guidance in self-attention. Specifically, the model assigns high attention weights to pixels of close depth and low attention weights to pixels of distant depth. As a result, the features of similar depth can become more likely to each other and thus less prone to misused visual hints. We show that the proposed model achieves competitive results in monocular depth estimation benchmarks and is less biased to RGB information. In addition, we propose a novel monocular depth estimation benchmark that limits the observable depth range during training in order to evaluate the robustness of the model for unseen depths.","doi":"10.24963/ijcai.2023/155","url":"https://ijcai.org/proceedings/2023/155","pdf":"https://ijcai.org/proceedings/2023/0155.pdf","markdown":"https://ijcai.org/proceedings/2023/155.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/155","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":155,"pages":{"first":1396,"last":1404},"keywords":["Computer Vision: CV: Applications","Computer Vision: CV: Machine learning for vision"],"videos":[],"references":[{"position":1,"raw":"[Agarwal and Arora, 2023] Ashutosh Agarwal and Chetan Arora. 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