{"type":"ScholarlyArticle","title":"Towards Generalized Action Recognition on Low-Resolutions with Domain-Invariant Representation","authors":[{"name":"Hao Li","slug":"hao-li","given":"Hao","family":"Li","affiliation":""},{"name":"Jinhui Xu","slug":"jinhui-xu","given":"Jinhui","family":"Xu","affiliation":""},{"name":"Dianlong You","slug":"dianlong-you","given":"Dianlong","family":"You","affiliation":""}],"abstract":"This paper studies cross-domain/species action recognition from low-resolution videos with sharp appearance variations and domain-specific biases. Its attractive viewpoint is mining domain-invariant representations of cross-domain/species features under rough spatial details to enhance recognition and generalization, overcoming the significant decline of existing methods. To address this, we propose a generalized Action recognition framework for Low-resolution conditions with Domain-invariant Representation learning, named ActLDR, designed to learn domain-invariant representations. First, it decomposes video understanding into spatial and temporal pathways for explicitly separating domain-dependent appearance cues from robust motion dynamics; Second, it constructs a Spatial-Temporal Feature Exchange module to enable cross-branch refinement and suppress domain bias; Third, we inject Gaussian feature interference to simulate feature corruption and enforce prediction-level consistency to encourage stable representations. Empirical results demonstrate that our proposal outperforms previous methods, significantly improving robustness across resolutions, domains, and species, and demonstrating outstanding generalization and transferability.","doi":"10.24963/ijcai.2026/146","url":"https://ijcai.org/proceedings/2026/146","pdf":"https://ijcai.org/proceedings/2026/0146.pdf","markdown":"https://ijcai.org/proceedings/2026/146.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/146","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Computer Vision","track":"Main Track","number":146,"pages":{"first":1306,"last":1314},"keywords":["Computer Vision: Video analysis and understanding","Computer Vision: Action and behavior recognition","Computer Vision: Representation learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}