{"type":"ScholarlyArticle","title":"Shot-Conditioned Vision-Language Adaptation for Effective Harmful Content Detection from Online Short Videos","authors":[{"name":"Shuai Xu","slug":"shuai-xu","given":"Shuai","family":"Xu","affiliation":""},{"name":"Zao Qiu","slug":"zao-qiu","given":"Zao","family":"Qiu","affiliation":""},{"name":"Xuelin Zhu","slug":"xuelin-zhu","given":"Xuelin","family":"Zhu","affiliation":""},{"name":"Yicong Li","slug":"yicong-li","given":"Yicong","family":"Li","affiliation":""}],"abstract":"Short video harmful content detection aims to automatically identify diverse anomalies from user-generated media. This task presents unique challenges due to frequent editing cuts and highly variable anomaly densities, limiting the effectiveness of traditional surveillance-based approaches. Moreover, existing Vision-Language Model-based approaches typically rely on rigid instance selection mechanisms that fail to adapt to the unpredictable duration of anomalies in such unconstrained videos. To address these issues, we propose SVLA, a Shot-conditioned Vision-Language Adaptation framework, for effectively detecting harmful contents from online short videos. Our approach introduces a novel π-adaptive strategy to dynamically estimate shot-level anomaly density, replacing rigid selection with calibrated supervision. Furthermore, we employ a shot-conditioned temporal encoder to respect video hierarchy and adopt a dual-path contextual adapter to resolve semantic ambiguity. To benchmark this task, we construct a new dataset (SVA) covering more genuine online short videos that involve seven anomaly categories. Experiments on the SVA dataset demonstrate that SVLA can achieve the state-of-the-art performance and outperform its competitors across diverse scenarios. Codes and datasets are available at: https://github.com/xushuai7/IJCAI-SVLA.","doi":"10.24963/ijcai.2026/216","url":"https://ijcai.org/proceedings/2026/216","pdf":"https://ijcai.org/proceedings/2026/0216.pdf","markdown":"https://ijcai.org/proceedings/2026/216.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/216","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":216,"pages":{"first":1938,"last":1946},"keywords":["Data Mining: Anomaly/outlier detection","Humans and AI: Applications","Computer Vision: Image and video retrieval"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}