---
title: "Shot-Conditioned Vision-Language Adaptation for Effective Harmful Content Detection from Online Short Videos"
authors:
  - "Shuai Xu"
  - "Zao Qiu"
  - "Xuelin Zhu"
  - "Yicong Li"
doi: "10.24963/ijcai.2026/216"
url: "https://ijcai.org/proceedings/2026/216"
pdf: "https://ijcai.org/proceedings/2026/0216.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "1938-1946"
track: "Main Track"
keywords:
  - "Data Mining: Anomaly/outlier detection"
  - "Humans and AI: Applications"
  - "Computer Vision: Image and video retrieval"
---

# Shot-Conditioned Vision-Language Adaptation for Effective Harmful Content Detection from Online Short Videos

Shuai Xu, Zao Qiu, Xuelin Zhu, Yicong Li

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 1938-1946. DOI: [10.24963/ijcai.2026/216](https://doi.org/10.24963/ijcai.2026/216)

## 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.