5 papers indexed
Cangjin Qiu, Quan Zhang, Dan Jiang, Ke Zhang
With the proliferation of AI-generated content, sophisticated multimedia manipulation has raised critical concerns about malicious applications such as opinion manipulation and evidence fabrication, making Audio-Visual Temporal Forgery Localization (AV-TFL) an urgent research fro…
Shen Shen, Quan Zhang, Dan Jiang, Ke Zhang
The rapid proliferation of artificial intelligence-generated content necessitates reliable multimodal forensics. Beyond video-level binary classification, precisely localizing sparsely distributed forged segments in long-form videos remains a critical challenge. This task is part…
Jijie Zhang, Zhe Ren, Quan Zhang, Dandan Guo
Large language models (LLMs) exhibit remarkable reasoning capabilities, but their task-specific fine-tuning is notoriously plagued by overconfidence, severely hindering trustworthy deployment. We propose Data-Adaptive Lower-Rank Adaptation (DALorRA), a simple and effective variat…
Jingchen Ni, Cangjin Yu, Dan Jiang, Quan Zhang, Keyu Lv, Shannan Yan, et al.
Driven by Artificial Intelligence-Generated Content (AIGC), the authenticity of audio-visual content is facing severe challenges. Temporal Forgery Localization (TFL) aims to precisely identify manipulated segments within untrimmed sequences. However, existing methods are limited…
Lifeng Zhang, Teng Li, Hongyan Cui, Quan Zhang, Zijie Jiang, Jiadong Li, et al.
Multimodal medical data provides a wide and real basis for disease diagnosis. Computer-aided diagnosis (CAD) powered by artificial intelligence (AI) is becoming increasingly prominent in disease diagnosis. CAD for multimodal medical data requires addressing the issues of data fus…