From the 1 of 6 linked papers with an AI index.
6 papers
AVSCap: Orchestrating Audio-Visual Synergy for Omni-modal Video Captioning
Yanghai Wang, Jiahao Wang, Jiafu Tang +9
The paper introduces AVSCap, a system for omni-modal video captioning that explicitly binds visual and audio events, using a large tri-modal dataset and a two-stage training with r…
The Latent Space: Foundation, Evolution, Mechanism, Ability, and Outlook
Xinlei Yu, Zhangquan Chen, Yongbo He +36
Latent space is rapidly emerging as a native substrate for language-based models. While modern systems are still commonly understood through explicit token-level generation, an inc…
T2AV-Compass: Towards Unified Evaluation for Text-to-Audio-Video Generation
Zhe Cao, Tao Wang, Jiaming Wang +10
Text-to-Audio-Video (T2AV) generation aims to synthesize temporally coherent video and semantically synchronized audio from natural language, yet its evaluation remains fragmented,…
OmniHalluc-L: Counterfactual Benchmarking and Modality-Perturbation Reliability Calibration for Long-Form Omni Hallucination
Zixuan Dong, Jiafu Tang, Zhide Lei +7
Long-video Omni assistants often fail not by inventing content, but by misbinding real evidence: they hear the right utterance and see the right event, yet attach it to the wrong s…
OmniVideoBench: Towards Audio-Visual Understanding Evaluation for Omni MLLMs
Caorui Li, Yu Chen, Yiyan Ji +40
Recent advances in multimodal large language models (MLLMs) have demonstrated substantial potential in video understanding. However, existing benchmarks fail to comprehensively eva…
SafeDialBench: A Fine-Grained Safety Evaluation Benchmark for Large Language Models in Multi-Turn Dialogues with Diverse Jailbreak Attacks
Hongye Cao, Sijia Jing, Yanming Wang +14
With the rapid advancement of Large Language Models (LLMs), the safety of LLMs has been a critical concern requiring precise assessment. Current benchmarks primarily concentrate on…