7 papers
FATE: Frame-Level Audio-Visual Temporal Embedding
Kaisi Guan, Bingzi Zhang, Xihua Wang +4
When a dog opens its mouth and barks, humans naturally recognize what the sound is and when it occurs. Building audio-visual models with this same ability requires representations…
AVOC: Enhancing Hour-Level Audio-Video Understanding in Omni-Modal LLMs via Retrieval-Inspired Token Compression
Yijing Chen, Wenhui Tan, Xiaoyi Yu +7
Multimodal Large Language Models have achieved remarkable progress in short-form audio-video understanding, yet long-form audio-video comprehension remains challenged by limited co…
Unified Synthesis of Compositional Speech and Sound from Free-Form Text Prompts
Yuyue Wang, Xihua Wang, Xin Cheng +2
Audio generation has made significant progress, yet synthesizing unified audio where speech and sounds are naturally composited remains a challenge. Current methods either rely on…
VSSFlow: Unifying Video-conditioned Sound and Speech Generation via Joint Learning
Xin Cheng, Yuyue Wang, Xihua Wang +7
Video-conditioned audio generation, including Video-to-Sound (V2S) and Visual Text-to-Speech (VisualTTS), has traditionally been treated as distinct tasks, leaving the potential fo…
MSJoE: Jointly Evolving MLLM and Sampler for Efficient Long-Form Video Understanding
Wenhui Tan, Xiaoyi Yu, Jiaze Li +5
Efficiently understanding long-form videos remains a fundamental challenge for multimodal large language models (MLLMs). In this paper, we present MLLM-Sampler Joint Evolution (MSJ…
ChronusOmni: Improving Time Awareness of Omni Large Language Models
Yijing Chen, Yihan Wu, Kaisi Guan +4
Time awareness is a fundamental ability of omni large language models, especially for understanding long videos and answering complex questions. Previous approaches mainly target v…