5 papers · 1 filter
DynFrame: Adaptive Reasoning-Driven Multimodal Framework with Dynamic Frame Augmentation for Complex Video Understanding
Peng Zhang, Guanghao Zhang, Wanggui He +10
Recent video multimodal large language models (MLLMs) increasingly couple step-by-step reasoning with on-demand visual evidence retrieval, allowing models to revisit relevant video…
RoMo: A Large-Scale, Richly Organized Dataset and Semantic Taxonomy for Human Motion Generation
Jiahao Zhang, Joseph Liu, Young-Yoon Lee +9
Success in generative modeling across language, image, and video demonstrates that large, well-curated datasets are the key driver for building capable models. 3D Human motion, how…
Diffusion-APO: Trajectory-Aware Direct Preference Alignment for Video Diffusion Transformers
Jingyuan Zhu, Biaolong Chen, Le Zhang +3
Efficiently aligning large-scale video diffusion models with human intent requires a scalable and trajectory-aware pathway that bridges the inherent discrepancy between training no…
Audio-Visual LLM for Video Understanding
Fangxun Shu, Lei Zhang, Hao Jiang +1
This paper presents Audio-Visual LLM, a Multimodal Large Language Model that takes both visual and auditory inputs for holistic video understanding. A key design is the modality-au…
Filter & Align: Leveraging Human Knowledge to Curate Image-Text Data
Lei Zhang, Fangxun Shu, Tianyang Liu +3
The increasing availability of image-text pairs has largely fueled the rapid advancement in vision-language foundation models. However, the vast scale of these datasets inevitably…