2 citations · 2 across the 8 of their papers we have counts for
8 papers
Cosmos 3: Omnimodal World Models for Physical AI
NVIDIA, :, Aditi +293
We introduce Cosmos 3, a family of omnimodal world models designed to jointly process and generate language, image, video, audio, and action sequences within a unified mixture-of-t…
VEBench:Benchmarking Large Multimodal Models for Real-World Video Editing
Andong Deng, Dawei Du, Zhenfang Chen +7
Real-world video editing demands not only expert knowledge of cinematic techniques but also multimodal reasoning to select, align, and combine footage into coherent narratives. Whi…
Vidi2.5: Large Multimodal Models for Video Understanding and Creation
Vidi Team, Chia-Wen Kuo, Chuang Huang +31
Video has emerged as the primary medium for communication and creativity on the Internet, driving strong demand for scalable, high-quality video production. Vidi models continue to…
Vidi: Large Multimodal Models for Video Understanding and Editing
Vidi Team, Celong Liu, Chia-Wen Kuo +20
Humans naturally share information with those they are connected to, and video has become one of the dominant mediums for communication and expression on the Internet. To support t…
Where do Large Vision-Language Models Look at when Answering Questions?
Xiaoying Xing, Chia-Wen Kuo, Li Fuxin +6
Large Vision-Language Models (LVLMs) have shown promising performance in vision-language understanding and reasoning tasks. However, their visual understanding behaviors remain und…
D-Attn: Decomposed Attention for Large Vision-and-Language Models
Chia-Wen Kuo, Sijie Zhu, Fan Chen +2
Large vision-and-language models (LVLMs) have traditionally integrated visual and textual tokens by concatenating them into a single homogeneous input for large language models (LL…
Beyond Raw Videos: Understanding Edited Videos with Large Multimodal Model
Lu Xu, Sijie Zhu, Chunyuan Li +7
The emerging video LMMs (Large Multimodal Models) have achieved significant improvements on generic video understanding in the form of VQA (Visual Question Answering), where the ra…
CuMo: Scaling Multimodal LLM with Co-Upcycled Mixture-of-Experts
Jiachen Li, Xinyao Wang, Sijie Zhu +6
Recent advancements in Multimodal Large Language Models (LLMs) have focused primarily on scaling by increasing text-image pair data and enhancing LLMs to improve performance on mul…