6 papers
Neural Computers
Mingchen Zhuge, Changsheng Zhao, Haozhe Liu +16
We propose a new frontier: Neural Computers (NCs) that unify computation, memory, and I/O of traditional computers in a learned runtime state. Our long-term goal is the Completely…
Small Vision-Language Models are Smart Compressors for Long Video Understanding
Junjie Fei, Jun Chen, Zechun Liu +13
Adapting Multimodal Large Language Models (MLLMs) for hour-long videos is bottlenecked by context limits. Dense visual streams saturate token budgets and exacerbate the lost-in-the…
WikiAutoGen: Towards Multi-Modal Wikipedia-Style Article Generation
Zhongyu Yang, Jun Chen, Dannong Xu +5
Knowledge discovery and collection are intelligence-intensive tasks that traditionally require significant human effort to ensure high-quality outputs. Recent research has explored…
Kestrel: 3D Multimodal LLM for Part-Aware Grounded Description
Mahmoud Ahmed, Junjie Fei, Jian Ding +2
In this paper, we introduce Part-Aware Point Grounded Description (PaPGD), a challenging task aimed at advancing 3D multimodal learning for fine-grained, part-aware segmentation gr…
MAGNET: A Multi-agent Framework for Finding Audio-Visual Needles by Reasoning over Multi-Video Haystacks
Sanjoy Chowdhury, Mohamed Elmoghany, Yohan Abeysinghe +5
Large multimodal models (LMMs) have shown remarkable progress in audio-visual understanding, yet they struggle with real-world scenarios that require complex reasoning across exten…
Document Haystacks: Vision-Language Reasoning Over Piles of 1000+ Documents
Jun Chen, Dannong Xu, Junjie Fei +2
Large multimodal models (LMMs) have achieved impressive progress in vision-language understanding, yet they face limitations in real-world applications requiring complex reasoning…