128 citations · 757 across the 38 of their papers we have counts for
12 papers · 1 filter
Task Preference Optimization: Improving Multimodal Large Language Models with Vision Task Alignment
Ziang Yan, Zhilin Li, Yinan He +9
Current multimodal large language models (MLLMs) struggle with fine-grained or precise understanding of visuals although they give comprehensive perception and reasoning in a spect…
Bootstrapping Language-Guided Navigation Learning with Self-Refining Data Flywheel
Zun Wang, Jialu Li, Yicong Hong +8
Creating high-quality data for training robust language-instructed agents is a long-lasting challenge in embodied AI. In this paper, we introduce a Self-Refining Data Flywheel (SRD…
TransAgent: Transfer Vision-Language Foundation Models with Heterogeneous Agent Collaboration
Yiwei Guo, Shaobin Zhuang, Kunchang Li +2
Vision-language foundation models (such as CLIP) have recently shown their power in transfer learning, owing to large-scale image-text pre-training. However, target domain data in…
TimeSuite: Improving MLLMs for Long Video Understanding via Grounded Tuning
Xiangyu Zeng, Kunchang Li, Chenting Wang +10
Multimodal Large Language Models (MLLMs) have demonstrated impressive performance in short video understanding. However, understanding long-form videos still remains challenging fo…
MUSES: 3D-Controllable Image Generation via Multi-Modal Agent Collaboration
Yanbo Ding, Shaobin Zhuang, Kunchang Li +3
Despite recent advancements in text-to-image generation, most existing methods struggle to create images with multiple objects and complex spatial relationships in the 3D world. To…
VideoEval: Comprehensive Benchmark Suite for Low-Cost Evaluation of Video Foundation Model
Xinhao Li, Zhenpeng Huang, Jing Wang +2
With the growth of high-quality data and advancement in visual pre-training paradigms, Video Foundation Models (VFMs) have made significant progress recently, demonstrating their r…
Video Mamba Suite: State Space Model as a Versatile Alternative for Video Understanding
Guo Chen, Yifei Huang, Jilan Xu +7
Understanding videos is one of the fundamental directions in computer vision research, with extensive efforts dedicated to exploring various architectures such as RNN, 3D CNN, and…
VideoMamba: State Space Model for Efficient Video Understanding
Kunchang Li, Xinhao Li, Yi Wang +4
Addressing the dual challenges of local redundancy and global dependencies in video understanding, this work innovatively adapts the Mamba to the video domain. The proposed VideoMa…
InternVideo2: Scaling Foundation Models for Multimodal Video Understanding
Yi Wang, Kunchang Li, Xinhao Li +17
We introduce InternVideo2, a new family of video foundation models (ViFM) that achieve the state-of-the-art results in video recognition, video-text tasks, and video-centric dialog…
Percept, Chat, and then Adapt: Multimodal Knowledge Transfer of Foundation Models for Open-World Video Recognition
Boyu Chen, Siran Chen, Kunchang Li +3
Open-world video recognition is challenging since traditional networks are not generalized well on complex environment variations. Alternatively, foundation models with rich knowle…
From GPT-4 to Gemini and Beyond: Assessing the Landscape of MLLMs on Generalizability, Trustworthiness and Causality through Four Modalities
Chaochao Lu, Chen Qian, Guodong Zheng +33
Multi-modal Large Language Models (MLLMs) have shown impressive abilities in generating reasonable responses with respect to multi-modal contents. However, there is still a wide ga…
Vlogger: Make Your Dream A Vlog
Shaobin Zhuang, Kunchang Li, Xinyuan Chen +4
In this work, we present Vlogger, a generic AI system for generating a minute-level video blog (i.e., vlog) of user descriptions. Different from short videos with a few seconds, vl…