3 citations · 3 across the 6 of their papers we have counts for
6 papers
MOSH-WM: Mask-Grounded Soft-Hamiltonian Dynamics for Object-Centric World Models
Zhekai Wang, Haoxiang Huang, Xiang Liu +6
Object-centric world models forecast future videos by evolving a set of entity slots, but the variables receiving dynamics supervision are often unconstrained visual features. We i…
CoDoL: Conditional Domain Prompt Learning for Out-of-Distribution Generalization
Min Zhang, Yuyin Wang, Zhongxiang Dai +4
Recent advances in pre-training vision-language models (VLMs), e.g., contrastive language-image pre-training (CLIP) methods, have shown great potential in learning out-of-distribut…
Building a Mind Palace: Structuring Environment-Grounded Semantic Graphs for Effective Long Video Analysis with LLMs
Zeyi Huang, Yuyang Ji, Xiaofang Wang +11
Long-form video understanding with Large Vision Language Models is challenged by the need to analyze temporally dispersed yet spatially concentrated key moments within limited cont…
Accelerating Multimodal Large Language Models by Searching Optimal Vision Token Reduction
Shiyu Zhao, Zhenting Wang, Felix Juefei-Xu +7
Prevailing Multimodal Large Language Models (MLLMs) encode the input image(s) as vision tokens and feed them into the language backbone, similar to how Large Language Models (LLMs)…
Unleashing In-context Learning of Autoregressive Models for Few-shot Image Manipulation
Bolin Lai, Felix Juefei-Xu, Miao Liu +8
Text-guided image manipulation has experienced notable advancement in recent years. In order to mitigate linguistic ambiguity, few-shot learning with visual examples has been appli…
Model-in-the-Loop (MILO): Accelerating Multimodal AI Data Annotation with LLMs
Yifan Wang, David Stevens, Pranay Shah +10
The growing demand for AI training data has transformed data annotation into a global industry, but traditional approaches relying on human annotators are often time-consuming, lab…