28 citations · 50 across the 21 of their papers we have counts for
12 papers · 1 filter
A Visual Dependence-Aware Framework for Multimodal Unsupervised Continual Post-Training
Kaichen Li, Zhilin Zhu, Jianhao Huang +7
In this paper, we explore a novel task of Multimodal Unsupervised Continual Post-Training (MU-CPT), enabling deployed MLLMs to continually evolve from streaming unlabeled data. Exi…
UniFed-VLM: Federated Instruction Tuning for Vision-Language Models with Multiple Heterogeneity
Pengyu Wang, Baochen Xiong, Xiaoshan Yang +4
Vision-Language Models (VLMs) have demonstrated strong performance in multimodal understanding and generation. However, fine-tuning of VLMs typically relies on centralized data, wh…
Decoupled Vision-Language System for Multimodal Understanding and Generation
Yifan Xu, Baochen Xiong, Xiaoshan Yang +3
We introduce a new architecture design for multimodal large language models (MLLMs), Libra, capable of both multimodal understanding and generation. Libra architecture contains one…
VTM-Nav: Harnessing Cross-Episode Experience for Object-Goal Navigation with Hierarchical Visual-Topological Memory
Xiaoran Xu, Yupeng Wu, Tianyu Xue +4
Training-free ObjectNav agents increasingly use vision-language models (VLMs), yet typically discard acquired scene knowledge after each request. We study cross-episode ObjectNav,…
DOME: Learning Transferable Domain Variables from Sparse Supervision for Test-Time Adaptation
Xiaoran Xu, Yifan Xu, Yupeng Wu +2
Test-time adaptation (TTA) aims to align a model to shifting test domains using only unlabeled streaming data. Most existing methods implicitly infer a single global domain distrib…
Boosting Multimodal Federated Learning via Chained Modality Optimization
Zixin Zhang, Fan Qi, Shuai Li +2
Multimodal Federated Learning (MMFL) enables privacy-preserving collaborative learning across decentralized clients with heterogeneous data and modality availability. However, most…
General Covariant Action Modeling: Constructing Generalized Manifolds via Spatio-Temporal Decoupling
Huaihai Lyu, Chaofan Chen, Mingyu Cao +2
Achieving robust generalization from limited data is a central challenge in embodied intelligence. Prevailing methods fail by regressing absolute coordinates, which violates the pr…
Replacing Parameters with Preferences: Federated Alignment of Heterogeneous Vision-Language Models
Shule Lu, Yujing Wang, Hainan Zhang +5
Vision-Language Models (VLMs) have broad potential in privacy-sensitive domains such as healthcare and finance, yet strict data-sharing constraints render centralized training infe…
PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging
Zibo Shao, Baochen Xiong, Xiaoshan Yang +4
Multimodal Large Language Models (MLLMs) rely on multimodal pre-training over diverse data sources, where different datasets often induce complementary cross-modal alignment capabi…
A Step Toward Federated Pretraining of Multimodal Large Language Models
Baochen Xiong, Yifan Xu, Xiaoshan Yang +3
The rapid evolution of Multimodal Large Language Models (MLLMs) is bottlenecked by the saturation of high-quality public data, while vast amounts of diverse multimodal data remain…
Replacing Parameters with Preferences: Federated Alignment of Heterogeneous Vision-Language Models
Shule Lu, Yujing Wang, Hainan Zhang +5
VLMs have broad potential in privacy-sensitive domains such as healthcare and finance, yet strict data-sharing constraints render centralized training infeasible. FL mitigates this…
Towards Domain-Generalized Open-Vocabulary Object Detection: A Progressive Domain-invariant Cross-modal Alignment Method
Xiaoran Xu, Xiaoshan Yang, Jiangang Yang +3
Open-Vocabulary Object Detection (OVOD) has achieved remarkable success in generalizing to novel categories. However, this success often rests on the implicit assumption of domain…