15 citations · 28 across the 13 of their papers we have counts for
14 papers
BACAM: Behavior-Aware Continual Agent Merging for Multi-Turn Interaction
Shuaitong Li, Baochen Xiong, Xiaoshan Yang +4
Model merging offers a way to integrate the capabilities of specialized experts, but existing agent merging methods typically require all of them to be available at once. We study…
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…
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…
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…
Libra: Building Decoupled Vision System on Large Language Models
Yifan Xu, Xiaoshan Yang, Yaguang Song +1
In this work, we introduce Libra, a prototype model with a decoupled vision system on a large language model (LLM). The decoupled vision system decouples inner-modal modeling and c…
Exploring Multi-Modal Contextual Knowledge for Open-Vocabulary Object Detection
Yifan Xu, Mengdan Zhang, Xiaoshan Yang +1
In this paper, we for the first time explore helpful multi-modal contextual knowledge to understand novel categories for open-vocabulary object detection (OVD). The multi-modal con…
Multi-modal Queried Object Detection in the Wild
Yifan Xu, Mengdan Zhang, Chaoyou Fu +4
We introduce MQ-Det, an efficient architecture and pre-training strategy design to utilize both textual description with open-set generalization and visual exemplars with rich desc…
RecipeSnap -- a lightweight image-to-recipe model
Jianfa Chen, Yue Yin, Yifan Xu
In this paper we want to address the problem of automation for recognition of photographed cooking dishes and generating the corresponding food recipes. Current image-to-recipe mod…
A multi-scale sampling method for accurate and robust deep neural network to predict combustion chemical kinetics
Tianhan Zhang, Yuxiao Yi, Yifan Xu +4
Machine learning has long been considered as a black box for predicting combustion chemical kinetics due to the extremely large number of parameters and the lack of evaluation stan…
Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer
Yifan Xu, Zhijie Zhang, Mengdan Zhang +6
Vision transformers (ViTs) have recently received explosive popularity, but the huge computational cost is still a severe issue. Since the computation complexity of ViT is quadrati…
Towards Corruption-Agnostic Robust Domain Adaptation
Yifan Xu, Kekai Sheng, Weiming Dong +3
Big progress has been achieved in domain adaptation in decades. Existing works are always based on an ideal assumption that testing target domain are i.i.d. with training target do…