2 citations · 3 across the 19 of their papers we have counts for
21 papers
FedHarmony: Harmonizing Heterogeneous Label Correlations in Federated Multi-Label Learning
Zhiqiang Kou, Junxiang Wu, Wenke Huang +8
Federated Multi-Label Learning is a distributed paradigm where multiple clients possess heterogeneous multi-label data and perform collaborative learning under privacy constraints…
FedBPrompt: Federated Domain Generalization Person Re-Identification via Body Distribution Aware Visual Prompts
Xin Xu, Weilong Li, Wei Liu +5
Federated Domain Generalization for Person Re-Identification (FedDG-ReID) learns domain-invariant representations from decentralized data. While Vision Transformer (ViT) is widely…
Generalizable Geometric Prior and Recurrent Spiking Feature Learning for Humanoid Robot Manipulation
Xuetao Li, Wenke Huang, Mang Ye +4
Humanoid robot manipulation is a crucial research area for executing diverse human-level tasks, involving high-level semantic reasoning and low-level action generation. However, pr…
RGMP: Recurrent Geometric-prior Multimodal Policy for Generalizable Humanoid Robot Manipulation
Xuetao Li, Wenke Huang, Nengyuan Pan +7
Humanoid robots exhibit significant potential in executing diverse human-level skills. However, current research predominantly relies on data-driven approaches that necessitate ext…
SafeGRPO: Self-Rewarded Multimodal Safety Alignment via Rule-Governed Policy Optimization
Xuankun Rong, Wenke Huang, Tingfeng Wang +3
Multimodal large language models (MLLMs) have demonstrated impressive reasoning and instruction-following capabilities, yet their expanded modality space introduces new composition…
Divide, Conquer and Unite: Hierarchical Style-Recalibrated Prototype Alignment for Federated Medical Segmentation
Xingyue Zhao, Wenke Huang, Xingguang Wang +5
Federated learning enables multiple medical institutions to train a global model without sharing data, yet feature heterogeneity from diverse scanners or protocols remains a major…
MAPO: Mixed Advantage Policy Optimization
Wenke Huang, Quan Zhang, Yiyang Fang +11
Recent advances in reinforcement learning for foundation models, such as Group Relative Policy Optimization (GRPO), have significantly improved the performance of foundation models…
S2FGL: Spatial Spectral Federated Graph Learning
Zihan Tan, Suyuan Huang, Guancheng Wan +3
Federated Graph Learning (FGL) combines the privacy-preserving capabilities of federated learning (FL) with the strong graph modeling capability of Graph Neural Networks (GNNs). Cu…
Backdoor Cleaning without External Guidance in MLLM Fine-tuning
Xuankun Rong, Wenke Huang, Jian Liang +5
Multimodal Large Language Models (MLLMs) are increasingly deployed in fine-tuning-as-a-service (FTaaS) settings, where user-submitted datasets adapt general-purpose models to downs…
CoT-Kinetics: A Theoretical Modeling Assessing LRM Reasoning Process
Jinhe Bi, Danqi Yan, Yifan Wang +8
Recent Large Reasoning Models significantly improve the reasoning ability of Large Language Models by learning to reason, exhibiting the promising performance in solving complex ta…
An Empirical Study of Federated Prompt Learning for Vision Language Model
Zhihao Wang, Wenke Huang, Tian Chen +7
The Vision Language Model (VLM) excels in aligning vision and language representations, and prompt learning has emerged as a key technique for adapting such models to downstream ta…
ThanoRA: Task Heterogeneity-Aware Multi-Task Low-Rank Adaptation
Jian Liang, Wenke Huang, Xianda Guo +3
Low-Rank Adaptation (LoRA) is widely adopted for downstream fine-tuning of foundation models due to its efficiency and zero additional inference cost. Many real-world applications…
Adversarial Curriculum Graph-Free Knowledge Distillation for Graph Neural Networks
Yuang Jia, Xiaojuan Shan, Jun Xia +5
Data-free Knowledge Distillation (DFKD) is a method that constructs pseudo-samples using a generator without real data, and transfers knowledge from a teacher model to a student by…
LoRASculpt: Sculpting LoRA for Harmonizing General and Specialized Knowledge in Multimodal Large Language Models
Jian Liang, Wenke Huang, Guancheng Wan +2
While Multimodal Large Language Models (MLLMs) excel at generalizing across modalities and tasks, effectively adapting them to specific downstream tasks while simultaneously retain…
3D Human Interaction Generation: A Survey
Siyuan Fan, Wenke Huang, Xiantao Cai +1
3D human interaction generation has emerged as a key research area, focusing on producing dynamic and contextually relevant interactions between humans and various interactive enti…
Privacy-Enhancing Paradigms within Federated Multi-Agent Systems
Zitong Shi, Guancheng Wan, Wenke Huang +4
LLM-based Multi-Agent Systems (MAS) have proven highly effective in solving complex problems by integrating multiple agents, each performing different roles. However, in sensitive…
Keeping Yourself is Important in Downstream Tuning Multimodal Large Language Model
Wenke Huang, Jian Liang, Xianda Guo +14
Multi-modal Large Language Models (MLLMs) integrate visual and linguistic reasoning to address complex tasks such as image captioning and visual question answering. While MLLMs dem…
A Survey of Safety on Large Vision-Language Models: Attacks, Defenses and Evaluations
Mang Ye, Xuankun Rong, Wenke Huang +3
With the rapid advancement of Large Vision-Language Models (LVLMs), ensuring their safety has emerged as a crucial area of research. This survey provides a comprehensive analysis o…
Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning
Wenke Huang, Jian Liang, Zekun Shi +6
Multimodal Large Language Model (MLLM) have demonstrated strong generalization capabilities across diverse distributions and tasks, largely due to extensive pre-training datasets.…
SURDS: Benchmarking Spatial Understanding and Reasoning in Driving Scenarios with Vision Language Models
Xianda Guo, Ruijun Zhang, Yiqun Duan +7
Accurate spatial reasoning in outdoor environments - covering geometry, object pose, and inter-object relationships - is fundamental to downstream tasks such as mapping, motion for…
FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference
Zihan Tan, Guancheng Wan, Wenke Huang +1
Personalized Federated Graph Learning (pFGL) facilitates the decentralized training of Graph Neural Networks (GNNs) without compromising privacy while accommodating personalized re…