5 citations · 8 across the 16 of their papers we have counts for
17 papers
Beyond Fixed Visual History: Learning Motion-Adaptive Temporal Receptive Fields for Complex-Terrain Quadruped Locomotion
Hao Zhang, Yuping Yan, Yitian Hong +5
Versatile quadruped locomotion depends on a tight coupling between what the robot perceives and what its body can execute. Yet existing visual controllers typically organize depth…
IP Protection in the Era of Visual Generative AI: A Survey
Zhuan Shi, Shunchang Liu, Alireza Dehghanpour Farashah +8
The rapid evolution of visual generative AI has introduced a wide range of intellectual property risks, spanning the unauthorized learning, reproduction, extraction, misuse, and re…
Rethinking Attention Locality in Spiking Transformers
Zeqi Zheng, Zizheng Zhu, Yuping Yan +3
Spiking Transformers provide a promising paradigm for efficient visual processing with spike-driven computation, yet their Softmax-free Spiking Self-Attention (SSA) struggles to es…
HMACE: Heterogeneous Multi-Agent Collaborative Evolution for Combinatorial Optimization
Yuping Yan, Jirui Han, Fei Ming +2
Large Language Models have recently emerged as a promising paradigm for automated heuristic design for NP-hard combinatorial optimization problems. Despite this progress, existing…
STRONG-VLA: Decoupled Robustness Learning for Vision-Language-Action Models under Multimodal Perturbations
Yuhan Xie, Yuping Yan, Yunqi Zhao +2
Despite their strong performance in embodied tasks, recent Vision-Language-Action (VLA) models remain highly fragile under multimodal perturbations, where visual corruption and lin…
IRIS: Implicit Reward-Guided Internal Sifting for Mitigating Multimodal Hallucination
Yuanshuai Li, Yuping Yan, Jirui Han +3
Hallucination remains a fundamental challenge for Multimodal Large Language Models (MLLMs). While Direct Preference Optimization (DPO) is a key alignment framework, existing approa…
Adversarial Robustness in Zero-Shot Learning:An Empirical Study on Class and Concept-Level Vulnerabilities
Zhiyuan Peng, Zihan Ye, Shreyank N Gowda +3
Zero-shot Learning (ZSL) aims to enable image classifiers to recognize images from unseen classes that were not included during training. Unlike traditional supervised classificati…
When Alignment Fails: Multimodal Adversarial Attacks on Vision-Language-Action Models
Yuping Yan, Yuhan Xie, Yixin Zhang +3
Vision-Language-Action models (VLAs) have recently demonstrated remarkable progress in embodied environments, enabling robots to perceive, reason, and act through unified multimoda…
OutSafe-Bench: A Benchmark for Multimodal Offensive Content Detection in Large Language Models
Yuping Yan, Yuhan Xie, Yuanshuai Li +3
Since Multimodal Large Language Models (MLLMs) are increasingly being integrated into everyday tools and intelligent agents, growing concerns have arisen regarding their possible o…
Mitigating Visual Hallucinations via Semantic Curriculum Preference Optimization in MLLMs
Yuanshuai Li, Yuping Yan, Junfeng Tang +3
Multimodal Large Language Models (MLLMs) have significantly improved the performance of various tasks, but continue to suffer from visual hallucinations, a critical issue where gen…
TriCon-SF: A Triple-Shuffle and Contribution-Aware Serial Federated Learning Framework for Heterogeneous Healthcare Data
Yuping Yan, Yizhi Wang, Yuanshuai Li +1
Serial pipeline training is an efficient paradigm for handling data heterogeneity in cross-silo federated learning with low communication overhead. However, even without centralize…
Position Paper: Unlocking the Potential of AI Researchers in Scientific Discovery-What Is Missing?
Hengjie Yu, Shuya Liu, Haiyun Yang +3
The potential of AI researchers in scientific discovery remains largely untapped. Over the past decade, AI for Science (AI4Science) publications in 145 Nature Index journals have i…
Think Small, Plan Smart: Minimalist Symbolic Abstraction and Heuristic Subspace Search for LLM-Guided Task Planning
Junfeng Tang, Yuping Yan, Zihan Ye +4
Reliable task planning is pivotal for achieving long-horizon autonomy in real-world robotic systems. Large language models (LLMs) offer a promising interface for translating comple…
IP-RSNN: Bi-level Intrinsic Plasticity Enables Learning-to-learn in Recurrent Spiking Neural Networks
Yingchao Yu, Yaochu Jin, Kuangrong Hao +5
Learning-to-learn (L2L), defined as progressively faster learning across similar tasks, is fundamental to both neuroscience and artificial intelligence. However, its neural basis r…
Privacy-preserving Quantification of Non-IID Degree in Federated Learning
Yuping Yan, Yizhi Wang, Yingchao Yu +1
Federated learning (FL) offers a privacy-preserving approach to machine learning for multiple collaborators without sharing raw data. However, the existence of non-independent and…
Heterogeneous Federated Learning with Convolutional and Spiking Neural Networks
Yingchao Yu, Yuping Yan, Jisong Cai +1
Federated learning (FL) has emerged as a promising paradigm for training models on decentralized data while safeguarding data privacy. Most existing FL systems, however, assume tha…
HE-DKSAP: Privacy-Preserving Stealth Address Protocol via Additively Homomorphic Encryption
Yuping Yan, George Shao, Dennis Song +2
Blockchain transactions have gained widespread adoption across various industries, largely attributable to their unparalleled transparency and robust security features. Nevertheles…