5 citations · 8 across the 16 of their papers we have counts for
8 papers · 1 filter
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…