11 citations · 39 across the 32 of their papers we have counts for
19 papers · 1 filter
NeuSpring: Neural Spring Fields for Reconstruction and Simulation of Deformable Objects from Videos
Qingshan Xu, Jiao Liu, Shangshu Yu +6
In this paper, we aim to create physical digital twins of deformable objects under interaction. Existing methods focus more on the physical learning of current state modeling, but…
MazeMate: An LLM-Powered Chatbot to Support Computational Thinking in Gamified Programming Learning
Chenyu Hou, Hua Yu, Gaoxia Zhu +3
Computational Thinking (CT) is a foundational problem-solving skill, and gamified programming environments are a widely adopted approach to cultivating it. While large language mod…
Distributional Multi-objective Black-box Optimization for Diffusion-model Inference-time Multi-Target Generation
Kim Yong Tan, Yueming Lyu, Ivor Tsang +1
Diffusion models have been successful in learning complex data distributions. This capability has driven their application to high-dimensional multi-objective black-box optimizatio…
Evolutionary Computation as Natural Generative AI
Yaxin Shi, Abhishek Gupta, Ying Wu +7
Generative AI (GenAI) has achieved remarkable success across a range of domains, but its capabilities remain constrained to statistical models of finite training sets and learning…
Lang-PINN: From Language to Physics-Informed Neural Networks via a Multi-Agent Framework
Xin He, Liangliang You, Hongduan Tian +3
Physics-informed neural networks (PINNs) provide a powerful approach for solving partial differential equations (PDEs), but constructing a usable PINN remains labor-intensive and e…
EvoSpeak: Large Language Models for Interpretable Genetic Programming-Evolved Heuristics
Meng Xu, Jiao Liu, Yew Soon Ong
Genetic programming (GP) has demonstrated strong effectiveness in evolving tree-structured heuristics for complex optimization problems. Yet, in dynamic and large-scale scenarios,…
Lightweight and Accurate Multi-View Stereo with Confidence-Aware Diffusion Model
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong +1
To reconstruct the 3D geometry from calibrated images, learning-based multi-view stereo (MVS) methods typically perform multi-view depth estimation and then fuse depth maps into a…
RouteMark: A Fingerprint for Intellectual Property Attribution in Routing-based Model Merging
Xin He, Junxi Shen, Zhenheng Tang +4
Model merging via Mixture-of-Experts (MoE) has emerged as a scalable solution for consolidating multiple task-specific models into a unified sparse architecture, where each expert…
A Plug-and-Play Multi-Criteria Guidance for Diverse In-Betweening Human Motion Generation
Hua Yu, Jiao Liu, Xu Gui +3
In-betweening human motion generation aims to synthesize intermediate motions that transition between user-specified keyframes. In addition to maintaining smooth transitions, a cru…
Pushing the Limits of Safety: A Technical Report on the ATLAS Challenge 2025
Zonghao Ying, Siyang Wu, Run Hao +44
Multimodal Large Language Models (MLLMs) have enabled transformative advancements across diverse applications but remain susceptible to safety threats, especially jailbreak attacks…
LLM-to-Phy3D: Physically Conform Online 3D Object Generation with LLMs
Melvin Wong, Yueming Lyu, Thiago Rios +2
The emergence of generative artificial intelligence (GenAI) and large language models (LLMs) has revolutionized the landscape of digital content creation in different modalities. H…
SiamNAS: Siamese Surrogate Model for Dominance Relation Prediction in Multi-objective Neural Architecture Search
Yuyang Zhou, Ferrante Neri, Yew-Soon Ong +1
Modern neural architecture search (NAS) is inherently multi-objective, balancing trade-offs such as accuracy, parameter count, and computational cost. This complexity makes NAS com…
MermaidFlow: Redefining Agentic Workflow Generation via Safety-Constrained Evolutionary Programming
Chengqi Zheng, Jianda Chen, Yueming Lyu +5
Despite the promise of autonomous agentic reasoning, existing workflow generation methods frequently produce fragile, unexecutable plans due to unconstrained LLM-driven constructio…
Dynamic-Aware Video Distillation: Optimizing Temporal Resolution Based on Video Semantics
Yinjie Zhao, Heng Zhao, Bihan Wen +2
With the rapid development of vision tasks and the scaling on datasets and models, redundancy reduction in vision datasets has become a key area of research. To address this issue,…
Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis
Yu Hua, Weiming Liu, Gui Xu +3
Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation. Recent score-based generative models (SGMs) h…
Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation
Kim Yong Tan, Yueming Lyu, Ivor Tsang +1
Guided diffusion-model generation is a promising direction for customizing the generation process of a pre-trained diffusion model to address specific downstream tasks. Existing gu…
Mastering Continual Reinforcement Learning through Fine-Grained Sparse Network Allocation and Dormant Neuron Exploration
Chengqi Zheng, Haiyan Yin, Jianda Chen +3
Continual Reinforcement Learning (CRL) is essential for developing agents that can learn, adapt, and accumulate knowledge over time. However, a fundamental challenge persists as ag…
()-Parametric Multi-Task Optimization: Joint Search in Solution and Infinite Task Spaces
Tingyang Wei, Jiao Liu, Abhishek Gupta +2
Multi-task optimization is typically characterized by a fixed and finite set of tasks. The present paper relaxes this condition by considering a non-fixed and potentially infinite…
Evolutionary Optimization of Physics-Informed Neural Networks: Evo-PINN Frontiers and Opportunities
Jian Cheng Wong, Abhishek Gupta, Chin Chun Ooi +3
Deep learning models trained on finite data lack a complete understanding of the physical world. On the other hand, physics-informed neural networks (PINNs) are infused with such k…