14 citations · 46 across the 21 of their papers we have counts for
22 papers
Conversational Human Audio-visual Talking Dialogue Generation
Junhao Song, Lluis Guasch, Xilin He +8
Large-scale dyadic interactive audio-visual dialogue (DIAD) datasets provide fundamental data resources for developing humanoid interactive virtual agents and digital humans. Howev…
Scaling with Confidence: Calibrating Confidence of LLMs for Adaptive Test Time Scaling
Xuqing Yang, Yi Yuan, Shanzhe Lei +1
Training large language models (LLMs) with reinforcement learning (RL) has significantly advanced their performance on reasoning and question-answering tasks. However, prevailing R…
Towards Trustworthy Report Generation: A Deep Research Agent with Progressive Confidence Estimation and Calibration
Yi Yuan, Xuhong Wang, Shanzhe Lei
As agent-based systems continue to evolve, deep research agents are capable of automatically generating research-style reports across diverse domains. While these agents promise to…
SVAgent: Storyline-Guided Long Video Understanding via Cross-Modal Multi-Agent Collaboration
Zhongyu Yang, Zuhao Yang, Shuo Zhan +3
Video question answering (VideoQA) is a challenging task that requires integrating spatial, temporal, and semantic information to capture the complex dynamics of video sequences. A…
MultiHaystack: Benchmarking Multimodal Retrieval and Reasoning over 40K Images, Videos, and Documents
Dannong Xu, Zhongyu Yang, Jun Chen +6
Multimodal large language models (MLLMs) achieve strong performance on benchmarks that evaluate text, image, or video understanding separately. However, these settings do not asses…
XR: Cross-Modal Agents for Composed Image Retrieval
Zhongyu Yang, Wei Pang, Yingfang Yuan
Retrieval is being redefined by agentic AI, demanding multimodal reasoning beyond conventional similarity-based paradigms. Composed Image Retrieval (CIR) exemplifies this shift as…
STProtein: predicting spatial protein expression from multi-omics data
Zhaorui Jiang, Yingfang Yuan, Lei Hu +1
The integration of spatial multi-omics data from single tissues is crucial for advancing biological research. However, a significant data imbalance impedes progress: while spatial…
SLOPE: Optimistic Potential Landscape Shaping for Model-based Reinforcement Learning
Yao-Hui Li, Zeyu Wang, Xin Li +7
Model-based reinforcement learning (MBRL) is sample-efficient but struggles in sparse reward settings. A critical bottleneck arises from the lack of informative gradients in sparse…
InEx: Hallucination Mitigation via Introspection and Cross-Modal Multi-Agent Collaboration
Zhongyu Yang, Yingfang Yuan, Xuanming Jiang +2
Hallucination remains a critical challenge in large language models (LLMs), hindering the development of reliable multimodal LLMs (MLLMs). Existing solutions often rely on human in…
Script: Graph-Structured and Query-Conditioned Semantic Token Pruning for Multimodal Large Language Models
Zhongyu Yang, Dannong Xu, Wei Pang +1
The rapid growth of visual tokens in multimodal large language models (MLLMs) leads to excessive memory consumption and inference latency, especially when handling high-resolution…
Mixture of Experts in Large Language Models
Danyang Zhang, Junhao Song, Ziqian Bi +5
This paper presents a comprehensive review of the Mixture-of-Experts (MoE) architecture in large language models, highlighting its ability to significantly enhance model performanc…
A Unifying Bias-aware Multidisciplinary Framework for Investigating Socio-Technical Issues
Sacha Hasan, Mehdi Rizvi, Yingfang Yuan +3
This paper aims to bring together the disciplines of social science (SS) and computer science (CS) in the design and implementation of a novel multidisciplinary framework for syste…
ScaleNet: Scale Invariance Learning in Directed Graphs
Qin Jiang, Chengjia Wang, Michael Lones +2
Graph Neural Networks (GNNs) have advanced relational data analysis but lack invariance learning techniques common in image classification. In node classification with GNNs, it is…
Quantifying the Cross-sectoral Intersecting Discrepancies within Multiple Groups Using Latent Class Analysis Towards Fairness
Yingfang Yuan, Kefan Chen, Mehdi Rizvi +2
The growing interest in fair AI development is evident. The ''Leave No One Behind'' initiative urges us to address multiple and intersecting forms of inequality in accessing servic…
Exploring Public Attention in the Circular Economy through Topic Modelling with Twin Hyperparameter Optimisation
Junhao Song, Yingfang Yuan, Kaiwen Chang +3
To advance the circular economy (CE), it is crucial to gain insights into the evolution of public attention, cognitive pathways of the masses concerning circular products, and to i…
SAIS: A Novel Bio-Inspired Artificial Immune System Based on Symbiotic Paradigm
Junhao Song, Yingfang Yuan, Wei Pang
We propose a novel type of Artificial Immune System (AIS): Symbiotic Artificial Immune Systems (SAIS), drawing inspiration from symbiotic relationships in biology. SAIS parallels t…
FACE: Evaluating Natural Language Generation with Fourier Analysis of Cross-Entropy
Zuhao Yang, Yingfang Yuan, Yang Xu +3
Measuring the distance between machine-produced and human language is a critical open problem. Inspired by empirical findings from psycholinguistics on the periodicity of entropy i…
PaCaNet: A Study on CycleGAN with Transfer Learning for Diversifying Fused Chinese Painting and Calligraphy
Zuhao Yang, Huajun Bai, Zhang Luo +5
AI-Generated Content (AIGC) has recently gained a surge in popularity, powered by its high efficiency and consistency in production, and its capability of being customized and dive…
Which Hyperparameters to Optimise? An Investigation of Evolutionary Hyperparameter Optimisation in Graph Neural Network For Molecular Property Prediction
Yingfang Yuan, Wenjun Wang, Wei Pang
Recently, the study of graph neural network (GNN) has attracted much attention and achieved promising performance in molecular property prediction. Most GNNs for molecular property…
A Genetic Algorithm with Tree-structured Mutation for Hyperparameter Optimisation of Graph Neural Networks
Yingfang Yuan, Wenjun Wang, Wei Pang
In recent years, graph neural networks (GNNs) have gained increasing attention, as they possess the excellent capability of processing graph-related problems. In practice, hyperpar…
A Systematic Comparison Study on Hyperparameter Optimisation of Graph Neural Networks for Molecular Property Prediction
Yingfang Yuan, Wenjun Wang, Wei Pang
Graph neural networks (GNNs) have been proposed for a wide range of graph-related learning tasks. In particular, in recent years, an increasing number of GNN systems were applied t…
A Novel Genetic Algorithm with Hierarchical Evaluation Strategy for Hyperparameter Optimisation of Graph Neural Networks
Yingfang Yuan, Wenjun Wang, George M. Coghill +1
Graph representation of structured data can facilitate the extraction of stereoscopic features, and it has demonstrated excellent ability when working with deep learning systems, t…