38 citations · 83 across the 23 of their papers we have counts for
28 papers
I-KAN: Interpretable Intra- and Inter-Modal Multi-Pathway Representation Learning for Multimodal Sentiment Analysis
Xiaoting Xie, Feng Qiao, Xianwei Zhang +5
Multimodal sentiment analysis (MSA) aims to infer sentiment by integrating linguistic, acoustic, and visual information. However, many fusion methods integrate modality-specific re…
A Comprehensive Review of Multimodal Facial State Analysis: Tasks, Methods, and Resources
Xuri Ge, Tianshuo Zhang, Ruihan Li +6
Facial state analysis plays a crucial role in understanding human expressions, psychological modeling, and human computer interaction. Traditional unimodal vision-based methods are…
Recommender System as Slow and Fast Thinkers
Zichen Yuan, Xiaoxuan Dong, Linkun Dai +8
Sequential recommendation models are foundational to modern personalized services, yet their effectiveness varies substantially across heterogeneous user environments. In particula…
Role of Personality in Conversational Information Seeking
Abdisalam Abukar, Junchen Fu, Chengli Zhai +1
Large language models (LLMs) are increasingly used for information seeking, where users find, compare, and evaluate information through dialogue. In this role, the assistant does m…
Do We Really Need Multimodal Emotion Language Models Larger Than 1B Parameters?
Kaiwen Zheng, Junchen Fu, Wenhao Deng +3
Recent advances in multimodal large language models (MLLMs) have significantly improved the performance of multimodal emotion recognition (MER) and enabled interpretable descriptio…
RecRec: Latent Interests Recursive Reasoning for Sequential Recommendation
Wenhao Deng, Junchen Fu, Hanwen Du +6
Sequential recommender systems rely on a single forward pass to encode user interaction histories and predict the next item. Increasing inference-time computation through latent re…
Stream-aware Side Adaptation for Large Pre-trained Multimodal Embedding Models in Sequential Recommendation
Junchen Fu, Kaiwen Zheng, Ioannis Arapakis +4
Recently, large pretrained multimodal embedding models such as Qwen3-VL Embedding have shown strong promise for sequential recommendation, as they provide reusable semantic item re…
FrED: External Data Influence Estimation via Domain Knowledge Graph Grounding
Theodoros Aivalis, Iraklis A. Klampanos, Antonis Troumpoukis +1
The rapid deployment of generative AI has amplified the critical need for Training Data Attribution to ensure transparency and accountability. However, current parametric approache…
The 2nd EReL@MIR Workshop on Efficient Representation Learning for Multimodal Information Retrieval
Junchen Fu, Xuri Ge, Xin Xin +6
Multimodal representation learning has attracted increasing attention in AI, driven by the strong performance of large, pretrained multimodal foundation models such as Qwen, LLaVA,…
Benchmarking Multimodal Large Language Models for Missing Modality Completion in Product Catalogues
Junchen Fu, Wenhao Deng, Kaiwen Zheng +5
Missing-modality information on e-commerce platforms, such as absent product images or textual descriptions, often arises from annotation errors or incomplete metadata, impairing b…
Differentiable Semantic ID for Generative Recommendation
Junchen Fu, Xuri Ge, Alexandros Karatzoglou +4
Generative recommendation provides a novel paradigm in which each item is represented by a discrete semantic ID (SID) learned from rich content. Most existing methods treat SIDs as…
Are Multimodal Embeddings Truly Beneficial for Recommendation? A Deep Dive into Whole vs. Individual Modalities
Yu Ye, Junchen Fu, Yu Song +2
Multimodal recommendation has emerged as a mainstream paradigm, typically leveraging text and visual embeddings extracted from pre-trained models such as Sentence-BERT, Vision Tran…
PromptAug: Fine-grained Conflict Classification Using Data Augmentation
Oliver Warke, Joemon M. Jose, Faegheh Hasibi +1
Given the rise of conflicts on social media, effective classification models to detect harmful behaviours are essential. Following the garbage-in-garbage-out maxim, machine learnin…
Beyond One-Size-Fits-All: A Study of Neural and Behavioural Variability Across Different Recommendation Categories
Georgios Koutroumpas, Sebastian Idesis, Mireia Masias Bruns +4
Traditionally, Recommender Systems (RS) have primarily measured performance based on the accuracy and relevance of their recommendations. However, this algorithmic-centric approach…
The 1st EReL@MIR Workshop on Efficient Representation Learning for Multimodal Information Retrieval
Junchen Fu, Xuri Ge, Xin Xin +5
Multimodal representation learning has garnered significant attention in the AI community, largely due to the success of large pre-trained multimodal foundation models like LLaMA,…
Multimodal Representation Learning Techniques for Comprehensive Facial State Analysis
Kaiwen Zheng, Xuri Ge, Junchen Fu +2
Multimodal foundation models have significantly improved feature representation by integrating information from multiple modalities, making them highly suitable for a broader set o…
CROSSAN: Towards Efficient and Effective Adaptation of Multiple Multimodal Foundation Models for Sequential Recommendation
Junchen Fu, Yongxin Ni, Joemon M. Jose +4
In this paper, we explore a less-studied yet practically important problem: how to efficiently and effectively adapt multiple (2) multimodal foundation models (MFMs) for the seq…
LLMPopcorn: Exploring LLMs as Assistants for Popular Micro-video Generation
Junchen Fu, Xuri Ge, Kaiwen Zheng +5
In an era where micro-videos dominate platforms like TikTok and YouTube, AI-generated content is nearing cinematic quality. The next frontier is using large language models (LLMs)…
Large Language Model driven Policy Exploration for Recommender Systems
Jie Wang, Alexandros Karatzoglou, Ioannis Arapakis +1
Recent advancements in Recommender Systems (RS) have incorporated Reinforcement Learning (RL), framing the recommendation as a Markov Decision Process (MDP). However, offline RL po…
R^3AG: First Workshop on Refined and Reliable Retrieval Augmented Generation
Zihan Wang, Xuri Ge, Joemon M. Jose +4
Retrieval-augmented generation (RAG) has gained wide attention as the key component to improve generative models with external knowledge augmentation from information retrieval. It…
Efficient and Effective Adaptation of Multimodal Foundation Models in Sequential Recommendation
Junchen Fu, Xuri Ge, Xin Xin +5
Multimodal foundation models (MFMs) have revolutionized sequential recommender systems through advanced representation learning. While Parameter-efficient Fine-tuning (PEFT) is com…
Towards End-to-End Explainable Facial Action Unit Recognition via Vision-Language Joint Learning
Xuri Ge, Junchen Fu, Fuhai Chen +3
Facial action units (AUs), as defined in the Facial Action Coding System (FACS), have received significant research interest owing to their diverse range of applications in facial…
Hire: Hybrid-modal Interaction with Multiple Relational Enhancements for Image-Text Matching
Xuri Ge, Fuhai Chen, Songpei Xu +3
Image-text matching (ITM) is a fundamental problem in computer vision. The key issue lies in jointly learning the visual and textual representation to estimate their similarity acc…
Detail-Enhanced Intra- and Inter-modal Interaction for Audio-Visual Emotion Recognition
Tong Shi, Xuri Ge, Joemon M. Jose +2
Capturing complex temporal relationships between video and audio modalities is vital for Audio-Visual Emotion Recognition (AVER). However, existing methods lack attention to local…