10 citations · 10 across the 11 of their papers we have counts for
14 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…
Target-Aware Calibration Data Selection for Preserving Uncertainty in Quantized Language Models
Zhen Yang, Sizai Hou, Kaiwen Zheng +4
Quantization is widely used to deploy large language models, but its effect on uncertainty behavior, such as confidence, margins, and abstention, is rarely treated as a primary obj…
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…
Training-Free Test-Time Contrastive Learning for Large Language Models
Kaiwen Zheng, Kai Zhou, Jinwu Hu +3
Large language models (LLMs) demonstrate strong reasoning capabilities, but their performance often degrades under distribution shift. Existing test-time adaptation (TTA) methods r…
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…
Focal-RegionFace: Generating Fine-Grained Multi-attribute Descriptions for Arbitrarily Selected Face Focal Regions
Kaiwen Zheng, Junchen Fu, Songpei Xu +4
In this paper, we introduce an underexplored problem in facial analysis: generating and recognizing multi-attribute natural language descriptions, containing facial action units (A…
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…
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)…
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…