1 citations · 1 across the 5 of their papers we have counts for
6 papers
Dynamic Interaction-Aware and Causality-Disentangled Framework for Multimodal Sentiment Analysis
Guangyuan Dong, Ziwei Hong, Shenghao Liu +9
Although Multimodal Sentiment Analysis (MSA) effectively leverages rich information from language, visual, and acoustic modalities, existing methods still face two core challenges:…
Traffic-MLLM: Curiosity-Regularized Supervised Learning for Traffic Scenario Case-Based Reasoning
Waikit Xiu, Qiang Lu, Bingchen Liu +2
For safe and robust autonomous driving, decision-making systems must effectively leverage past experiences to handle the inherent long-tail of traffic scenarios. Case-Based Reasoni…
A Zero-shot Learning Method Based on Large Language Models for Multi-modal Knowledge Graph Embedding
Bingchen Liu, Jingchen Li, Yuanyuan Fang +1
Zero-shot learning (ZL) is crucial for tasks involving unseen categories, such as natural language processing, image classification, and cross-lingual transfer.Current applications…
Large Language Models for Knowledge Graph Embedding: A Survey
Bingchen Liu, Yuanyuan Fang, Naixing Xu +3
Large language models (LLMs) have garnered significant attention for their superior performance in many knowledge-driven applications on the world wide web.These models are designe…
Learn to Unlearn: Meta-Learning-Based Knowledge Graph Embedding Unlearning
Naixing Xu, Qian Li, Xu Wang +2
Knowledge graph (KG) embedding methods map entities and relations into continuous vector spaces, improving performance in tasks like link prediction and question answering. With ri…
Federated Knowledge Graph Unlearning via Diffusion Model
Bingchen Liu, Yuanyuan Fang
Federated learning (FL) promotes the development and application of artificial intelligence technologies by enabling model sharing and collaboration while safeguarding data privacy…