3 citations · 3 across the 8 of their papers we have counts for
8 papers
Fine-Grained Zero-Shot Composed Image Retrieval with Complementary Visual-Semantic Integration
Yongcong Ye, Kai Zhang, Yanghai Zhang +3
Zero-shot composed image retrieval (ZS-CIR) is a rapidly growing area with significant practical applications, allowing users to retrieve a target image by providing a reference im…
Layer-Order Inversion: Rethinking Latent Multi-Hop Reasoning in Large Language Models
Xukai Liu, Ye Liu, Jipeng Zhang +3
Large language models (LLMs) perform well on multi-hop reasoning, yet how they internally compose multiple facts remains unclear. Recent work proposes \emph{hop-aligned circuit hyp…
WDMIR: Wavelet-Driven Multimodal Intent Recognition
Weiyin Gong, Kai Zhang, Yanghai Zhang +4
Multimodal intent recognition (MIR) seeks to accurately interpret user intentions by integrating verbal and non-verbal information across video, audio and text modalities. While ex…
Self-Reflective Planning with Knowledge Graphs: Enhancing LLM Reasoning Reliability for Question Answering
Jiajun Zhu, Ye Liu, Meikai Bao +3
Recently, large language models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks, yet they remain prone to hallucinations when reasoning with i…
Know3-RAG: A Knowledge-aware RAG Framework with Adaptive Retrieval, Generation, and Filtering
Xukai Liu, Ye Liu, Shiwen Wu +4
Recent advances in large language models (LLMs) have led to impressive progress in natural language generation, yet their tendency to produce hallucinated or unsubstantiated conten…
Leveraging Entity Information for Cross-Modality Correlation Learning: The Entity-Guided Multimodal Summarization
Yanghai Zhang, Ye Liu, Shiwei Wu +4
The rapid increase in multimedia data has spurred advancements in Multimodal Summarization with Multimodal Output (MSMO), which aims to produce a multimodal summary that integrates…
Learn while Unlearn: An Iterative Unlearning Framework for Generative Language Models
Haoyu Tang, Ye Liu, Xi Zhao +5
Recent advances in machine learning, particularly in Natural Language Processing (NLP), have produced powerful models trained on vast datasets. However, these models risk leaking s…
Detect, Investigate, Judge and Determine: A Knowledge-guided Framework for Few-shot Fake News Detection
Ye Liu, Jiajun Zhu, Xukai Liu +5
Few-Shot Fake News Detection (FS-FND) aims to distinguish inaccurate news from real ones in extremely low-resource scenarios. This task has garnered increased attention due to the…