2 citations · 2 across the 11 of their papers we have counts for
11 papers
Does Forgetting Transfer Across Modalities? A Real-World Benchmark for Cross-Modal Knowledge Unlearning Evaluation
Chunlin Liu, Junnian Chen, Haitong Jiang +7
Vision-Language Models (VLMs), like Large Language Models (LLMs), may memorize sensitive, copyrighted, or harmful knowledge from their pretraining corpora. Removing such knowledge…
KnowHal: A Knowledge-Driven Benchmark for Comprehensive Multimodal Hallucination Evaluation
Ruihan Li, Jiyang Tan, Kailin Jiang +5
Hallucination remains a critical challenge for developing trustworthy Multimodal Large Language Models (MLLMs). While existing benchmarks mainly focus on entity, attribute, and rel…
Can Multimodal Large Language Models Understand OCT?
Baochen Fu, Wenzhi Deng, Baihao Jin +5
Optical coherence tomography (OCT) imaging is essential for the diagnosis and treatment of retinal diseases. Although multimodal large language models (MLLMs) have demonstrated con…
Delineating Knowledge Boundaries for Honest Large Vision-Language Models
Junru Song, Yimeng Hu, Yijing Chen +4
Large Vision-Language Models (VLMs) have achieved remarkable multimodal performance yet remain prone to factual hallucinations, particularly in long-tail or specialized domains. Mo…
MMKU-Bench: A Multimodal Update Benchmark for Diverse Visual Knowledge
Baochen Fu, Yuntao Du, Cheng Chang +6
As real-world knowledge continues to evolve, the parametric knowledge acquired by multimodal models during pretraining becomes increasingly difficult to remain consistent with real…
MINED: Probing and Updating with Multimodal Time-Sensitive Knowledge for Large Multimodal Models
Kailin Jiang, Ning Jiang, Yuntao Du +8
Large Multimodal Models (LMMs) encode rich factual knowledge via cross-modal pre-training, yet their static representations struggle to maintain an accurate understanding of time-s…
KORE: Enhancing Knowledge Injection for Large Multimodal Models via Knowledge-Oriented Controls
Kailin Jiang, Hongbo Jiang, Ning Jiang +7
Large Multimodal Models encode extensive factual knowledge in their pre-trained weights. However, its knowledge remains static and limited, unable to keep pace with real-world deve…
When Large Multimodal Models Confront Evolving Knowledge: Challenges and Explorations
Kailin Jiang, Yuntao Du, Yukai Ding +7
Large Multimodal Models (LMMs) store vast amounts of pretrained knowledge but struggle to remain aligned with real-world updates, making it difficult to avoid capability degradatio…
MMKE-Bench: A Multimodal Editing Benchmark for Diverse Visual Knowledge
Yuntao Du, Kailin Jiang, Zhi Gao +4
Knowledge editing techniques have emerged as essential tools for updating the factual knowledge of large language models (LLMs) and multimodal models (LMMs), allowing them to corre…
VideoAgent: A Memory-augmented Multimodal Agent for Video Understanding
Yue Fan, Xiaojian Ma, Rujie Wu +4
We explore how reconciling several foundation models (large language models and vision-language models) with a novel unified memory mechanism could tackle the challenging video und…
CLOVA: A Closed-Loop Visual Assistant with Tool Usage and Update
Zhi Gao, Yuntao Du, Xintong Zhang +4
Utilizing large language models (LLMs) to compose off-the-shelf visual tools represents a promising avenue of research for developing robust visual assistants capable of addressing…