10 citations · 14 across the 7 of their papers we have counts for
8 papers
Empowering Multimodal LLMs with External Tools: A Comprehensive Survey
Wenbin An, Jiahao Nie, Yaqiang Wu +3
By integrating the perception capabilities of multimodal encoders with the generative power of Large Language Models (LLMs), Multimodal Large Language Models (MLLMs), exemplified b…
A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future
Shilin Sun, Wenbin An, Feng Tian +5
Artificial intelligence (AI) has rapidly developed through advancements in computational power and the growth of massive datasets. However, this progress has also heightened challe…
Unleashing the Potential of Model Bias for Generalized Category Discovery
Wenbin An, Haonan Lin, Jiahao Nie +5
Generalized Category Discovery is a significant and complex task that aims to identify both known and undefined novel categories from a set of unlabeled data, leveraging another la…
Mitigating Object Hallucinations in Large Vision-Language Models with Assembly of Global and Local Attention
Wenbin An, Feng Tian, Sicong Leng +6
Despite great success across various multimodal tasks, Large Vision-Language Models (LVLMs) often encounter object hallucinations with generated textual responses being inconsisten…
Transfer and Alignment Network for Generalized Category Discovery
Wenbin An, Feng Tian, Wenkai Shi +4
Generalized Category Discovery is a crucial real-world task. Despite the improved performance on known categories, current methods perform poorly on novel categories. We attribute…
Generalized Category Discovery with Large Language Models in the Loop
Wenbin An, Wenkai Shi, Feng Tian +8
Generalized Category Discovery (GCD) is a crucial task that aims to recognize both known and novel categories from a set of unlabeled data by utilizing a few labeled data with only…
A Diffusion Weighted Graph Framework for New Intent Discovery
Wenkai Shi, Wenbin An, Feng Tian +3
New Intent Discovery (NID) aims to recognize both new and known intents from unlabeled data with the aid of limited labeled data containing only known intents. Without considering…
DNA: Denoised Neighborhood Aggregation for Fine-grained Category Discovery
Wenbin An, Feng Tian, Wenkai Shi +4
Discovering fine-grained categories from coarsely labeled data is a practical and challenging task, which can bridge the gap between the demand for fine-grained analysis and the hi…