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20242026
most citedMitigating Object Hallucinations in Large Vision-Language Models with Assembly of Global and Local Attention

2 citations · 3 across the 10 of their papers we have counts for

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6 papers · 1 filter

cs.LG2024

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…

cs.CV2024★ 1 cited

Knowledge Acquisition Disentanglement for Knowledge-based Visual Question Answering with Large Language Models

Wenbin An, Feng Tian, Jiahao Nie +7

Knowledge-based Visual Question Answering (KVQA) requires both image and world knowledge to answer questions. Current methods first retrieve knowledge from the image and external k…

cs.CV2024★ 2 cited

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…

cs.CV2024

MMRel: Benchmarking Relation Understanding in Multi-Modal Large Language Models

Jiahao Nie, Gongjie Zhang, Wenbin An +4

Though Multi-modal Large Language Models (MLLMs) have recently achieved significant progress, they often struggle to understand diverse and complicated inter-object relations. Spec…

cs.CV2024

Color Space Learning for Cross-Color Person Re-Identification

Jiahao Nie, Shan Lin, Alex C. Kot

The primary color profile of the same identity is assumed to remain consistent in typical Person Re-identification (Person ReID) tasks. However, this assumption may be invalid in r…

cs.CV2024

Cross-Domain Few-Shot Segmentation via Iterative Support-Query Correspondence Mining

Jiahao Nie, Yun Xing, Gongjie Zhang +5

Cross-Domain Few-Shot Segmentation (CD-FSS) poses the challenge of segmenting novel categories from a distinct domain using only limited exemplars. In this paper, we undertake a co…