8 papers
Are Multimodal Large Language Models Good Annotators for Image Tagging?
Ming-Kun Xie, Jia-Hao Xiao, Zhiqiang Kou +3
Image tagging, a fundamental vision task, traditionally relies on human-annotated datasets to train multi-label classifiers, which incurs significant labor and costs. While Multimo…
From Coefficients to Directions: Rethinking Model Merging with Directional Alignment
Zhikang Chen, Sen Cui, Deheng Ye +5
Model merging has emerged as a practical paradigm for integrating multiple independently trained models into a single model without joint retraining. Previous studies have demonstr…
Rethinking Toxicity Evaluation in Large Language Models: A Multi-Label Perspective
Zhiqiang Kou, Junyang Chen, Xin-Qiang Cai +8
Large language models (LLMs) have achieved impressive results across a range of natural language processing tasks, but their potential to generate harmful content has raised seriou…
Rethinking Consistent Multi-Label Classification Under Inexact Supervision
Wei Wang, Tianhao Ma, Ming-Kun Xie +2
Partial multi-label learning and complementary multi-label learning are two popular weakly supervised multi-label classification paradigms that aim to alleviate the high annotation…
Accessible, Realistic, and Fair Evaluation of Positive-Unlabeled Learning Algorithms
Wei Wang, Dong-Dong Wu, Ming Li +3
Positive-unlabeled (PU) learning is a weakly supervised binary classification problem, in which the goal is to learn a binary classifier from only positive and unlabeled data, with…
What Makes "Good" Distractors for Object Hallucination Evaluation in Large Vision-Language Models?
Ming-Kun Xie, Jia-Hao Xiao, Gang Niu +4
Large Vision-Language Models (LVLMs), empowered by the success of Large Language Models (LLMs), have achieved impressive performance across domains. Despite the great advances in L…