1 citations · 1 across the 7 of their papers we have counts for
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Next Generation Active Learning: Mixture of LLMs in the Loop
Yuanyuan Qi, Xiaohao Yang, Jueqing Lu +4
With the rapid advancement and strong generalization capabilities of large language models (LLMs), they have been increasingly incorporated into the active learning pipelines as an…
ALScope: A Unified Toolkit for Deep Active Learning
Chenkai Wu, Yuanyuan Qi, Xiaohao Yang +4
Deep Active Learning (DAL) reduces annotation costs by selecting the most informative unlabeled samples during training. As real-world applications become more complex, challenges…
DPL: Decoupled Prototype Learning for Enhancing Robustness of Vision-Language Transformers to Missing Modalities
Jueqing Lu, Yuanyuan Qi, Xiaohao Yang +8
The performance of Visio-Language Transformers drops sharply when an input modality (e.g., image) is missing, because the model is forced to make predictions using incomplete infor…
Multi-Label Bayesian Active Learning with Inter-Label Relationships
Yuanyuan Qi, Jueqing Lu, Xiaohao Yang +2
The primary challenge of multi-label active learning, differing it from multi-class active learning, lies in assessing the informativeness of an indefinite number of labels while a…
Navigating Conflicting Views: Harnessing Trust for Learning
Jueqing Lu, Wray Buntine, Yuanyuan Qi +3
Resolving conflicts is critical for improving the reliability of multi-view classification. While prior work focuses on learning consistent and informative representations across v…
Multi-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs
Jueqing Lu, Lan Du, Ming Liu +1
Few/Zero-shot learning is a big challenge of many classifications tasks, where a classifier is required to recognise instances of classes that have very few or even no training sam…