Abstract Recently lightweight Vision Transformers (ViTs) demonstrate superior performance
and lower latency compared with lightweight Convolutional Neural Networks (CNNs) on …

Collecting large-scale multi-label data with full labels is difficult for real-world scenarios.
Many existing studies have tried to address the issue of missing labels caused by annotation …

Recently, large vision-language models (LVLMs) have rapidly gained popularity for their
strong generation and reasoning capabilities given diverse multimodal inputs. However …

Recently, large-scale visual language pre-trained (VLP) models have demonstrated
impressive performance across various downstream tasks. Motivated by these …

In reality, data often exhibit associations with multiple labels, making multi-label learning
(MLL) become a prominent research topic. The last two decades have witnessed the …

Large pre-trained vision-language models like CLIP have shown amazing zero-shot
recognition performance. To adapt pre-trained vision-language models to downstream …

Multimodal Large Language Models (MLLMs) have recently demonstrated strong
performance across a wide range of vision-language tasks, garnering significant attention in …

Although semi-supervised learning has made significant advances in the field of medical
image segmentation, fully annotating a volumetric sample slice by slice remains a costly and …

In multi-label classification, each training instance is associated with multiple class labels
simultaneously. Unfortunately, collecting the fully precise class labels for each training …

Multi-label learning (MLL) requires comprehensive multi-semantic annotations that is hard to
fully obtain, thus often resulting in missing labels scenarios. In this paper, we investigate …