This paper presents a CLIP-based unsupervised learning method for annotation-free multi-
label image classification, including three stages: initialization, training, and inference. At the …

Partially-supervised multi-organ medical image segmentation aims to develop a unified
semantic segmentation model by utilizing multiple partially-labeled datasets, with each …

Accurate segmentation of power lines in various aerial images is very important for UAV
flight safety. The complex background and very thin structures of power lines, however …

Multi-label image classification aims to predict all possible labels in an image. It is usually
formulated as a partial-label learning problem, since it could be expensive in practice to …

Annotating data for multi-label classification is prohibitively expensive because every
category of interest must be confirmed to be present or absent. Recent work on single …

Partial Label Learning (PLL) grapples with learning from ambiguously labelled data, and it
has been successfully applied in fields such as image recognition. Nevertheless, traditional …

This paper proposes a novel framework for multi-label image recognition without any
training data, called data-free framework, which uses knowledge of pre-trained Large …

The scale of parallel corpus plays an important role in training high-quality neural machine
translation models. In order to expand the scale of parallel corpus in low-resource scenarios …

UNet has rapidly become the architecture of choice for precise real-time semantic
segmentation, which is crucial in medical diagnostics and autonomous navigation …

Deep learning, especially through Convolutional Neural Networks (CNNs), has
revolutionized image analysis. Image classification, which involves assigning labels to …