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 …

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 …

Traditional supervised learning heavily relies on human-annotated datasets, especially in
data-hungry neural approaches. However, various tasks, especially multi-label tasks like …

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 …

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 …

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

This thesis introduces various methods for tackling the challenge of multi-label image
classification in different scenarios, including partial labels and free-annotation settings …