To reduce the discrepancy between the source and target domains, a new multi-label
adaptation network (ML-ANet) based on multiple kernel variants with maximum mean …

In this paper, a discriminator-free adversarial-based Unsupervised Domain Adaptation
(UDA) for Multi-Label Image Classification (MLIC) referred to as DDA-MLIC is proposed …

Deep learning (DL) methods are promising for the multilabel aerial image classification
(MAIC) task. However, current DL methods face a common problem: the need for large …

The multi-label image classification problem is one of the most important problems in the
field of computer vision, which needs to predict and output all the labels in an image …

Multi-label image classification is a fundamental but challenging task in computer vision. To
tackle the problem, the label-related semantic information is often exploited, but the …

Multi-label image classification is a challenging problem in computer vision. Motivated by
the recent development in image classification performance using Deep Neural Networks, in …

Most publicly available datasets for image classification are with single labels, while images
are inherently multilabeled in our daily life. Such an annotation gap makes many pretrained …

In this work, we propose a two-branch neural network architecture for multi-label
classification in UAV imagery. Compared to single-label classification, the multi-label …

Multi-label image classification is an essential task in image processing. How to improve the
correlation between labels by learning multi-scale features from images is a very …

Multi-label image classification is a prediction task that aims to identify more than one label
from a given image. This paper considers the semantic consistency of the latent space …