Addressing issues related to multi-label classification is relevant in many fields of
applications. In this work. We present a multi-label classification architecture based on Multi …

While deep convolutional neural networks (CNNs) have shown a great success in single-
label image classification, it is important to note that most real world images contain multiple …

We propose a recurrent neural network (RNN) based model for image multi-label
classification. Our model uniquely integrates and learning of visual attention and Long Short …

Multi-label image classification problem is one of the most important and fundamental
problems in computer vision. In an image with multiple labels, the objects usually locate at …

Multilabel image classification aims to assign images to multiple possible labels. In this task,
each image may be associated with multiple labels, making it more challenging than the …

This paper proposes a multi-task deep neural network (MT-DNN) architecture to handle the
multi-label learning problem, in which each label learning is defined as a binary …

Deep convolution neural networks (CNNs) have demonstrated advanced performance on
single-label image classification, and various progress also has been made to apply CNN …

Learning the correlation among labels is a standing-problem in the multi-label image
recognition task. The label correlation is the key to solve the multi-label classification but it is …

Convolutional Neural Network (CNN) has demonstrated promising performance in single-
label image classification tasks. However, how CNN best copes with multi-label images still …

Compared with single-label image classification, multi-label image classification is more
practical and challenging. Some recent studies attempted to leverage the semantic …