For the multi-label text classification problems with many classes, many existing multi-label
classification algorithms become infeasible or suffer an unaffordable cost. Some researches …

This paper contributes to cross-lingual image annotation and retrieval in terms of data and
baseline methods. We propose COCO-CN, a novel dataset enriching MS-COCO with …

Automatic image annotation is a key step in image retrieval and image understanding. In this
paper, we present an end-to-end automatic image annotation method based on a deep …

Automatic image annotation (AIA) plays a vital role in dealing with the exponentially growing
digital images. Image annotation helps in effective retrieval, organization, classification, auto …

Multilabel image annotation attracts a lot of research interest due to its practicability in
multimedia and computer vision fields, while the need for a large amount of labeled training …

In multi-label learning, each training instance is associated with multiple class labels. It is
typical in reality that relevant labels are partially missing and only a part of labels are valid …

In multilabel learning, each training example is represented by a single instance, which is
relevant to multiple class labels simultaneously. Generally, all relevant labels are …

Supervised deep learning depends on massive accurately annotated examples, which is
usually impractical in many real-world scenarios. A typical alternative is learning from …

Domain adaptive person re-identification (Re-ID) is challenging due to the domain gap
between the source and target domains. Existing methods have recently shown great …

In this study, a convolutional neural network with threshold optimization (CNN-THOP) is
proposed to solve the issue of overlabeling or downlabeling arising during the multilabel …