Exabytes of data are generated daily by humans, leading to the growing needs for new
efforts in dealing with the grand challenges for multi-label learning brought by big data. For …

Solving multi-label recognition (MLR) for images in the low-label regime is a challenging
task with many real-world applications. Recent work learns an alignment between textual …

The simultaneous recognition of multiple objects in one image remains a challenging task,
spanning multiple events in the recognition field such as various object scales, inconsistent …

Weakly supervised multi-label classification (WSML) task, which is to learn a multi-label
classification using partially observed labels per image, is becoming increasingly important …

Multi-label image recognition is a practical and challenging task compared to single-label
image classification. However, previous works may be suboptimal because of a great …

Multi-label recognition (MLR) with incomplete labels is very challenging. Recent works strive
to explore the image-to-label correspondence in the vision-language model, ie, CLIP, to …

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 …

Multi-label image classification refers to assigning a set of labels for an image. One of the
main challenges of this task is how to effectively capture the correlation among labels …

Real world videos contain many complex actions with inherent relationships between action
classes. In this work, we propose an attention-based architecture that model these action …

The purpose of multi-label image classification task is to accurately assign a set of labels to
the objects in images. Although promising results have been achieved, most of the existing …