The ability of learning from noisy labels is very useful in many visual recognition tasks, as a
vast amount of data with noisy labels are relatively easy to obtain. Traditionally, label noise …

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 …

Deep ConvNets have shown great performance for single-label image classification (eg
ImageNet), but it is necessary to move beyond the single-label classification task because …

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 …

Multi-label classification is a practical yet challenging task in machine learning related fields,
since it requires the prediction of more than one label category for each input instance. We …

Multi-label classification aims to learn classification models from instances associated with
multiple labels. It is pivotal to learn and utilize the label dependence among multiple labels …

Among the three main components (data, labels, and models) of any supervised learning
system, data and models have been the main subjects of active research. However, studying …

Learning to rank has recently emerged as an attractive technique to train deep convolutional
neural networks for various computer vision tasks. Pairwise ranking, in particular, has been …

Existing multi-label learning approaches mainly utilize an identical data representation
composed of all the features in the discrimination of all the labels, and assume that all the …

Multi-label image recognition is a fundamental yet practical task because real-world images
inherently possess multiple semantic labels. However, it is difficult to collect large-scale multi …