Multi-label image classification is the task of predicting a set of labels corresponding to
objects, attributes or other entities present in an image. In this work we propose the …

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 …

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 …

Predicting all applicable labels for a given image is known as multi-label classification.
Compared to the standard multi-class case (where each image has only one label), it is …

Visual attributes constitute a large portion of information contained in a scene. Objects can
be described using a wide variety of attributes which portray their visual appearance (color …

The goal of open-world compositional zero-shot learning (OW-CZSL) is to recognize
compositions of state and objects in images, given only a subset of them during training and …

Due to the expensive costs of collecting labels in multi-label classification datasets, partially
annotated multi-label classification has become an emerging field in computer vision. One …

Large-scale multi-label classification datasets are commonly, and perhaps inevitably,
partially annotated. That is, only a small subset of labels are annotated per sample. Different …

Partially annotated images are easy to obtain in multi-label classification. However,
unknown labels in partially annotated images exacerbate the positive-negative imbalance …

Background Since the 2016 WHO guidelines, glioma diagnosis has entered an era of
integrated diagnosis, combining tissue pathology and molecular pathology. The WHO has …