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 …

Multi-label image recognition aims to recognize multiple objects simultaneously in one
image. Recent ideas to solve this problem have focused on learning dependencies of label …

Multi-label image classification is a fundamental yet challenging task in computer vision that
aims to identify multiple objects from a given image. Recent studies on this task mainly focus …

Generalized zero-shot learning (GZSL) aims to recognize samples whose categories may
not have been seen at training. Standard GZSL cannot handle dynamic addition of new …

Test-time domain adaptation aims to adapt the model trained on source domains to unseen
target domains using a few unlabeled images. Emerging research has shown that the label …

Recently, removing rain streaks from a single image has attracted a lot of attention because
rain streaks can severely degrade the perceptual quality of the image and cause many …

Despite its importance generating attacks for multi label learning (MLL) models has received
much less attention compared to multi-class recognition. Attacking an MLL model by …

Multi-label image recognition aims to predict a set of labels that present in an image. The
key to deal with such problem is to mine the associations between image contents and …

Graph Matching has recently emerged as an attractive technique applied to various
computer vision tasks. Graph Matching based multi-label image classification, in particular …

Incomplete Multiple Kernel Clustering algorithms, which aim to learn a common latent
representation from pre-constructed incomplete multiple kernels from the original data …