… In multi-label learning, this requirement is more evident than … a novel method for multi-label
annotation based on a … the feature representation and the annotation labels. The proposed …

… As a second contribution, we propose a scalable method to learn a ConvNet with … annotate
a multi-label dataset. The goal is to answer the question: what is the best strategy to annotate …

… to perform feature selection on multi-view multi-label data. To address these challenges, in
this paper, we propose a novel multi-view multi-label sparse feature selection (MSFS) method…

… they cannot capture the multi-label semantic information. Recently, by taking into account
the high level semantic information in the form of multi-label annotations, multi-label CCA (ml-…

… the ability to handle the multi-label setting. In this paper, we present a novel framework to
efficiently transfer VLMs to address multi-label image recognition with limited annotations. …

… -to-end automatic image annotation model based on DCNN and multi-label data augmentation,
which can significantly improve the performance of the multi-label image annotation. We …

… classification (XMLC) aims to learn a classifier that is able to automatically annotate a data
… Rai, “Scalable generative models for multi-label learning with missing labels,” in Proc. 34th …

… However, they are not scalable to large datasets and their optimization procedures are not
… In this section, we will report our main results on the partially annotated multi-label datasets: …

… network has to learn an up-scaling, at the cost of additional … a multi-label loss with a label
containing all semantic classes present in the receptive field. It follows, that instead of upscaling …

… number of annotations (eg, scaling COCO by 10× would require 200k hours and $2M [33]).
Annotation cost and annotation time are proportional, so in this paper we use annotation time …