… 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-…

… 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…

… 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. …

… 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: …

… 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 …

… datasets without extra annotation cost. In this work, we propose a scalable multidataset …
Openimages: A public dataset for large-scale multi-label and multi-class image classification…

… Figure 1: An example of semi-supervised multi-label annotation in crowdsourcing, where
the left one has 3 workers choosing from 11 candidate labels, but no one gives the right picture …

… 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 …

… We propose a scalable method to calculate the consistency of all raters on the dataset in …
multi-label annotations that mimic different annotators by following the pre-defined annotation …