Multi-Label Learning From Single Positive Labels

Elijah Cole, Oisin Mac Aodha, Titouan Lorieul, Pietro Perona, Dan Morris, Nebojsa Jojic

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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 considerably more challenging to annotate training data for multi-label classification. When the number of potential labels is large, human annotators find it difficult to mention all applicable labels for each training image. Furthermore, in some settings detection is intrinsically difficult e.g. finding small object instances in high resolution images. As a result, multi-label training data is often plagued by false negatives. We consider the hardest version of this problem, where annotators provide only one relevant label for each image. As a result, training sets will have only one positive label per image and no confirmed negatives. We explore this special case of learning from missing labels across four different multi-label image classification datasets for both linear classifiers and end-to-end fine-tuned deep networks. We extend existing multi-label losses to this setting and propose novel variants that constrain the number of expected positive labels during training. Surprisingly, we show that in some cases it is possible to approach the performance of fully labeled classifiers despite training with significantly fewer confirmed labels.
Original languageEnglish
Title of host publicationProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
PublisherInstitute of Electrical and Electronics Engineers
Pages933-942
Number of pages10
ISBN (Electronic)978-1-6654-4509-2
ISBN (Print)978-1-6654-4510-8
DOIs
Publication statusPublished - 13 Nov 2021
EventIEEE Conference on Computer Vision and Pattern Recognition 2021 - Virtual
Duration: 19 Jun 202125 Jun 2021
http://cvpr2021.thecvf.com/

Publication series

Name
ISSN (Print)1063-6919
ISSN (Electronic)2575-7075

Conference

ConferenceIEEE Conference on Computer Vision and Pattern Recognition 2021
Abbreviated titleCVPR 2021
Period19/06/2125/06/21
Internet address

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