Focus on the Positives: Self-Supervised Learning for Biodiversity Monitoring

Omiros Pantazis, Gabriel J. Brostow, Kate E. Jones, Oisin Mac Aodha

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

Abstract

We address the problem of learning self-supervised representations from unlabeled image collections. Unlike existing approaches that attempt to learn useful features by maximizing similarity between augmented versions of each input image or by speculatively picking negative samples, we instead also make use of the natural variation that occurs in image collections that are captured using static monitoring cameras. To achieve this, we exploit readily available context data that encodes information such as the spatial and temporal relationships between the input images. We are able to learn representations that are surprisingly effective for downstream supervised classification, by first identifying high probability positive pairs at training time, i.e. those images that are likely to depict the same visual concept. For the critical task of global biodiversity monitoring, this results in image features that can be adapted to challenging visual species classification tasks with limited human supervision. We present results on four different camera trap image collections, across three different families of self-supervised learning methods, and show that careful image selection at training time results in superior performance compared to existing baselines such as conventional self-supervised training and transfer learning.
Original languageEnglish
Title of host publicationInternational Conference on Computer Vision (ICCV 2021)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages10
Publication statusAccepted/In press - 22 Jul 2021
EventInternational Conference on Computer Vision 2021 - Online
Duration: 11 Oct 202117 Oct 2021
https://iccv2021.thecvf.com/home

Conference

ConferenceInternational Conference on Computer Vision 2021
Abbreviated titleICCV 2021
Period11/10/2117/10/21
Internet address

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