Kate E. Jones — accepted AI-conference papers

2024

INQUIRE: A Natural World Text-to-Image Retrieval Benchmark

Edward Vendrow, Omiros Pantazis, Alexander Shepard, Gabriel Brostow, Kate E. Jones, Oisin Mac Aodha +2

NeurIPS 2024poster

We introduce INQUIRE, a text-to-image retrieval benchmark designed to challenge multimodal vision-language models on expert-level queries. INQUIRE includes iNaturalist 2024 (iNat24), a new dataset of five million natural world images, along with 250 expert-level retrieval queries. These queries are…

Cited by 1 Source ↗PDF ↗code ↗Scholar ↗

2021

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

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

ICCV 2021poster

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…

Cited by 32 PDF ↗code ↗Scholar ↗