Presence-Only Geographical Priors for Fine-Grained Image Classification
Open accessAbstract
Appearance information alone is often not sufficient to accurately differentiate between fine-grained visual categories. Human experts make use of additional cues such as where, and when, a given image was taken in order to inform their final decision. This contextual information is readily available in many online image collections but has been underutilized by existing image classifiers that focus solely on making predictions based on the image contents. We propose an efficient spatio-temporal prior, that when conditioned on a geographical location and time, estimates the probability that a given object category occurs at that location. Our prior is trained from presence-only observation data and jointly models object categories, their spatio-temporal distributions, and photographer biases. Experiments performed on multiple challenging image classification datasets show that combining our prior with the predictions from image classifiers results in a large improvement in final classification performance.
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Ref. 139 incorporates knowledge about the distribution of species and photographer biases into a DL model for species classification in images and reports accuracy improvements of up to 12% in iNaturalist over a baseline without such priors.
“Similarly ref. 139, incorporate knowledge about the distribution of species as well as photographer biases into a DL model for species classification in images and report accuracy improvements of up to 12% in iNaturalist over a baseline without such priors.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [139]
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Cited by
Sorted by citations
Perspectives in machine learning for wildlife conservation
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Self-citationDigital taxonomist: Identifying plant species in community scientists’ photographs
de Lutio, R., She, Y., D’Aronco, S., Russo, S., Brun, P., Wegner, J. D., & Schindler, K. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.10.002 · Cited by 23 · see citing works
- Authors
- Aodha, O. M., Cole, E., & Perona, P.
- Affiliations
- California Institute of Technology (US)
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 9595-9605
- OpenAlex
- W2950121050
- Cited by
- 149
- References
- 69
- Citations by year
- 2026: 16 · 2025: 33 · 2024: 28 · 2023: 19 · 2022: 18 · 2021: 24 · 2020: 8 · 2019: 3
- FWCI
- 4.27
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1745301)
- Nvidia
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- fine-grained image classification, presence-only data, spatiotemporal variation, contextual information
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