Recognition in Terra Incognita
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Across different camera traps, there is drastic variation in illumination, color, camera angle, background, vegetation, and relative animal frequencies, which results in models generalizing poorly to new camera trap deployments.
“However, across different camera traps, there is drastic variation in illumination, color, camera angle, background, vegetation, and relative animal frequencies, which results in models generalizing poorly to new camera trap deployments (Beery et al., 2018).”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Beery et al., 2018
Distribution shifts, where the training distribution differs from the test distribution, can substantially degrade the accuracy of machine learning systems deployed in the wild.
“Distribution shifts-where the training distribution differs from the test distribution-can substantially degrade the accuracy of machine learning (ML) systems deployed in the wild.”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Beery et al., 2018
Across different camera traps, drastic variation in illumination, color, camera angle, background, vegetation, and relative animal frequencies results in models generalizing poorly to new camera trap deployments.
“However, across different camera traps, there is drastic variation in illumination, color, camera angle, background, vegetation, and relative animal frequencies, which results in models generalizing poorly to new camera trap deployments (Beery et al., 2018).”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Beery et al., 2018
Using sequence information, such as taking the median prediction across a sequence, can improve model performance on camera trap data.
“Using this sequence information (e.g., by taking the median prediction across a sequence) can also improve model performance (Beery et al., 2018)”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Beery et al., 2018
Using an animal detection model like MegaDetector typically improves classification performance on camera traps.
“Using an animal detection model like MegaDetector typically improves classification performance on camera traps (Beery et al., 2018).”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Beery et al., 2018
Terra Incognita contains photographs of wild animals taken by camera traps at locations d ∈ {L100, L38, L43, L46}.
“Terra Incognita [Beery et al., 2018] contains photographs of wild animals taken by camera traps at locations d ∈ { L100 , L38 , L43 , L46 } .”
From In Search of Lost Domain Generalization (2020) · cites this paper as [Beery et al., 2018]
Terra Incognita has 10 classes.
“Our version of this dataset contains 24 , 788 examples of dimension (3 , 224 , 224) and 10 classes.”
From In Search of Lost Domain Generalization (2020) · cites this paper as [Beery et al., 2018]
Our version of Terra Incognita contains 24,788 examples.
“Our version of this dataset contains 24 , 788 examples of dimension (3 , 224 , 224) and 10 classes.”
From In Search of Lost Domain Generalization (2020) · cites this paper as [Beery et al., 2018]
Terra Incognita examples have dimension (3,224,224).
“Our version of this dataset contains 24 , 788 examples of dimension (3 , 224 , 224) and 10 classes.”
From In Search of Lost Domain Generalization (2020) · cites this paper as [Beery et al., 2018]
Problems related to lack of generality across geographies, day/night acquisition, or sensors are still major obstacles to production-ready accurate systems.
“problems related to lack of generality across geographies, day/night acquisition, or sensors are still major obstacles to production-ready accurate systems55.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [55]
Biases are not limited to geographical provenance: the type of sensors used, the species they depict, and the imbalance in the number of individuals observed per species must also be considered when training or using models to avoid potentially catastrophic drop-offs in accuracy.
“Biases are not limited to the geographical provenance of images: the type of sensors used (RGB vs. infrared or thermal), the species they depict, and the imbalance in the number of individuals observed per species55,132 must also be considered when training or using models to avoid potentially catastrophic drop-offs in accuracy”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [55]
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“, 2018), images taken by different cameras (Beery et al., 2018), bioassays from different cell types (Li et al.”
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BackgroundMethodInfluentialUnderstanding the Failure Modes of Out-of-Distribution Generalization
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- Authors
- Beery, S., Van Horn, G., & Perona, P.
- Affiliations
- California Institute of Technology (US)
- Corresponding author
- Beery, Sara
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 472-489
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2883386984
- Cited by
- 679
- References
- 49
- Citations by year
- 2026: 78 · 2025: 137 · 2024: 104 · 2023: 114 · 2022: 91 · 2021: 94 · 2020: 47 · 2019: 13 · 2018: 1
- FWCI
- 15.82
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
- Keywords
- novel environment, camera trap images, animal detection, animal classification, benchmark dataset, wildlife monitoring
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