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• W2951792921 abstract "It is desirable for detection and classification algorithms to generalize to unfamiliar environments, but suitable benchmarks for quantitatively studying this phenomenon are not yet available. We present a dataset designed to measure recognition generalization to novel environments. The images in our dataset are harvested from twenty camera traps deployed to monitor animal populations. Camera traps are fixed at one location, hence the background changes little across images; capture is triggered automatically, hence there is no human bias. The challenge is learning recognition in a handful of locations, and generalizing animal detection and classification to new locations where no training data is available. In our experiments state-of-the-art algorithms show excellent performance when tested at the same location where they were trained. However, we find that generalization to new locations is poor, especially for classification systems." @default.
• W2951792921 created "2019-06-27" @default.
• W2951792921 creator A5024685173 @default.
• W2951792921 creator A5026825380 @default.
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• W2951792921 date "2018-07-13" @default.
• W2951792921 modified "2023-09-27" @default.
• W2951792921 title "Recognition in Terra Incognita" @default.
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• W2951792921 hasPublicationYear "2018" @default.
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