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Query-based Hard-Image Retrieval for Object Detection at Test Time. (arXiv:2209.11559v1 [cs.CV])
Sept. 26, 2022, 1:11 a.m. | Edward Ayers, Jonathan Sadeghi, John Redford, Romain Mueller, Puneet K. Dokania
cs.LG updates on arXiv.org arxiv.org
There is a longstanding interest in capturing the error behaviour of object
detectors by finding images where their performance is likely to be
unsatisfactory. In real-world applications such as autonomous driving, it is
also crucial to characterise potential failures beyond simple requirements of
detection performance. For example, a missed detection of a pedestrian close to
an ego vehicle will generally require closer inspection than a missed detection
of a car in the distance. The problem of predicting such potential failures …
More from arxiv.org / cs.LG updates on arXiv.org
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