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pro vyhledávání: '"Vidal, Maxime"'
Autor:
Ye, Shaokai, Filippova, Anastasiia, Lauer, Jessy, Schneider, Steffen, Vidal, Maxime, Qiu, Tian, Mathis, Alexander, Mathis, Mackenzie Weygandt
Quantification of behavior is critical in applications ranging from neuroscience, veterinary medicine and animal conservation efforts. A common key step for behavioral analysis is first extracting relevant keypoints on animals, known as pose estimati
Externí odkaz:
http://arxiv.org/abs/2203.07436
We propose an end-to-end trainable approach for multi-instance pose estimation, called POET (POse Estimation Transformer). Combining a convolutional neural network with a transformer encoder-decoder architecture, we formulate multiinstance pose estim
Externí odkaz:
http://arxiv.org/abs/2103.12115
Publikováno v:
Integr Comp Biol . 2021 Oct 4;61(3):900-916
Identifying individual animals is crucial for many biological investigations. In response to some of the limitations of current identification methods, new automated computer vision approaches have emerged with strong performance. Here, we review cur
Externí odkaz:
http://arxiv.org/abs/2103.00560
Recent advances in the field of language modeling have improved state-of-the-art results on many Natural Language Processing tasks. Among them, Reading Comprehension has made significant progress over the past few years. However, most results are rep
Externí odkaz:
http://arxiv.org/abs/2002.06071
Akademický článek
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Autor:
Ye, Shaokai, Filippova, Anastasiia, Lauer, Jessy, Vidal, Maxime, Schneider, Steffen, Qiu, Tian, Mathis, Alexander, Mathis, Mackenzie Weygandt
Quantification of behavior is critical in applications ranging from neuroscience, veterinary medicine and animal conservation efforts. A common key step for behavioral analysis is first extracting relevant keypoints on animals, known as pose estimati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1806bd16f24ab293e88e87c9da348a08
http://arxiv.org/abs/2203.07436
http://arxiv.org/abs/2203.07436