Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Vidit Vidit"'
Animal Re-ID is crucial for wildlife conservation, yet it faces unique challenges compared to person Re-ID. First, the scarcity and lack of diversity in datasets lead to background-biased models. Second, animal Re-ID depends on subtle, species-specif
Externí odkaz:
http://arxiv.org/abs/2405.13781
Single Domain Generalization (SDG) tackles the problem of training a model on a single source domain so that it generalizes to any unseen target domain. While this has been well studied for image classification, the literature on SDG object detection
Externí odkaz:
http://arxiv.org/abs/2301.05499
The performance of modern object detectors drops when the test distribution differs from the training one. Most of the methods that address this focus on object appearance changes caused by, e.g., different illumination conditions, or gaps between sy
Externí odkaz:
http://arxiv.org/abs/2301.05496
Despite achieving remarkable progress in recent years, single-image super-resolution methods are developed with several limitations. Specifically, they are trained on fixed content domains with certain degradations (whether synthetic or real). The pr
Externí odkaz:
http://arxiv.org/abs/2208.12327
Autor:
Vidit, Vidit, Salzmann, Mathieu
While domain adaptation has been used to improve the performance of object detectors when the training and test data follow different distributions, previous work has mostly focused on two-stage detectors. This is because their use of region proposal
Externí odkaz:
http://arxiv.org/abs/2106.07283
Autor:
Vidit, Vidit1 (AUTHOR) vidit.vidit@epfl.ch, Salzmann, Mathieu1 (AUTHOR)
Publikováno v:
Machine Vision & Applications. Sep2022, Vol. 33 Issue 5, p1-14. 14p.
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031250620
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d6b41953742c81a92a902eabfcabff5e
https://doi.org/10.1007/978-3-031-25063-7_22
https://doi.org/10.1007/978-3-031-25063-7_22