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Anomaly segmentation is a critical task for driving applications, and it is approached traditionally as a per-pixel classification problem. However, reasoning individually about each pixel without considering their contextual semantics results in hig
Externí odkaz:
http://arxiv.org/abs/2307.13316
Autor:
Fontanel, Rémi1 (AUTHOR) remi.fontanel@univ-lyon2.fr
Publikováno v:
French Screen Studies. Nov2024, Vol. 24 Issue 4, p350-368. 19p.
Object detection methods have witnessed impressive improvements in the last years thanks to the design of novel neural network architectures and the availability of large scale datasets. However, current methods have a significant limitation: they ar
Externí odkaz:
http://arxiv.org/abs/2208.11641
Despite the recent advances in the field of object detection, common architectures are still ill-suited to incrementally detect new categories over time. They are vulnerable to catastrophic forgetting: they forget what has been already learned while
Externí odkaz:
http://arxiv.org/abs/2204.08766
The COVID-19 Pandemic Highlights International Insecurity and the Violence of Economic Globalisation
Publikováno v:
International Migration, COVID-19, and Environmental Sustainability
Although existing semantic segmentation approaches achieve impressive results, they still struggle to update their models incrementally as new categories are uncovered. Furthermore, pixel-by-pixel annotations are expensive and time-consuming. This pa
Externí odkaz:
http://arxiv.org/abs/2112.01882
Robotic visual systems operating in the wild must act in unconstrained scenarios, under different environmental conditions while facing a variety of semantic concepts, including unknown ones. To this end, recent works tried to empower visual object r
Externí odkaz:
http://arxiv.org/abs/2107.04461
Traditional semantic segmentation methods can recognize at test time only the classes that are present in the training set. This is a significant limitation, especially for semantic segmentation algorithms mounted on intelligent autonomous systems, d
Externí odkaz:
http://arxiv.org/abs/2106.00472