Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Sarić, Josip"'
Domain adaptive panoptic segmentation promises to resolve the long tail of corner cases in natural scene understanding. Previous state of the art addresses this problem with cross-task consistency, careful system-level optimization and heuristic impr
Externí odkaz:
http://arxiv.org/abs/2407.14110
Most dense recognition approaches bring a separate decision in each particular pixel. These approaches deliver competitive performance in usual closed-set setups. However, important applications in the wild typically require strong performance in pre
Externí odkaz:
http://arxiv.org/abs/2301.03407
Publikováno v:
International Journal of Computer Vision, 2024, 1-23
Deep supervised models have an unprecedented capacity to absorb large quantities of training data. Hence, training on multiple datasets becomes a method of choice towards strong generalization in usual scenes and graceful performance degradation in e
Externí odkaz:
http://arxiv.org/abs/2212.10340
Publikováno v:
Remote Sensing. 2023, 15(8), 1968
Dense panoptic prediction is a key ingredient in many existing applications such as autonomous driving, automated warehouses or remote sensing. Many of these applications require fast inference over large input resolutions on affordable or even embed
Externí odkaz:
http://arxiv.org/abs/2203.07908
Deep supervised models have an unprecedented capacity to absorb large quantities of training data. Hence, training on many datasets becomes a method of choice towards graceful degradation in unusual scenes. Unfortunately, different datasets often use
Externí odkaz:
http://arxiv.org/abs/2108.11224
Dense semantic forecasting anticipates future events in video by inferring pixel-level semantics of an unobserved future image. We present a novel approach that is applicable to various single-frame architectures and tasks. Our approach consists of t
Externí odkaz:
http://arxiv.org/abs/2101.10777
This paper considers semantic forecasting in road-driving scenes. Most existing approaches address this problem as deterministic regression of future features or future predictions given observed frames. However, such approaches ignore the fact that
Externí odkaz:
http://arxiv.org/abs/2010.09067
We present our submission to the semantic segmentation contest of the Robust Vision Challenge held at ECCV 2020. The contest requires submitting the same model to seven benchmarks from three different domains. Our approach is based on the SwiftNet ar
Externí odkaz:
http://arxiv.org/abs/2009.01636
Future anticipation is of vital importance in autonomous driving and other decision-making systems. We present a method to anticipate semantic segmentation of future frames in driving scenarios based on feature-to-feature forecasting. Our method is b
Externí odkaz:
http://arxiv.org/abs/1907.11475
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