Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Yucel, Mehmet Kerim"'
Autor:
Skartados, Evangelos, Yucel, Mehmet Kerim, Manganelli, Bruno, Drosou, Anastasios, Saà-Garriga, Albert
Neural Radiance Fields (NeRF) have quickly become the primary approach for 3D reconstruction and novel view synthesis in recent years due to their remarkable performance. Despite the huge interest in NeRF methods, a practical use case of NeRFs has la
Externí odkaz:
http://arxiv.org/abs/2403.04508
In multimedia understanding tasks, corrupted samples pose a critical challenge, because when fed to machine learning models they lead to performance degradation. In the past, three groups of approaches have been proposed to handle noisy data: i) enha
Externí odkaz:
http://arxiv.org/abs/2402.18402
Convolutional Neural Networks (CNN) are known to exhibit poor generalization performance under distribution shifts. Their generalization have been studied extensively, and one line of work approaches the problem from a frequency-centric perspective.
Externí odkaz:
http://arxiv.org/abs/2307.11823
Autor:
Skartados, Evangelos, Georgiadis, Konstantinos, Yucel, Mehmet Kerim, Ioannis, Koskinas, Domi, Armando, Drosou, Anastasios, Manganelli, Bruno, Saa-Garriga, Albert
Space-time memory (STM) network methods have been dominant in semi-supervised video object segmentation (SVOS) due to their remarkable performance. In this work, we identify three key aspects where we can improve such methods; i) supervisory signal,
Externí odkaz:
http://arxiv.org/abs/2306.15377
This paper tackles the problem of semi-supervised video object segmentation on resource-constrained devices, such as mobile phones. We formulate this problem as a distillation task, whereby we demonstrate that small space-time-memory networks with fi
Externí odkaz:
http://arxiv.org/abs/2303.07815
Autor:
Georgiadis, Konstantinos, Saà-Garriga, Albert, Yucel, Mehmet Kerim, Drosou, Anastasios, Manganelli, Bruno
Bokeh effect highlights an object (or any part of the image) while blurring the rest of the image, and creates a visually pleasant artistic effect. Due to the sensor-based limitations on mobile devices, machine learning (ML) based bokeh rendering has
Externí odkaz:
http://arxiv.org/abs/2210.16078
Data shift robustness has been primarily investigated from a fully supervised perspective, and robustness of zero-shot learning (ZSL) models have been largely neglected. In this paper, we present novel analyses on the robustness of discriminative ZSL
Externí odkaz:
http://arxiv.org/abs/2201.10972
Monocular (relative or metric) depth estimation is a critical task for various applications, such as autonomous vehicles, augmented reality and image editing. In recent years, with the increasing availability of mobile devices, accurate and mobile-fr
Externí odkaz:
http://arxiv.org/abs/2105.12053
Autor:
Bilge, Yunus Can, Yucel, Mehmet Kerim, Cinbis, Ramazan Gokberk, Ikizler-Cinbis, Nazli, Duygulu, Pinar
In many real-world problems, there is typically a large discrepancy between the characteristics of data used in training versus deployment. A prime example is the analysis of aggression videos: in a criminal incidence, typically suspects need to be i
Externí odkaz:
http://arxiv.org/abs/2009.07576
Machine learning (ML) systems have introduced significant advances in various fields, due to the introduction of highly complex models. Despite their success, it has been shown multiple times that machine learning models are prone to imperceptible pe
Externí odkaz:
http://arxiv.org/abs/2008.07651