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pro vyhledávání: '"Bilodeau, A."'
Autor:
Marton, Orsolya
Publikováno v:
Játéktér / Playing Area. (1):38-39
Externí odkaz:
https://www.ceeol.com/search/article-detail?id=1128257
Autor:
Lamarque, Maia Fernández
Publikováno v:
Hispania, 2022 Jun 01. 105(2), 307-309.
Externí odkaz:
https://www.jstor.org/stable/27184343
To extract robust and generalizable skeleton action recognition features, large amounts of well-curated data are typically required, which is a challenging task hindered by annotation and computation costs. Therefore, unsupervised representation lear
Externí odkaz:
http://arxiv.org/abs/2409.05749
Akademický článek
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Neuromorphic (brain-inspired) photonics leverages photonic chips to accelerate artificial intelligence, offering high-speed and energy efficient solutions in RF communication, tensor processing, and data classification. However, the limited physical
Externí odkaz:
http://arxiv.org/abs/2407.02366
We propose a novel Transformer-based module to address the data association problem for multi-object tracking. From detections obtained by a pretrained detector, this module uses only coordinates from bounding boxes to estimate an affinity score betw
Externí odkaz:
http://arxiv.org/abs/2403.08018
Increasing the accuracy of instance segmentation methods is often done at the expense of speed. Using coarser representations, we can reduce the number of parameters and thus obtain real-time masks. In this paper, we take inspiration from the set cov
Externí odkaz:
http://arxiv.org/abs/2403.03296
Autor:
Sabri, Khalil, Djilali, Célia, Bilodeau, Guillaume-Alexandre, Saunier, Nicolas, Bouachir, Wassim
Publikováno v:
Proceedings of the 21st Conference on Robots and Vision, 2024
Urban traffic environments present unique challenges for object detection, particularly with the increasing presence of micromobility vehicles like e-scooters and bikes. To address this object detection problem, this work introduces an adapted detect
Externí odkaz:
http://arxiv.org/abs/2402.18503
Consecutive frames in a video contain redundancy, but they may also contain relevant complementary information for the detection task. The objective of our work is to leverage this complementary information to improve detection. Therefore, we propose
Externí odkaz:
http://arxiv.org/abs/2402.10752