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pro vyhledávání: '"Shinya, Yosuke"'
Autor:
Shinya, Yosuke
Scale-wise evaluation of object detectors is important for real-world applications. However, existing metrics are either coarse or not sufficiently reliable. In this paper, we propose novel scale-wise metrics that strike a balance between fineness an
Externí odkaz:
http://arxiv.org/abs/2307.11748
Autor:
Kondo, Yuki, Ukita, Norimichi, Yamaguchi, Takayuki, Hou, Hao-Yu, Shen, Mu-Yi, Hsu, Chia-Chi, Huang, En-Ming, Huang, Yu-Chen, Xia, Yu-Cheng, Wang, Chien-Yao, Lee, Chun-Yi, Huo, Da, Kastner, Marc A., Liu, Tingwei, Kawanishi, Yasutomo, Hirayama, Takatsugu, Komamizu, Takahiro, Ide, Ichiro, Shinya, Yosuke, Liu, Xinyao, Liang, Guang, Yasui, Syusuke
Publikováno v:
2023 18th International Conference on Machine Vision and Applications (MVA)
Small Object Detection (SOD) is an important machine vision topic because (i) a variety of real-world applications require object detection for distant objects and (ii) SOD is a challenging task due to the noisy, blurred, and less-informative image a
Externí odkaz:
http://arxiv.org/abs/2307.09143
Autor:
Shinya, Yosuke
Benchmarks, such as COCO, play a crucial role in object detection. However, existing benchmarks are insufficient in scale variation, and their protocols are inadequate for fair comparison. In this paper, we introduce the Universal-Scale object detect
Externí odkaz:
http://arxiv.org/abs/2103.14027
Deep Neural Networks (DNNs) have recently been achieving state-of-the-art performance on a variety of computer vision related tasks. However, their computational cost limits their ability to be implemented in embedded systems with restricted resource
Externí odkaz:
http://arxiv.org/abs/1912.11853
ImageNet pre-training has been regarded as essential for training accurate object detectors for a long time. Recently, it has been shown that object detectors trained from randomly initialized weights can be on par with those fine-tuned from ImageNet
Externí odkaz:
http://arxiv.org/abs/1909.04021