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• W2336589871 abstract "The state-of-the-art performance for object detection has been significantly improved over the past two years. Besides the introduction of powerful deep neural networks such as GoogleNet and VGG, novel object detection frameworks such as R-CNN and its successors, Fast R-CNN and Faster R-CNN, play an essential role in improving the state-of-the-art. Despite their effectiveness on still images, those frameworks are not specifically designed for object detection from videos. Temporal and contextual information of videos are not fully investigated and utilized. In this work, we propose a deep learning framework that incorporates temporal and contextual information from tubelets obtained in videos, which dramatically improves the baseline performance of existing still-image detection frameworks when they are applied to videos. It is called T-CNN, i.e. tubelets with convolutional neueral networks. The proposed framework won the recently introduced object-detection-from-video (VID) task with provided data in the ImageNet Large-Scale Visual Recognition Challenge 2015 (ILSVRC2015)." @default.
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• W2336589871 date "2018-10-01" @default.
• W2336589871 modified "2023-09-29" @default.
• W2336589871 title "T-CNN: Tubelets With Convolutional Neural Networks for Object Detection From Videos" @default.
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• W2336589871 doi "https://doi.org/10.1109/tcsvt.2017.2736553" @default.
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