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pro vyhledávání: '"Park, Jongyoul"'
From a streaming video, online action detection aims to identify actions in the present. For this task, previous methods use recurrent networks to model the temporal sequence of current action frames. However, these methods overlook the fact that an
Externí odkaz:
http://arxiv.org/abs/1912.04461
Recent temporal action proposal generation approaches have suggested integrating segment- and snippet score-based methodologies to produce proposals with high recall and accurate boundaries. In this paper, different from such a hybrid strategy, we fo
Externí odkaz:
http://arxiv.org/abs/1911.11306
Autor:
Lee, Youngwan, Park, Jongyoul
We propose a simple yet efficient anchor-free instance segmentation, called CenterMask, that adds a novel spatial attention-guided mask (SAG-Mask) branch to anchor-free one stage object detector (FCOS) in the same vein with Mask R-CNN. Plugged into t
Externí odkaz:
http://arxiv.org/abs/1911.06667
As DenseNet conserves intermediate features with diverse receptive fields by aggregating them with dense connection, it shows good performance on the object detection task. Although feature reuse enables DenseNet to produce strong features with a sma
Externí odkaz:
http://arxiv.org/abs/1904.09730
Autor:
Jung, Jaewon, Park, Jongyoul
Publikováno v:
Third IEEE International Conference on Image Processing, Applications and Systems (IPAS 2018)
Visual relationship detection is an intermediate image understanding task that detects two objects and classifies a predicate that explains the relationship between two objects in an image. The three components are linguistically and visually correla
Externí odkaz:
http://arxiv.org/abs/1904.07798
Autor:
Jo, Youngjoo, Park, Jongyoul
We present a novel image editing system that generates images as the user provides free-form mask, sketch and color as an input. Our system consist of a end-to-end trainable convolutional network. Contrary to the existing methods, our system wholly u
Externí odkaz:
http://arxiv.org/abs/1902.06838
In visual surveillance systems, it is necessary to recognize the behavior of people handling objects such as a phone, a cup, or a plastic bag. In this paper, to address this problem, we propose a new framework for recognizing object-related human act
Externí odkaz:
http://arxiv.org/abs/1901.06882
Akademický článek
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Publikováno v:
In Knowledge-Based Systems 28 November 2022 256
Publikováno v:
In Expert Systems With Applications 15 November 2022 206