Zobrazeno 1 - 10
of 69
pro vyhledávání: '"Huynh, Du. Q."'
Autor:
Zhuang, Shengxin, Tanner, John, Wu, Yusen, Huynh, Du Q., Cadet, Wei Liu Xavier F., Fontaine, Nicolas, Charton, Philippe, Damour, Cedric, Cadet, Frederic, Wang, Jingbo
Quantum machine learning (QML) is one of the most promising applications of quantum computation. However, it is still unclear whether quantum advantages exist when the data is of a classical nature and the search for practical, real-world application
Externí odkaz:
http://arxiv.org/abs/2402.03847
Automatic evaluation of hashtag recommendation models is a fundamental task in many online social network systems. In the traditional evaluation method, the recommended hashtags from an algorithm are firstly compared with the ground truth hashtags fo
Externí odkaz:
http://arxiv.org/abs/2305.18330
Pedestrian trajectory prediction is valuable for understanding human motion behaviors and it is challenging because of the social influence from other pedestrians, the scene constraints and the multimodal possibilities of predicted trajectories. Most
Externí odkaz:
http://arxiv.org/abs/2010.05507
Hashtag recommendation is a crucial task, especially with an increase of interest in using social media platforms such as Twitter in the last decade. Hashtag recommendation systems automatically suggest hashtags to a user while writing a tweet. Most
Externí odkaz:
http://arxiv.org/abs/2010.01258
Pedestrian trajectory prediction is a challenging task as there are three properties of human movement behaviors which need to be addressed, namely, the social influence from other pedestrians, the scene constraints, and the multimodal (multiroute) n
Externí odkaz:
http://arxiv.org/abs/2004.09760
From video streaming to security and surveillance applications, video data play an important role in our daily living today. However, managing a large amount of video data and retrieving the most useful information for the user remain a challenging t
Externí odkaz:
http://arxiv.org/abs/1906.11465
Video-based human action recognition is currently one of the most active research areas in computer vision. Various research studies indicate that the performance of action recognition is highly dependent on the type of features being extracted and h
Externí odkaz:
http://arxiv.org/abs/1906.09955
Publikováno v:
ICCV 2019
In this paper, we revive the use of old-fashioned handcrafted video representations for action recognition and put new life into these techniques via a CNN-based hallucination step. Despite of the use of RGB and optical flow frames, the I3D model (am
Externí odkaz:
http://arxiv.org/abs/1906.05910
Existing techniques for 3D action recognition are sensitive to viewpoint variations because they extract features from depth images which change significantly with viewpoint. In contrast, we directly process the pointclouds and propose a new techniqu
Externí odkaz:
http://arxiv.org/abs/1408.3809
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