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Thian Song

As seen in: arXiv, MDPI, F1000Research
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Articles by Thian Song

PAtt-Lite: Lightweight Patch and Attention MobileNet for Challenging Facial Expression Recognition

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By Chin Poo, Thian Song, Jia Le, Kian Ming
| arXiv
arXiv:2306.09626 (cs) Download a PDF of the paper titled PAtt-Lite: Lightweight Patch and Attention MobileNet for Challenging Facial Expression Recognition, by Jia Le Ngwe and 3 other authors Download PDF Submission history From: Kian Ming Lim [ view email] [v1] Fri, 16 Jun 2023 04:51:18 UTC (6,728 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code,...
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Deep Reinforcement Learning-Based Dynamic Pricing for Parking Solutions

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By Tee Connie, Thian Song Ong, Li Zhe, Thian Song
| MDPI
Abstract : The growth in the number of automobiles in metropolitan areas has drawn attention to the need for more efficient carpark control in public spaces such as healthcare, retail stores, and office blocks. In this research, dynamic pricing is integrated with real-time parking data to optimise parking utilisation and reduce traffic jams. Dynamic pricing is the practice of changing the price of a product or service in response to market trends.
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[2210.00294] Gait-based Age Group Classification with Adaptive Graph Neural Network

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By Thian Song, Andrew Jin, Michael Ong
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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F1000Research Article: Deep supervised hashing for gait retrieval.

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By Bukit Beruang, Thian Song, Shohel Sayeed, Thian Song Ong
| F1000Research
Introduction Gait recognition is perceived as the most promising biometric approach1-3 among behavioural biometric approaches, especially because of its efficient applicability in surveillance systems. However, gait recognition and identification tasks are becoming increasingly difficult due to the large-scale images and videos generated from surveillance systems.
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