J
Jin-Hua Du
Publications - Â 4
Citations - Â 67
Jin-Hua Du is an academic researcher. The author has contributed to research in topics: Mobile device & Android (operating system). The author has an hindex of 2, co-authored 4 publications receiving 28 citations.
Papers
More filters
Proceedings ArticleDOI
Fast and Accurate Quantized Camera Scene Detection on Smartphones, Mobile AI 2021 Challenge: Report
Andrey Ignatov,Grigory Malivenko,Radu Timofte,Sheng Chen,Xin Xia,Zhaoyan Liu,Yuwei Zhang,Feng Zhu,Jiashi Li,Xuefeng Xiao,Yuan Tian,Xinglong Wu,Christos Kyrkou,Yixin Chen,Zexin Zhang,Yunbo Peng,Yue Lin,Saikat Dutta,Sourya Dipta Das,Nisarg Shah,Himanshu Kumar,Chao Ge,Pei-Lin Wu,Jin-Hua Du,Andrew Batutin,Juan Pablo Federico,Konrad Lyda,Levon Khojoyan,Abhishek Thanki,Sayak Paul,Shahid Siddiqui +30 more
TL;DR: In this article, the first Mobile AI challenge was introduced to develop quantized deep learning-based camera scene classification solutions that can demonstrate a real-time performance on smartphones and IoT platforms.
Proceedings ArticleDOI
Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report
Andrey Ignatov,Grigory Malivenko,David Plowman,Samarth Shukla,Radu Timofte,Ziyu Zhang,Yicheng Wang,Zilong Huang,Guozhong Luo,Gang Yu,Bin Fu,Yiran Wang,Xingyi Li,Min Shi,Ke Xian,Zhiguo Cao,Jin-Hua Du,Pei-Lin Wu,Chao Ge,Jiaoyang Yao,Fangwen Tu,Bo Li,Jung Eun Yoo,Kwanggyoon Seo,Jialei Xu,Zhenyu Li,Xianming Liu,Junjun Jiang,Wei-Chi Chen,Shayan Joya,Huanhuan Fan,Zhaobing Kang,Ang Li,Tianpeng Feng,Yang Liu,Chuannan Sheng,Jian Yin,Fausto T. Benavides +37 more
TL;DR: The first Mobile AI challenge as discussed by the authors was introduced to develop an end-to-end deep learning-based depth estimation solutions that can demonstrate a nearly real-time performance on smartphones and IoT platforms, where participants were provided with a new large-scale dataset containing RGB-depth image pairs obtained with a dedicated stereo ZED camera producing high-resolution depth maps for objects located at up to 50 meters.
Posted Content
Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report.
Andrey Ignatov,Grigory Malivenko,David Plowman,Samarth Shukla,Radu Timofte,Ziyu Zhang,Yicheng Wang,Zilong Huang,Guozhong Luo,Gang Yu,Bin Fu,Yiran Wang,Xingyi Li,Min Shi,Ke Xian,Zhiguo Cao,Jin-Hua Du,Pei-Lin Wu,Chao Ge,Jiaoyang Yao,Fangwen Tu,Bo Li,Jung Eun Yoo,Kwanggyoon Seo,Jialei Xu,Zhenyu Li,Xianming Liu,Junjun Jiang,Wei-Chi Chen,Shayan Joya,Huanhuan Fan,Zhaobing Kang,Ang Li,Tianpeng Feng,Yang Liu,Chuannan Sheng,Jian Yin,Fausto T. Benavide +37 more
TL;DR: The first Mobile AI challenge as discussed by the authors was introduced to develop an end-to-end deep learning-based depth estimation solutions that can demonstrate a nearly real-time performance on smartphones and IoT platforms.
Posted Content
Fast and Accurate Quantized Camera Scene Detection on Smartphones, Mobile AI 2021 Challenge: Report
Andrey Ignatov,Grigory Malivenko,Radu Timofte,Sheng Chen,Xin Xia,Zhaoyan Liu,Yuwei Zhang,Feng Zhu,Jiashi Li,Xuefeng Xiao,Yuan Tian,Xinglong Wu,Christos Kyrkou,Yixin Chen,Zexin Zhang,Yunbo Peng,Yue Lin,Saikat Dutta,Sourya Dipta Das,Nisarg Shah,Himanshu Kumar,Chao Ge,Pei-Lin Wu,Jin-Hua Du,Andrew Batutin,Juan Pablo Federico,Konrad Lyda,Levon Khojoyan,Abhishek Thanki,Sayak Paul,Shahid Siddiqui +30 more
TL;DR: In this paper, the first Mobile AI challenge was introduced to develop quantized deep learning-based camera scene classification solutions that can demonstrate a real-time performance on smartphones and IoT platforms.