1 code implementation • 1 Aug 2023 • Nikolaj Reiser, Min Guo, Hari Shroff, Patrick J. La Riviere
Here, the Gaussian- and Poisson-based estimation algorithms are implemented and compared for widefield microscopy in simulation.
no code implementations • 18 Jul 2023 • Hongwei Zheng, Han Li, Bowen Shi, Wenrui Dai, Botao Wan, Yu Sun, Min Guo, Hongkai Xiong
Recent 2D-to-3D human pose estimation (HPE) utilizes temporal consistency across sequences to alleviate the depth ambiguity problem but ignore the action related prior knowledge hidden in the pose sequence.
no code implementations • 15 Feb 2023 • Han Li, Bowen Shi, Wenrui Dai, Hongwei Zheng, Botao Wang, Yu Sun, Min Guo, Chenlin Li, Junni Zou, Hongkai Xiong
There has been a recent surge of interest in introducing transformers to 3D human pose estimation (HPE) due to their powerful capabilities in modeling long-term dependencies.
no code implementations • 8 Dec 2022 • Zhendong Liu, Wenyu Jiang, Min Guo, Chongjun Wang
Based on the analysis of the internal mechanisms, we develop a mask-based boosting method for data augmentation that comprehensively improves several robustness measures of AI models and beats state-of-the-art data augmentation approaches.
no code implementations • 6 Nov 2022 • Qi-Qing Song, Min Guo
This gives two existence results of alpha-core solutions by introducing P-open conditions and strong P-open conditions into games without ordered preferences.
1 code implementation • 23 Nov 2021 • Han Li, Bowen Shi, Wenrui Dai, Yabo Chen, Botao Wang, Yu Sun, Min Guo, Chenlin Li, Junni Zou, Hongkai Xiong
Recent 2D-to-3D human pose estimation works tend to utilize the graph structure formed by the topology of the human skeleton.
Ranked #42 on 3D Human Pose Estimation on MPI-INF-3DHP (AUC metric)
no code implementations • 10 Oct 2021 • Yuyang Zhang, Dik Hin Leung, Min Guo, Yijia Xiao, Haoyue Liu, Yunfei Li, Jiyuan Zhang, Guan Wang, Zhen Chen
Matrix multiplication is the bedrock in Deep Learning inference application.
no code implementations • 23 Sep 2020 • Rohun Kshirsagar, Li-Yen Hsu, Vatshank Chaturvedi, Charles H. Greenberg, Matthew McClelland, Anushadevi Mohan, Wideet Shende, Nicolas P. Tilmans, Renzo Frigato, Min Guo, Ankit Chheda, Meredith Trotter, Shonket Ray, Arnold Lee, Miguel Alvarado
We evaluated the ability of machine learning models to predict the per member per month cost of employer groups in their next renewal period, especially those groups who will cost less than 95\% of what an actuarial model predicts (groups with "concession opportunities").