On Total Capture dataset, KineFuse surpasses previous state-of-the-art which uses IMU only for testing by 8. 6\%.
Ranked #2 on 3D Human Pose Estimation on Total Capture
The softmax-based loss functions and its variants (e. g., cosface, sphereface, and arcface) significantly improve the face recognition performance in wild unconstrained scenes.
In this study, we present a novel normalization technique called window normalization (WIN) to improve the model generalization on heterogeneous medical images, which is a simple yet effective alternative to existing normalization methods.
Therefore, in this paper, we propose a hybrid camera calibration framework which combines learning-based approaches with traditional methods to handle these bottlenecks.
In order to solve these problems, our method combines the two-dimensional (2-D) CNN-based real-time object detector network with spatiotemporal information.
Specifically, the encoder of a DL model that is pre-trained on the source domain is used to initialize the encoder of a reconstruction model.
Models embedded with the channel-wise attention structure always achieve better scores on sensitivity and precision than the plain Resnet models.