However, considering the variants and diverse action types in human motion data, the cross-dependency of the spatio-temporal relationships will be difficult to depict due to the decoupled modeling strategy, which may also exacerbate the problem of insufficient generalization.
Artificial intelligence (AI) based device identification improves the security of the internet of things (IoT), and accelerates the authentication process.
The designed framework circumvents the traditional forecasting step and avoids the estimation of the covariance matrix, lifting the bottleneck for generalizing to a large amount of instruments.
In this paper, we present a new perspective on the 3D human pose estimation method from point cloud sequences.
Ranked #1 on Pose Estimation on ITOP front-view
This paper presents the Rail-5k dataset for benchmarking the performance of visual algorithms in a real-world application scenario, namely the rail surface defects detection task.
We design multi-horizon forecasting models for limit order book (LOB) data by using deep learning techniques.
Market by order (MBO) data - a detailed feed of individual trade instructions for a given stock on an exchange - is arguably one of the most granular sources of microstructure information.
We also provide a theoretical explanation of our method.
The proposed framework achieves at least 40% improvement on stability evaluation metrics while enhancing detection accuracy versus state-of-the-art methods.
We develop a large-scale deep learning model to predict price movements from limit order book (LOB) data of cash equities.