With the growth of the economy and society, enterprises, especially in the FinTech industry, have increasing demands of outbound calls for customers such as debt collection, marketing, anti-fraud calls, and so on.
Our results show improved performance in counting and localization of objects in 2D and 3D microscopy data.
Annotation is a major hurdle in the semantic segmentation of microscopy images and volumes due to its prerequisite expertise and effort.
Trajectory forecasting is a crucial step for autonomous vehicles and mobile robots in order to navigate and interact safely.
To this end, the network first refines the poses before they are further processed to recognize the action.
Real-time semantic segmentation of LiDAR data is crucial for autonomously driving vehicles, which are usually equipped with an embedded platform and have limited computational resources.
Ranked #2 on Real-Time 3D Semantic Segmentation on SemanticKITTI
Many point-based semantic segmentation methods have been designed for indoor scenarios, but they struggle if they are applied to point clouds that are captured by a LiDAR sensor in an outdoor environment.
Despite the capabilities of these approaches in capturing temporal dependencies, their predictions suffer from over-segmentation errors.
Ranked #2 on Action Segmentation on Assembly101
On the other hand, fast training/testing and low computational cost are also necessary for quick deployment and development of COVID-19 screening systems, but traditional deep learning methods are usually computationally intensive.
The proposed FSN can make dense predictions at frame-level for a video clip using both spatial and temporal context information.
In particular, our method can pre-determine and remove unnecessary edges based on the joint graphical structure, referred to as JAG screening, and can decompose a large network into smaller subnetworks in a robust manner, referred to as JAG decomposition.