We propose a novel framework, On-Demand MOtion Generation (ODMO), for generating realistic and diverse long-term 3D human motion sequences conditioned only on action types with an additional capability of customization.
In this paper, we propose an extension of this task, where the goal is to predict the logical relationship of fine-grained knowledge elements within a piece of text to an image.
The AdaReLU can dynamically adjust the slope parameters according to the target style and can be utilized to increase the controllability by combining with Adaptive Instance Normalization (AdaIN).
To that end, we propose a hybrid framework extended from traditional slate optimization to solve the conditional slate optimization problem.
Humans are incredibly good at transferring knowledge from one domain to another, enabling rapid learning of new tasks.
Offensive and abusive language is a pressing problem on social media platforms.
In this work, we study the performance of the region-based CNN for the electrical equipment defect detection by using the UAV images.