We present the encoder-forecaster convolutional long short-term memory (LSTM) deep-learning model that powers Microsoft Weather's operational precipitation nowcasting product.
In this paper, we start with the idea that a model must be able to understand individual objects and relationships between objects in order to generate complex scenes well.
Ranked #1 on Layout-to-Image Generation on COCO-Stuff 256x256
Understanding audio-visual content and the ability to have an informative conversation about it have both been challenging areas for intelligent systems.
Conditional text-to-image generation is an active area of research, with many possible applications.
Ranked #2 on Text-to-Image Generation on GeNeVA (i-CLEVR)
Synthesizing realistic images from text descriptions on a dataset like Microsoft Common Objects in Context (MS COCO), where each image can contain several objects, is a challenging task.
Ranked #20 on Text-to-Image Generation on COCO (Inception score metric)
However, previous work in dialogue response generation has shown that these metrics do not correlate strongly with human judgment in the non task-oriented dialogue setting.
We developed this dataset to study the role of memory in goal-oriented dialogue systems.
Natural language generation plays a critical role in spoken dialogue systems.
We consider the problem of learning preferences over trajectories for mobile manipulators such as personal robots and assembly line robots.