Search Results for author:
Found 3 papers, 2 papers with code
SEE-Few: Seed, Expand and Entail for Few-shot Named Entity Recognition
Current few-shot NER methods focus on leveraging existing datasets in the rich-resource domains which might fail in a training-from-scratch setting where no source-domain data is used.
Enforcing Deterministic Constraints on Generative Adversarial Networks for Emulating Physical Systems
Recently, GANs have been used to emulate complex physical systems such as turbulent flows.
Driving Scene Perception Network: Real-time Joint Detection, Depth Estimation and Semantic Segmentation
As the demand for enabling high-level autonomous driving has increased in recent years and visual perception is one of the critical features to enable fully autonomous driving, in this paper, we introduce an efficient approach for simultaneous object detection, depth estimation and pixel-level semantic segmentation using a shared convolutional architecture.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
