Search Results for author:
Found 6 papers, 2 papers with code
Self-Ordering Point Clouds
In this paper we address the task of finding representative subsets of points in a 3D point cloud by means of a point-wise ordering.
Less than Few: Self-Shot Video Instance Segmentation
The goal of this paper is to bypass the need for labelled examples in few-shot video understanding at run time.
Few-Shot Transformation of Common Actions into Time and Space
This paper introduces the task of few-shot common action localization in time and space.
PointMixup: Augmentation for Point Clouds
In this paper, we define data augmentation between point clouds as a shortest path linear interpolation.
Ranked #3 on
3D Point Cloud Data Augmentation
on ModelNet40
3D Point Cloud Classification
3D Point Cloud Data Augmentation
+2
Localizing the Common Action Among a Few Videos
The start and end of an action in a long untrimmed video is determined based on just a hand-full of trimmed video examples containing the same action, without knowing their common class label.
Attention-Based Multi-Context Guiding for Few-Shot Semantic Segmentation
Few-shot learning is a nascent research topic, motivated by the fact that traditional deep learning methods require tremen- dous amounts of data.
Ranked #1 on
Few-Shot Semantic Segmentation
on Pascal5i
