This paper introduces a new problem in 3D point cloud: few-shot instance segmentation. Given a few annotated point clouds exemplified a target class, our goal is to segment all instances of this target class in a query point cloud. This problem has a wide range of practical applications ...
现在很多物体级SLAM的工程落地都需要能够识别场景中的所有物体,但如果遇到一个新目标就重新训练的话,成本太高了,所以zero-shot、one-shot、few-shot就非常重要。 今天笔者将带领大家阅读ETHZ的最近开源工作ISAR,包括一个few-shot实例分割数据集,以及一个同时实现实例分割和重识别的few-shot新baseline,ISAR这个名字也是...
文章链接: Incremental Few-Shot Instance Segmentation 代码地址:github.com/danganea/iMT 1.本文创新点: 首次提出了小样本实例分割的增量学习方法iMTFA,超过了目前小样本实例分割和增量小样本目标检测的SOTA方法 。 同时为了比较增量式与非增量式方法,将非增量的小样本目标检测TFA扩展到实例分割任务,提出的MTFA也达到...
FGN: Fully Guided Network for Few-Shot Instance Segmentation Zhibo Fan1, Jin-Gang Yu1,2,∗, Zhihao Liang1, Jiarong Ou1, Changxin Gao3, Gui-Song Xia4, Yuanqing Li1,2 1South China University of Technology 2Guangzhou Laboratory 3Huazhong University of Science and Te...
Paper tables with annotated results for Reference Twice: A Simple and Unified Baseline for Few-Shot Instance Segmentation
Form segmentations and mix up, aiming at eliminates the back ground noise. [ICCV 2019] (paper) Boosting Few-Shot Visual Learning with Self-Supervision Self-supervision means to rotate itself, and compute two losses. Self-training [NIPS 2019] (paper) Learning to Self-Train for Semi-Supervised...
Geodesic-Former: a Geodesic-Guided Few-shot 3D Point Cloud Instance Segmenter Abstract: This paper introduces a new problem in 3D point cloud: few-shot instance segmentation. Given a few annotated point clouds characterizing a target class, our goal is to segment all instances of this target cla...
Few-shot semantic segmentation aims at training a model that can segment novel classes in a query image with only a few densely annotated support exemplars. It remains a challenge because of large intra-class variations between the support and query imag
Incremental Few-Shot Instance Segmentation Dan Andrei Ganea, Bas Boom, Ronald Poppe 本文提出一种针对小样本的实例分割算法MTFA,以及对应的增量算法iMTFA, 效果达到STOA。instance feature的平均值被保存如模型,最终通过余弦距离做最终分类, MTFA在已有的TFA算法基础上增加mask分支,同样采用双阶段训练,第一阶段...
n-way k-shot的意思是:support points包含n个类别,每个类别提供k个point clouds,通过embedding network提取到support points特征以后,与query points的特征做对比,实现分割任务。 开山之作:Few-shot 3D Point Cloud Semantic Segmentation 2021 CVPR 支持集特征聚合成多原型,计算原型与查询集特征的亲和性,构造k-nn图,...