论文标题
第三连续学习研讨会挑战以egintric类别和实例级别对象理解
3rd Continual Learning Workshop Challenge on Egocentric Category and Instance Level Object Understanding
论文作者
论文摘要
持续学习，也称为终身学习或逐步学习，最近在人工智能研究界引起了人们的兴趣。最近的研究工作迅速导致了新型算法的设计，能够减少深层神经网络中灾难性遗忘现象的影响。由于对该领域的兴趣激增，近年来已经举行了许多比赛，因为它们是刺激有希望的方向研究的绝佳机会。本文总结了在CVPR 2022的第三次连续学习（CLVISION）研讨会上所面临的挑战的想法，设计选择，规则和结果。与分类任务相比，该竞赛的重点是复杂的持续对象检测任务，在文献中，该任务仍未被淘汰。挑战是基于新型EgoObjects数据集的挑战版本，该数据集是一种大规模的以自我为中心的对象数据集，该数据集明确设计用于基准基于egipentric类别/实例级对象的持续学习算法 - 涵盖大约1K唯一的主对象，而大约100k视频视频中的250+类别涵盖了更多的主体。
Continual Learning, also known as Lifelong or Incremental Learning, has recently gained renewed interest among the Artificial Intelligence research community. Recent research efforts have quickly led to the design of novel algorithms able to reduce the impact of the catastrophic forgetting phenomenon in deep neural networks. Due to this surge of interest in the field, many competitions have been held in recent years, as they are an excellent opportunity to stimulate research in promising directions. This paper summarizes the ideas, design choices, rules, and results of the challenge held at the 3rd Continual Learning in Computer Vision (CLVision) Workshop at CVPR 2022. The focus of this competition is the complex continual object detection task, which is still underexplored in literature compared to classification tasks. The challenge is based on the challenge version of the novel EgoObjects dataset, a large-scale egocentric object dataset explicitly designed to benchmark continual learning algorithms for egocentric category-/instance-level object understanding, which covers more than 1k unique main objects and 250+ categories in around 100k video frames.
