Incremental few-shot object detection aims at detecting novel classes without forgetting knowledge of the base classes with only a few labeled training data from the novel classes. Most related prior works are on incremental object detection that rely on the availability of abundant training samples per novel class that substantially limits the scalability to real-world setting where novel data can be scarce. In this paper, we propose the Incremental-DETR that does incremental few-shot object detection via fine-tuning and self-supervised learning on the DETR object detector. To alleviate severe over-fitting with few novel class data, we first fine-tune the class-specific components of DETR with self-supervision from additional object proposals generated using Selective Search as pseudo labels. We further introduce an incremental few-shot fine-tuning strategy with knowledge distillation on the class-specific components of DETR to encourage the network in detecting novel classes without forgetting the base classes. Extensive experiments conducted on standard incremental object detection and incremental few-shot object detection settings show that our approach significantly outperforms state-of-the-art methods by a large margin.
翻译:增量微粒物体探测旨在探测新类,同时不忘记对基础类的了解,只有几个新类的标签培训数据。大多数相关的先前工作是增量物体探测,依靠每个新类的大量培训样本,大大限制可扩缩到新数据稀少的现实世界环境。在本文件中,我们建议增加-DETR,通过微调和自我监督的学习,在DETR物体探测器上增加微粒物体探测。为了减轻对少数新类数据的严重过度应用,我们首先从使用选择性搜索假标签产生的额外对象建议中用自我监督对DETR的类别特定组成部分进行微调。我们进一步引入了对DERTR特定类别组成部分进行知识蒸馏的增量微微微调整战略,以鼓励网络在不忘记基础类的情况下探测新类。在标准增量物体探测和增量微点物体探测设置上进行的广泛实验表明,我们的方法大大超出以大幅度方式生成的状态。</s>