Few-shot learning has recently emerged as a new challenge in the deep learning field: unlike conventional methods that train the deep neural networks (DNNs) with a large number of labeled data, it asks for the generalization of DNNs on new classes with few annotated samples. Recent advances in few-shot learning mainly focus on image classification while in this paper we focus on object detection. The initial explorations in few-shot object detection tend to simulate a classification scenario by using the positive proposals in images with respect to certain object class while discarding the negative proposals of that class. Negatives, especially hard negatives, however, are essential to the embedding space learning in few-shot object detection. In this paper, we restore the negative information in few-shot object detection by introducing a new negative- and positive-representative based metric learning framework and a new inference scheme with negative and positive representatives. We build our work on a recent few-shot pipeline RepMet with several new modules to encode negative information for both training and testing. Extensive experiments on ImageNet-LOC and PASCAL VOC show our method substantially improves the state-of-the-art few-shot object detection solutions. Our code is available at https://github.com/yang-yk/NP-RepMet.
翻译:在深层学习领域,最近出现了一项新的挑战:与用大量标签数据培训深神经网络的传统方法不同,它要求将DNN用于新类别,并附有少量附加说明的样本。最近一些短片学习的进展主要侧重于图像分类,而在本文中我们侧重于物体探测。一些短片物体探测的初步探索倾向于模拟一种分类设想,方法是使用某些物体类图像中的正面建议,同时放弃该类的负面建议。但是,负面,特别是硬负是将空间学习嵌入微粒物体探测中的关键。在本文中,我们通过采用新的负和正代表性的参数学习框架和新的推论计划,恢复少数物体探测中的负面信息。我们的工作建立在最近几发的管道RepetMetMet上,用几个新模块来为培训和测试输入负面信息。关于图像Net-LOC和PASAL VOC的大规模实验,对于在微粒物体探测中嵌入空间学习至关重要。我们在微粒子物体探测中展示了我们的方法,即引入了以负和正反向/正方码。