论文标题
学习和汇总深层的本地描述符，以实例级别的识别
Learning and aggregating deep local descriptors for instance-level recognition
论文作者
论文摘要
我们提出了一种有效的方法来学习深度的本地描述符，以实现实例级别的识别。该培训仅需要正面图像对和负面图像对的示例，并且作为汇总全局图像描述符的度量学习。在推断时，本地描述符由网络内部组件的激活提供。我们证明了为什么这种方法学习了与经典有效匹配内核方法相似性估计效果很好的本地描述符。实验验证研究了基于匹配内核的最先进图像搜索方法的性能和记忆要求之间的权衡。与现有的本地描述符相比，提议的符号在两个实例级识别任务中的表现更好，并使内存要求较低。我们在实验上表明，全球描述符在大规模上不够有效，并且本地描述符至关重要。即使在某些情况下，我们达到了最先进的性能，即使具有与RESNET18一样小的骨干网络。
We propose an efficient method to learn deep local descriptors for instance-level recognition. The training only requires examples of positive and negative image pairs and is performed as metric learning of sum-pooled global image descriptors. At inference, the local descriptors are provided by the activations of internal components of the network. We demonstrate why such an approach learns local descriptors that work well for image similarity estimation with classical efficient match kernel methods. The experimental validation studies the trade-off between performance and memory requirements of the state-of-the-art image search approach based on match kernels. Compared to existing local descriptors, the proposed ones perform better in two instance-level recognition tasks and keep memory requirements lower. We experimentally show that global descriptors are not effective enough at large scale and that local descriptors are essential. We achieve state-of-the-art performance, in some cases even with a backbone network as small as ResNet18.
