Global Topology Constraint Network for Fine-Grained Vehicle Recognition

Abstract

Fine-grained vehicle recognition is challenging due to the large intra-class variation in vehicle pose and viewpoint. Many existing methods, especially convolution neural network (CNN)-based methods, solve this problem via detecting and aligning parts individually, and do not consider the interaction between parts, which is very important for effective part detection and vehicle recognition. In this paper, we propose a global topology constraint network for fine-grained vehicle recognition, which adopts the constraint of global topology relationship to depict the interaction between parts and integrates it into CNN in an efficient way. Different CNNs for image classification truncated at intermediate layer can be used for part detection. The global topology relationship between parts is encoded into kernel of depthwise convolution layer, and can be learned from training images. The response under global topology constraint reflects the probability of topology relationship between parts existing in input image. All responses build the description for classification with translation invariance. Through training the whole network, the back-propagation of gradient information of kernel for global topology relationship will guide former layers to better detect useful parts, and thereby improve vehicle recognition. Our proposed method does not require additional annotation, such as bounding box or part annotation, and can be trained in an end-to-end way. We conduct comparison experiments on public Stanford Cars and CompCars datasets, which both show that our method achieves the state-of-the-art performance.