Global Structure Graph Guided Fine-Grained Vehicle Recognition

Fine-grained vehicle recognition is a challenging problem due to the subtle intra-category appearance variation, which requires the recognition model can capture discriminative features from distinguishing regions. The structure is an important characteristic of vehicles which can help to find substantial parts and learn distinguishing representations. In this paper, we propose an approach that introduces the structure graph into consideration to learn distinguishing representations for vehicle recognition. Our proposed method first constructs a global structure graph from the features generated by the convolutional network and then it applies the graph as the guidance to produce effective representations of vehicles. The results of extensive experiments demonstrate that our proposed method can produce more promising results than other state-of-the-art methods. The results of the visualization illustrate that our approach can construct a suitable structure graph and the global structure information facilitates learning discriminative representations at crucial parts of vehicles.