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Vehicle Make and Model Recognition (VMMR) deals with the problem of classifying vehicles whose appearance may vary significantly when captured from different perspectives. A number of successful approaches to this problem rely on part-based models, requiring however labor-intensive parts annotations. In this work, we address the VMMR problem proposing a deep convolutional architecture built upon multi-scale attention windows. The proposed architecture classifies a vehicle over attention windows which are predicted to minimize the classification error. Through these windows, the visual representations of the most discriminative part of the vehicle are aggregated over different scales which in fact provide more representative features for the classifier. In addition, we define a loss function accounting for the joint classification error across make and model. Besides, a training methodology is devised to …