  Understanding vehicles in images is important for various applications such
as intelligent transportation and self-driving system. Existing vehicle-centric
works typically pre-train models on large-scale classification datasets and
then fine-tune them for specific downstream tasks. However, they neglect the
specific characteristics of vehicle perception in different tasks and might
thus lead to sub-optimal performance. To address this issue, we propose a novel
vehicle-centric pre-training framework called VehicleMAE, which incorporates
the structural information including the spatial structure from vehicle profile
information and the semantic structure from informative high-level natural
language descriptions for effective masked vehicle appearance reconstruction.
To be specific, we explicitly extract the sketch lines of vehicles as a form of
the spatial structure to guide vehicle reconstruction. The more comprehensive
knowledge distilled from the CLIP big model based on the similarity between the
paired/unpaired vehicle image-text sample is further taken into consideration
to help achieve a better understanding of vehicles. A large-scale dataset is
built to pre-train our model, termed Autobot1M, which contains about 1M vehicle
images and 12693 text information. Extensive experiments on four vehicle-based
downstream tasks fully validated the effectiveness of our VehicleMAE. The
source code and pre-trained models will be released at
https://github.com/Event-AHU/VehicleMAE.
