  This paper introduces a novel approach to leverage features learned from both
supervised and self-supervised paradigms, to improve image classification
tasks, specifically for vehicle classification. Two state-of-the-art
self-supervised learning methods, DINO and data2vec, were evaluated and
compared for their representation learning of vehicle images. The former
contrasts local and global views while the latter uses masked prediction on
multi-layered representations. In the latter case, supervised learning is
employed to finetune a pretrained YOLOR object detector for detecting vehicle
wheels, from which definitive wheel positional features are retrieved. The
representations learned from these self-supervised learning methods were
combined with the wheel positional features for the vehicle classification
task. Particularly, a random wheel masking strategy was utilized to finetune
the previously learned representations in harmony with the wheel positional
features during the training of the classifier. Our experiments show that the
data2vec-distilled representations, which are consistent with our wheel masking
strategy, outperformed the DINO counterpart, resulting in a celebrated Top-1
classification accuracy of 97.2% for classifying the 13 vehicle classes defined
by the Federal Highway Administration.
