  This paper introduces a plug-and-play descriptor that can be effectively
adopted for image retrieval tasks without prior initialization or preparation.
The description method utilizes the recently proposed Vision Transformer
network while it does not require any training data to adjust parameters. In
image retrieval tasks, the use of Handcrafted global and local descriptors has
been very successfully replaced, over the last years, by the Convolutional
Neural Networks (CNN)-based methods. However, the experimental evaluation
conducted in this paper on several benchmarking datasets against 36
state-of-the-art descriptors from the literature demonstrates that a neural
network that contains no convolutional layer, such as Vision Transformer, can
shape a global descriptor and achieve competitive results. As fine-tuning is
not required, the presented methodology's low complexity encourages adoption of
the architecture as an image retrieval baseline model, replacing the
traditional and well adopted CNN-based approaches and inaugurating a new era in
image retrieval approaches.
