Abstract:
In recent times, self-supervised visual representation learning has made considerable
advancements. But no such studies are available which determine the extent to which
features learned through self-supervised pre-training on the ImageNet dataset can
generalize to distinct tasks. Labelled data is one of the major challenges faced in the field
of vehicle re-Identification. So, we have decided to perform this analytical study on the
downstream task of Vehicle Re-Identification. In our model, architecture is based on two
steps to address this problem and assess the transfer performance of SSL method on
Vehicle Re-identification task. The first step is to pretrain the self-supervised model,
which in our case SimCLRv-2, on ImageNet to learn representations from this data. The
pre-training phase leverages unlabeled data to initialize the model and learn
representations. The second step is to use these pre trained weights from the intial step to
perform vehicle re-identification task. In this step, we have used fully connected
convolutional layers on top of the frozen features that are acquired from pretrained
model. We have used Veri-WILD dataset to further tarin and test the model. We have
experimented with pretraining and without pretraining the model. Our experiments
provide evidence that the inclusion of self-supervised pre-training results in quicker
convergence and less training time in comparison to training without pre-training. The
evaluation metric that we have used is mean Average Precision (mAP%). This strategy
has helped us achieve more accurate results, by using both learned representations as well
as a few annotations present in the data.
