  Pool-based sampling in active learning (AL) represents a key framework for
an-notating informative data when dealing with deep learning models. In this
paper, we present a novel pipeline for pool-based Active Learning. Unlike most
previous works, our method exploits accessible unlabelled examples during
training to estimate their co-relation with the labelled examples. Another
contribution of this paper is to adapt Visual Transformer as a sampler in the
AL pipeline. Visual Transformer models non-local visual concept dependency
between labelled and unlabelled examples, which is crucial to identifying the
influencing unlabelled examples. Also, compared to existing methods where the
learner and the sampler are trained in a multi-stage manner, we propose to
train them in a task-aware jointly manner which enables transforming the latent
space into two separate tasks: one that classifies the labelled examples; the
other that distinguishes the labelling direction. We evaluated our work on four
different challenging benchmarks of classification and detection tasks viz.
CIFAR10, CIFAR100,FashionMNIST, RaFD, and Pascal VOC 2007. Our extensive
empirical and qualitative evaluations demonstrate the superiority of our method
compared to the existing methods. Code available:
https://github.com/razvancaramalau/Visual-Transformer-for-Task-aware-Active-Learning
