  The primary goal of artificial intelligence is to mimic humans. Therefore, to
advance toward this goal, the AI community attempts to imitate qualities/skills
possessed by humans and imbibes them into machines with the help of
datasets/tasks. Earlier, many tasks which require knowledge about the objects
present in an image are satisfactorily solved by vision models. Recently, with
the aim to incorporate knowledge about non-object image regions (hideouts,
turns, and other obscured regions), a task for identification of potential
hideouts termed Covert Geo-Location (CGL) detection was proposed by Saha et al.
It involves identification of image regions which have the potential to either
cause an imminent threat or appear as target zones to be accessed for further
investigation to identify any occluded objects. Only certain occluding items
belonging to certain semantic classes can give rise to CGLs. This fact was
overlooked by Saha et al. and no attempts were made to utilize semantic class
information, which is crucial for CGL detection. In this paper, we propose a
multitask-learning-based approach to achieve 2 goals - i) extraction of
features having semantic class information; ii) robust training of the common
encoder, exploiting large standard annotated datasets as training set for the
auxiliary task (semantic segmentation). To explicitly incorporate class
information in the features extracted by the encoder, we have further employed
attention mechanism in a novel manner. We have also proposed a better
evaluation metric for CGL detection that gives more weightage to recognition
rather than precise localization. Experimental evaluations performed on the CGL
dataset, demonstrate a significant increase in performance of about 3% to 14%
mIoU and 3% to 16% DaR on split 1, and 1% mIoU and 1% to 2% DaR on split 2 over
SOTA, serving as a testimony to the superiority of our approach.
