  In this paper, we address the problem of joint detection of objects like dog
and its semantic parts like face, leg, etc. Our model is created on top of two
Faster-RCNN models that share their features to perform a novel Attention-based
feature fusion of related Object and Part features to get enhanced
representations of both. These representations are used for final
classification and bounding box regression separately for both models. Our
experiments on the PASCAL-Part 2010 dataset show that joint detection can
simultaneously improve both object detection and part detection in terms of
mean Average Precision (mAP) at IoU=0.5.
