Attention-based Joint Detection of Object and Semantic Part
This addresses the problem of enhancing object and part detection accuracy for computer vision applications, representing an incremental improvement over existing methods.
The paper tackles joint detection of objects and their semantic parts by proposing an attention-based feature fusion model built on Faster-RCNN, which improves both object and part detection on the PASCAL-Part 2010 dataset, achieving gains in mean Average Precision (mAP) at IoU=0.5.
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.