# Attention-based Joint Detection of Object and Semantic Part Source: [Attention-based Joint Detection of Object and Semantic Part]() Collection index: [llms.txt]() Data updated: 2026-01-04T03:18:38.962Z Authors: Keval Morabia, Jatin Arora, Tara Vijaykumar Published: 2020-07-05T18:54:10.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Animal Object Detection on PASCAL-Parts VOC 2010 (val)](): Animal Object Detection; PASCAL-Parts VOC 2010 (val); mAP@0.5: 87.5. - [Animal Part Detection on PASCAL-Parts VOC 2010 (val)](): Animal Part Detection; PASCAL-Parts VOC 2010 (val); mAP@0.5: 52. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()