Improving Object Localization with Fitness NMS and Bounded IoU Loss

November 01, 2017 · Entered Twilight · 🏛 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

"Last commit was 7.0 years ago (≥5 year threshold)"

Evidence collected by the PWNC Scanner

Repo contents: LICENCE, README.md, bin, denet, examples, models, papers

Authors Lachlan Tychsen-Smith, Lars Petersson arXiv ID 1711.00164 Category cs.CV: Computer Vision Citations 189 Venue 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Repository https://github.com/lachlants/denet ⭐ 111 Last Checked 1 month ago

Abstract

We demonstrate that many detection methods are designed to identify only a sufficently accurate bounding box, rather than the best available one. To address this issue we propose a simple and fast modification to the existing methods called Fitness NMS. This method is tested with the DeNet model and obtains a significantly improved MAP at greater localization accuracies without a loss in evaluation rate, and can be used in conjunction with Soft NMS for additional improvements. Next we derive a novel bounding box regression loss based on a set of IoU upper bounds that better matches the goal of IoU maximization while still providing good convergence properties. Following these novelties we investigate RoI clustering schemes for improving evaluation rates for the DeNet wide model variants and provide an analysis of localization performance at various input image dimensions. We obtain a MAP of 33.6%@79Hz and 41.8%@5Hz for MSCOCO and a Titan X (Maxwell). Source code available from: https://github.com/lachlants/denet