@Article{DiegoVelazquez2021,
author="Diego Velazquez
and Josep M. Gonfaus
and Pau Rodriguez
and Xavier Roca
and Seiichi Ozawa
and Jordi Gonzalez",
title="Logo Detection With No Priors",
journal="IEEE Access",
year="2021",
volume="9",
pages="106998--107011",
abstract="In recent years, top referred methods on object detection like R-CNN have implemented this task as a combination of proposal region generation and supervised classification on the proposed bounding boxes. Although this pipeline has achieved state-of-the-art results in multiple datasets, it has inherent limitations that make object detection a very complex and inefficient task in computational terms. Instead of considering this standard strategy, in this paper we enhance Detection Transformers (DETR) which tackles object detection as a set-prediction problem directly in an end-to-end fully differentiable pipeline without requiring priors. In particular, we incorporate Feature Pyramids (FP) to the DETR architecture and demonstrate the effectiveness of the resulting DETR-FP approach on improving logo detection results thanks to the improved detection of small logos. So, without requiring any domain specific prior to be fed to the model, DETR-FP obtains competitive results on the OpenLogo and MS-COCO datasets offering a relative improvement of up to 30\%, when compared to a Faster R-CNN baseline which strongly depends on hand-designed priors.",
optnote="ISE",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3664), last updated on Thu, 15 Sep 2022 10:21:46 +0200",
doi="10.1109/ACCESS.2021.3101297",
opturl="https://ieeexplore.ieee.org/document/9502074"
}