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Sep 12, 2019 · In this paper, we present a Robust Faster R-CNN, an effective object detection framework for detecting objects with occlusion and objects with ...
The framework is based on Faster R-CNN architecture. We improve the Faster R-CNN by replacing ROIpoolings with ROIAligns to remove the harsh quantization of ...
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Recognizing objects at vastly different scales and objects with occlusion is a fundamental challenge in computer vision. In this paper, we propose a novel ...
Oct 29, 2023 · Our extensive ablation study rigorously examines the influence of synthetic perturbations on the performance of object identification models ...
A-fast-rcnn: Hard positive generation via adversary for object detection. Pro- ceedings of the conference on Computer Vision and Pattern. Recognition (CVPR) ...
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Feb 23, 2016 · In my dataset I have a small number of high resolution images, with a large number of objects per image (the images are 5000x5000 pixels and the ...
Sep 20, 2019 · This paper addresses this issue by proposing a novel approach denoted as Robust Faster R-CNN for detecting objects in multi-label images. Robust ...
May 26, 2021 · The proposed algorithm generates a “confidence score” for each frame to check the trustworthiness of the BB generated by the CNN detector. As a ...
Faster R-CNN [37] is a popular algorithm in object detection that works in two stages: regions of interest selection with Region Proposal Network. (RPN) is ...
Sep 20, 2018 · The short answer is that there has been a lot of progress in the field of object detection and Faster R-CNN is no longer state of the art.
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