R-CNN minus R
@inproceedings{Lenc2015RCNNMR, title={R-CNN minus R}, author={Karel Lenc and Andrea Vedaldi}, booktitle={BMVC}, year={2015} }
Deep convolutional neural networks (CNNs) have had a major impact in most areas of image understanding, including object category detection. [] Key Method We do so by designing and evaluating a detector that uses a trivial region generation scheme, constant for each image. Combined with SPP, this results in an excellent and fast detector that does not require to process an image with algorithms other than the CNN itself. We also streamline and simplify the training of CNN-based detectors by integrating…
80 Citations
R-FCN: Object Detection via Region-based Fully Convolutional Networks
- Computer ScienceNIPS
- 2016
This work presents region-based, fully convolutional networks for accurate and efficient object detection, and proposes position-sensitive score maps to address a dilemma between translation-invariance in image classification and translation-variance in object detection.
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- Computer ScienceIEEE Transactions on Pattern Analysis and Machine Intelligence
- 2015
This work introduces a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals and further merge RPN and Fast R-CNN into a single network by sharing their convolutionAL features.
Deep Learning Techniques—R-CNN to Mask R-CNN: A Survey
- Computer ScienceComputational Intelligence in Pattern Recognition
- 2019
An overview of the last update in region-based convolutional neural network (R-CNN) and their practical applications and its classification for ease of understanding is presented and the performances and challenges of these techniques in terms of speed, accuracy, or simplicity are compared.
Study of object detection based on Faster R-CNN
- Computer Science, Engineering2017 Chinese Automation Congress (CAC)
- 2017
Numerical results show that Faster R-CNN trained by PVANET network obtained the highest mAP, and a better model can be obtained by comparing the experimental results using mean average precision (mAP) as an evaluation index.
Object Detection Networks on Convolutional Feature Maps
- Computer ScienceIEEE Transactions on Pattern Analysis and Machine Intelligence
- 2017
It is shown by experiments that despite the effective ResNets and Faster R-CNN systems, the design of NoCs is an essential element for the 1st-place winning entries in ImageNet and MS COCO challenges 2015.
Deep learning based multi-category object detection in aerial images
- Computer Science, Environmental ScienceDefense + Security
- 2017
This work proposes a deep neural network derived from the Faster R-CNN approach for multi- category object detection in aerial images and shows how the detection accuracy can be improved by replacing the network architecture by an architecture especially designed for handling small object sizes.
DeepID-Net: Object Detection with Deformable Part Based Convolutional Neural Networks
- Computer ScienceIEEE Transactions on Pattern Analysis and Machine Intelligence
- 2017
By changing the net structures, training strategies, adding and removing some key components in the detection pipeline, a set of models with large diversity are obtained, which significantly improves the effectiveness of model averaging.
G-CNN: An Iterative Grid Based Object Detector
- Computer Science2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- 2016
G-CNN, an object detection technique based on CNNs which works without proposal algorithms, makes detection faster by removing the object proposal stage as well as reducing the number of boxes to be processed.
A comprehensive survey on convolutional neural network in medical image analysis
- Computer ScienceMultimedia Tools and Applications
- 2020
This paper provides a review on CNN from the perspectives of its basic mechanism introduction, structure, typical architecture and main application in medical image analysis through analyzing over 100 references from Google Scholar, PubMed, Web of Science and various sources published from 1958 to 2020.
Part-based convolutional neural network for visual recognition
- Computer Science2017 IEEE International Conference on Image Processing (ICIP)
- 2017
A method to discover discriminative elements based on deep Convolutional Neural Networks (CNNs), namely Part-based CNN (P-CNN), which acts as the role of encoding module in part-based representation, is presented.
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