Deep Neural Networks for Object Detection
@inproceedings{Szegedy2013DeepNN, title={Deep Neural Networks for Object Detection}, author={Christian Szegedy and Alexander Toshev and D. Erhan}, booktitle={NIPS}, year={2013} }
Deep Neural Networks (DNNs) have recently shown outstanding performance on image classification tasks [14. [] Key Method We define a multi-scale inference procedure which is able to produce high-resolution object detections at a low cost by a few network applications. State-of-the-art performance of the approach is shown on Pascal VOC.
1,203 Citations
Structured Prediction for Object Detection in Deep Neural Networks
- Computer ScienceICANN
- 2014
This work proposes to adapt a structured loss function for neural network training which directly maximizes overlap of the prediction with ground truth bounding boxes, and shows how this structured loss can be implemented efficiently.
Deep learning for class-generic object detection
- Computer ScienceICLR
- 2014
It is shown that neural networks originally designed for image recognition can be trained to detect objects within images, regardless of their class, including objects for which no bounding box labels have been provided.
Scalable Object Detection Using Deep Neural Networks
- Computer Science2014 IEEE Conference on Computer Vision and Pattern Recognition
- 2014
This work proposes a saliency-inspired neural network model for detection, which predicts a set of class-agnostic bounding boxes along with a single score for each box, corresponding to its likelihood of containing any object of interest.
A Novel Representation and Pipeline for Object Detection
- Computer Science
- 2016
A novel training criterion is proposed which tackles background separately for object detection and how Learning without Forgetting and finetuning perform in transferring from the classification to the detection task is examined.
Improving object detection with deep convolutional networks via Bayesian optimization and structured prediction
- Computer Science2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- 2015
This work addresses the localization problem by using a search algorithm based on Bayesian optimization that sequentially proposes candidate regions for an object bounding box, and training the CNN with a structured loss that explicitly penalizes the localization inaccuracy.
AttentionNet: Aggregating Weak Directions for Accurate Object Detection
- Computer Science2015 IEEE International Conference on Computer Vision (ICCV)
- 2015
AttentionNet is presented, a novel detection method using a deep convolutional neural network, named AttentionNet, which detects objects without any separated models from the object proposal to the post bounding-box regression.
Learning to decompose for object detection and instance segmentation
- Computer ScienceArXiv
- 2015
This work proposes a novel end-to-end trainable deep neural network architecture that generates the correct number of object instances and their bounding boxes (or segmentation masks) given an image, using only a single network evaluation without any pre- or post-processing steps.
Efficient ConvNet for Surface Object Recognition
- Computer ScienceICIRA
- 2019
A dynamic-selecting criterion approach is proposed to prune a trained Yolo-v2 model to deal with drawbacks caused by redundant parameters in network and it is shown that this approach can reduce inference costs for Yolo -v2 by up to 65% on it while regaining close to the original performance by retraining the network.
Improving object detection via improving accuracy of object localization
- Computer Science2016 IEEE/CIC International Conference on Communications in China (ICCC)
- 2016
This work combines a high-recall algorithm proposing candidate regions for an object bounding box with an algorithm reducing localization bias, and utilizing box alignment which penalizing deviation via taking object boundaries into account, to instruct the procedure of proposing input of CNN.
Part Detector Discovery in Deep Convolutional Neural Networks
- Computer ScienceACCV
- 2014
This paper shows how pre-trained convolutional neural networks can be used for robust and efficient object part discovery and localization without the necessity to actually train the network on the current dataset.
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