The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Pedestrian Detection with Unsupervised Multi-stage Feature Learning
Abstract
Pedestrian detection is a problem of considerable practical interest. Adding to the list of successful applications of deep learning methods to vision, we report state-of-the-art and competitive results on all major pedestrian datasets with a convolutional network model. The model uses a few new twists, such as multi-stage features, connections that skip layers to integrate global shape information with local distinctive motif information, and an unsupervised method based on convolutional sparse coding to pre-train the filters at each stage.
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Cites
ImageNet classification with deep convolutional neural networks
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Self-citationHistograms of Oriented Gradients for Human Detection
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A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
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Object Detection with Discriminatively Trained Part-Based Models
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Greedy layer-wise training of deep networks
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Sparse coding with an overcomplete basis set: A strategy employed by V1?
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What is the best multi-stage architecture for object recognition?
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Self-citationOriginal approach for the localisation of objects in images
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Cited by
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
Fully convolutional networks for semantic segmentation
Shelhamer, E., Long, J., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572683 · Cited by 11,898
Playing Atari with Deep Reinforcement Learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013) · arXiv · doi:10.48550/arxiv.1312.5602 · Cited by 9,471
Self-citationLiu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., & Xie, S. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01167 · Cited by 8,494
Metadata
- Authors
- Sermanet, P., Kavukcuoglu, K., Chintala, S., & Lecun, Y.
- Venue
- 2013 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2013-06
- Pages
- 3626-3633
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2162741153
- Cited by
- 742
- References
- 9
- FWCI
- 56.53
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Sermanet, P., Kavukcuoglu, K., Chintala, S., & Lecun, Y. (2013). Pedestrian Detection with Unsupervised Multi-stage Feature Learning. 2013 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2013.465
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