The DOI matches its Crossref record; 70 of the 70 references it cites were found in a registry or an index.
Segmentation-Aware Convolutional Networks Using Local Attention Masks
Open accessAbstract
We introduce an approach to integrate segmentation information within a convolutional neural network (CNN). This counter-acts the tendency of CNNs to smooth information across regions and increases their spatial precision. To obtain segmentation information, we set up a CNN to provide an embedding space where region co-membership can be estimated based on Euclidean distance. We use these embeddings to compute a local attention mask relative to every neuron position. We incorporate such masks in CNNs and replace the convolution operation with a “segmentation-aware” variant that allows a neuron to selectively attend to inputs coming from its own region. We call the resulting network a segmentation-aware CNN because it adapts its filters at each image point according to local segmentation cues, while at the same time remaining fully-convolutional. We demonstrate the merit of our method on two widely different dense prediction tasks, that involve classification (semantic segmentation) and regression (optical flow). Our results show that in semantic segmentation we can replace DenseCRF inference with a cascade of segmentation-aware filters, and in optical flow we obtain clearly sharper responses than the ones obtained with comparable networks that do not use segmentation. In both cases segmentation-aware convolution yields systematic improvements over strong baselines.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · cited 1×
“DeepLabV2 is a fullyconvolutional version of a 101-layer residual network (ResNet) [24], modified with atrous spatial pyramid pooling and multi-scale input processing.”
BackgroundVery Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148 · cited 1×
“This progress has been built on the remarkable success of CNNs in image classification tasks [29, 50] – indeed, most dense prediction models are based closely on architectures that were successful in object recognition.”
BackgroundImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · cited 1×
“This progress has been built on the remarkable success of CNNs in image classification tasks [29, 50] – indeed, most dense prediction models are based closely on architectures that were successful in object recognition.”
BackgroundMicrosoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
Fully convolutional networks for semantic segmentationPreprint
Long, J., Shelhamer, E., & Darrell, T. (2014) · Computer Vision and Pattern Recognition · doi:10.48550/arxiv.1605.06211 · Cited by 43,601 · cited 3×
“For example, one effective approach is to add trainable upsampling stages to the network, via “deconvolution” layers [39, 34].”
BackgroundMethodImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · cited 2×
“Both networks are initial- ized with weights learned on ImageNet [46], then trained on the Microsoft COCO training and validation sets [33], and finally fine-tuned on the PASCAL images [16, 21].”
MethodFully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016) · arXiv · doi:10.48550/arxiv.1606.00915 · Cited by 22,392 · cited 5×
“Experiments are carried out with two different baseline networks, “DeepLab” [9] and “DeepLabV2” [10].”
MethodInfluentialSelf-citationChen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699184 · Cited by 22,392 · cited 5×
“Experiments are carried out with two different baseline networks, “DeepLab” [9] and “DeepLabV2” [10].”
MethodInfluentialSelf-citationNormalized cuts and image segmentation
Shi, J., & Malik, J. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.868688 · Cited by 15,935
Flagged first, then by citations
Research works printed but not linked: 1
- everingham, 2012, The pascal visual object classes (voc) challenge 2012 (voc 2012) results
Cited by
Sorted by citations
Wang, X., Girshick, R., Gupta, A., & He, K. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00813 · Cited by 11,596
Image Inpainting for Irregular Holes Using Partial Convolutions
Liu, G., Reda, F. A., Shih, K. J., Wang, T.-C., Tao, A., & Catanzaro, B. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01252-6_6 · Cited by 2,419
“The partial convolution at every location, similarly defined in [7], is expressed as:”
BackgroundMethodInfluentialYOLACT: Real-Time Instance Segmentation
Bolya, D., Zhou, C., Xiao, F., & Lee, Y. J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00925 · Cited by 2,536
Huang, Z., Huang, L., Gong, Y., Huang, C., & Wang, X. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00657 · Cited by 1,209
Dynamic Neural Networks: A Survey
Han, Y., Huang, G., Song, S., Yang, L., Wang, H., & Wang, Y. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3117837 · Cited by 848
“For example, segmentation-aware convolutional network [108] applies locally masked convolution to aggregate information with larger weights from”
BackgroundMethodInfluential
Metadata
- Authors
- Harley, A. W., Derpanis, K. G., & Kokkinos, I.
- Affiliations
- Carnegie Mellon University (US)
- Toronto Metropolitan University (CA)
- Meta (United States) (US)
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 5048-5057
- OpenAlex
- W2747768235
- Cited by
- 142
- References
- 67
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 8 · 2023: 22 · 2022: 14 · 2021: 26 · 2020: 20 · 2019: 28 · 2018: 16
- FWCI
- 5.04
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- adaptive filtering, semantic segmentation, optical flow, dense prediction, spatial precision, fully convolutional network
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