The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Spatially Adaptive Computation Time for Residual Networks
Abstract
This paper proposes a deep learning architecture based on Residual Network that dynamically adjusts the number of executed layers for the regions of the image. This architecture is end-to-end trainable, deterministic and problem-agnostic. It is therefore applicable without any modifications to a wide range of computer vision problems such as image classification, object detection and image segmentation. We present experimental results showing that this model improves the computational efficiency of Residual Networks on the challenging ImageNet classification and COCO object detection datasets. Additionally, we evaluate the computation time maps on the visual saliency dataset cat2000 and find that they correlate surprisingly well with human eye fixation positions.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
ImageNet 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
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
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
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016) · arXiv · doi:10.48550/arxiv.1606.00915 · Cited by 22,392
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_38 · Cited by 10,741
Identity Mappings in Deep Residual Networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · arXiv · doi:10.48550/arxiv.1603.05027 · Cited by 10,665
Research works printed but not linked: 29
- 10.1109/CVPR.2015.7298594 · DOI · Paper page
- simonyan, 2015, Very deep convolutional networks for large-scale image recognition, ICLRE
- mnih, 2014, Recurrent models of visual attention, NIPS
- 10.1109/CVPR.2015.7298965 · DOI · Paper page
- lin, 2014, Microsoft coco: Common objects in context, ECCV
- lin, 2014, Network in network, ICLRE
- sharma, 2016, Action recognition using visual attention, ICLR Workshop
- 10.1080/135062800394667 · DOI · Paper page
- figurnov, 2016, PerforatedCNNs: Acceleration through elimination of redundant convolutions, NIPS
- 10.1109/ICPR.2016.7900006 · DOI · Paper page
- greff, 2017, Highway and residual networks learn unrolled iterative estimation, ICLRE
- han, 2016, Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding, ICLRE
- huang, 2016, Deep networks with stochastic depth, ECCV
- li, 2016, Videolstm convolves attends and flows for action recognition
- bylinskii, 0, MIT Saliency Benchmark
- 10.1109/CVPR.2015.7299170 · DOI · Paper page
- bengio, 2016, Conditional computation in neural networks for faster models, ICLR Workshop
- liao, 2016, Bridging the gaps between residual learning recurrent neural networks and visual cortex
- bylinskii, 2016, What do different evaluation metrics tell us about saliency models?
- dai, 2016, R-fcn: Object detection via region-based fully convolutional networks, NIPS
- ba, 2015, Multiple object recognition with visual attention, ICLRE
- 10.1109/CVPR.2015.7298761 · DOI · Paper page
- almahairi, 2016, Dynamic capacity networks, ICML
- jaderberg, 2015, Spatial transformer networks, NIPS
- jaderberg, 2014, Speeding up convolutional neural networks with low rank expansions, BMVC
- kruthiventi, 2015, Deepfix A fully convolutional neural network for predicting human eye fixations
- zhang, 2015, Character-level convolutional networks for text classification, NIPS
- larochelle, 2010, Learning to combine foveal glimpses with a third-order boltzmann machine, NIPS
- larochelle, 2016, My notes on adaptive computation time for recurrent neural networks
Cited by
Neural ordinary differential equations
Chen, R. T. Q., Rubanova, Y., Bettencourt, J., & Duvenaud, D. (2018) · arXiv · doi:10.48550/arxiv.1806.07366 · Cited by 2,097
CondenseNet: An Efficient DenseNet Using Learned Group Convolutions
Huang, G., Liu, S., Maaten, L. V. D., & Weinberger, K. Q. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00291 · Cited by 904
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
Distributed Deep Neural Networks Over the Cloud, the Edge and End Devices
Teerapittayanon, S., McDanel, B., & Kung, H. T. (2017) · 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2017.226 · Cited by 843
Patient Knowledge Distillation for BERT Model Compression
Sun, S., Cheng, Y., Gan, Z., & Liu, J. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1441 · Cited by 631
Metadata
- Authors
- Figurnov, M., Collins, M. D., Zhu, Y., Zhang, L., Huang, J., Vetrov, D., & Salakhutdinov, R.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 1790-1799
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2562731582
- Cited by
- 329
- References
- 16
- FWCI
- 9.06
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Spatially Adaptive Computation Time for Residual Networks. https://app.cochat.ai/papers/10.1109/cvpr.2017.194
- The paper
- Figurnov, M., Collins, M. D., Zhu, Y., Zhang, L., Huang, J., Vetrov, D., & Salakhutdinov, R. (2017). Spatially Adaptive Computation Time for Residual Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2017.194
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
