The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Attention to Scale: Scale-Aware Semantic Image Segmentation
Open accessAbstract
Incorporating multi-scale features in fully convolutional neural networks (FCNs) has been a key element to achieving state-of-the-art performance on semantic image segmentation. One common way to extract multi-scale features is to feed multiple resized input images to a shared deep network and then merge the resulting features for pixelwise classification. In this work, we propose an attention mechanism that learns to softly weight the multi-scale features at each pixel location. We adapt a state-of-the-art semantic image segmentation model, which we jointly train with multi-scale input images and the attention model. The proposed attention model not only outperforms averageand max-pooling, but allows us to diagnostically visualize the importance of features at different positions and scales. Moreover, we show that adding extra supervision to the output at each scale is essential to achieving excellent performance when merging multi-scale features. We demonstrate the effectiveness of our model with extensive experiments on three challenging datasets, including PASCAL-Person-Part, PASCAL VOC 2012 and a subset of MS-COCO 2014.
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
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
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
ImageNet 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
Azuma, R. T. (1997) · Presence: Teleoperators and Virtual Environments · doi:10.1162/pres.1997.6.4.355 · Cited by 10,121
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248
The Pascal Visual Object Classes Challenge: A Retrospective
Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-014-0733-5 · Cited by 7,139
Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2014) · arXiv · doi:10.48550/arxiv.1412.7062 · Cited by 4,213
Self-citationLearning Hierarchical Features for Scene Labeling
Farabet, C., Couprie, C., Najman, L., & LeCun, Y. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.231 · Cited by 2,620
Research works printed but not linked: 43
- mallat, 1999, A Wavelet Tour of Signal Processing
- 10.1109/CVPR.2015.7298965 · DOI · Paper page
- lee, 2015, Deeply-supervised nets, AISTATS
- krähenbühl, 2011, Efficient inference in fully connected crfs with gaussian edge potentials, NIPS
- 10.1109/ICCV.2015.162 · DOI · Paper page
- liu, 2015, Parsenet Looking wider to see better
- lin, 2014, Microsoft COCO: Common objects in context, ECCV
- he, 2014, Spatial pyramid pooling in deep convolutional networks for visual recognition, ECCV
- jia, 2014, Caffe Convolutional Architecture for Fast Feature Embedding
- 10.1109/TPAMI.2010.161 · DOI · Paper page
- 10.1109/CVPR.2014.471 · DOI · Paper page
- 10.1109/ITSC.2013.6728473 · DOI · Paper page
- 10.1007/BF00126140 · DOI
- 10.1109/CVPR.2014.81 · DOI · Paper page
- 10.1145/1964921.1964964 · DOI · Paper page
- 10.1109/ICCV.2011.6126343 · DOI · Paper page
- gregor, 2015, Draw: A recurrent neural network for image generation, ICML
- 10.1109/CVPR.2015.7298788 · DOI
- 10.1109/ICCV.2015.305 · DOI
- 10.1109/ICCV.2015.512 · DOI · Paper page
- 10.1109/ICCV.2015.164 · DOI · Paper page
- xiao, 2015, The application of two-level attention models in deep convolutional neural network for fine-grained image classification, CVPR
- 10.1109/ICCV.2015.184 · DOI
- chen, 2015, Semantic image segmentation with task-specific edge detection using cnns and a discriminatively trained domain transform
- mnih, 2014, Recurrent models of visual attention, NIPS
- chen, 2015, Learning deep structured models, ICML
- 10.1109/CVPR.2012.6248110 · DOI · Paper page
- 10.1109/ICCV.2015.191 · DOI · Paper page
- 10.1109/ICCV.2015.304 · DOI · Paper page
- evening, 2005, Adobe Photoshop CS2 for Photographers A professional image editor's guide to the creative use of Photoshop for the Macintosh and PC, 10.4324/9780080481906 · doi:10.4324/9780080481906
- ba, 2014, Multiple object recognition with visual attention
- bahdanau, 2015, Neural machine translation by jointly learning to align and translate, ICLRE
- 10.1109/ICCV.2015.338 · DOI · Paper page
- 10.1109/ICCV.2015.286 · DOI · Paper page
- simonyan, 2015, Very deep convolutional networks for large-scale image recognition, ICLRE
- chen, 2015, ABC-CNN An attention based convolutional neural network for visual question answering
- pinheiro, 2013, Recurrent Convolutional Neural Networks for Scene Parsing
- sharma, 2015, Action recognition using visual attention
- sermanet, 2014, Overfeat: Integrated recognition, localization and detection using convolutional networks, ICLRE
- 10.1109/ICCV.2015.178 · DOI · Paper page
- 10.1109/CVPR.2015.7298959 · DOI · Paper page
- papandreou, 2015, Untangling local and global deformations in deep convolutional networks for image classification and sliding window detection, CVPR
- papandreou, 2015, Weakly- and semi-supervised learning of a dcnn for semantic image segmentation, ICCV
Cited by
Chen, 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
Self-citationChen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016) · arXiv · doi:10.48550/arxiv.1606.00915 · Cited by 22,392
Self-citationEncoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_49 · Cited by 20,054
Self-citationZhao, H., Shi, J., Qi, X., Wang, X., & Jia, J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.660 · Cited by 16,438
Multi-scale context aggregation by dilated convolutions
Yu, F., & Koltun, V. (2015) · arXiv · doi:10.48550/arxiv.1511.07122 · Cited by 7,738
Metadata
- Authors
- Chen, L.-C., Yang, Y., Wang, J., Xu, W., & Yuille, A. L.
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 3640-3649
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2962891704
- Cited by
- 1,377
- References
- 15
- FWCI
- 54.15
- Type
- conference paper
- Language
- en
- Open access
- green
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: Attention to Scale: Scale-Aware Semantic Image Segmentation. https://app.cochat.ai/papers/10.1109/cvpr.2016.396
- The paper
- Chen, L.-C., Yang, Y., Wang, J., Xu, W., & Yuille, A. L. (2016). Attention to Scale: Scale-Aware Semantic Image Segmentation. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2016.396
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.