The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Multi-scale Recognition with DAG-CNNs
Open accessAbstract
We explore multi-scale convolutional neural nets (CNNs) for image classification. Contemporary approaches extract features from a single output layer. By extracting features from multiple layers, one can simultaneously reason about high, mid, and low-level features during classification. The resulting multi-scale architecture can itself be seen as a feed-forward model that is structured as a directed acyclic graph (DAG-CNNs). We use DAG-CNNs to learn a set of multi-scale features that can be effectively shared between coarse and fine-grained classification tasks. While fine-tuning such models helps performance, we show that even "off-the-self" multi-scale features perform quite well. We present extensive analysis and demonstrate state-of-the-art classification performance on three standard scene benchmarks (SUN397, MIT67, and Scene15). In terms of the heavily benchmarked MIT67 and Scene15 datasets, our results reduce the lowest previously-reported error by 23.9% and 9.5%, respectively.
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Cites
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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
CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification
Chen, C.-F. R., Fan, Q., & Panda, R. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00041 · Cited by 2,074
Ranjan, R., Patel, V. M., & Chellappa, R. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2781233 · Cited by 1,333
Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation
Ghiasi, G., & Fowlkes, C. C. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46487-9_32 · Cited by 502
Growing a Brain: Fine-Tuning by Increasing Model Capacity
Wang, Y.-X., Ramanan, D., & Hebert, M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.323 · Cited by 154
“For a fair comparison, we focus on the approaches that use single scale AlexNet CNNs. Importantly, our approach can be also combined with other CNN variations ( e.g ., VGG-CNN [37], multi-scale CNN [12, 53]) for further improvement.”
MethodSelf-citation
Metadata
- Authors
- Yang, S., & Ramanan, D.
- Venue
- 2015 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 1215-1223
- OpenAlex
- W300523764
- Cited by
- 187
- References
- 14
- FWCI
- 8.73
- Type
- conference paper
- Language
- en
- Open access
- green
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