The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Locality-Sensitive Deconvolution Networks with Gated Fusion for RGB-D Indoor Semantic Segmentation
Abstract
This paper focuses on indoor semantic segmentation using RGB-D data. Although the commonly used deconvolution networks (DeconvNet) have achieved impressive results on this task, we find there is still room for improvements in two aspects. One is about the boundary segmentation. DeconvNet aggregates large context to predict the label of each pixel, inherently limiting the segmentation precision of object boundaries. The other is about RGB-D fusion. Recent state-of-the-art methods generally fuse RGB and depth networks with equal-weight score fusion, regardless of the varying contributions of the two modalities on delineating different categories in different scenes. To address the two problems, we first propose a locality-sensitive DeconvNet (LS-DeconvNet) to refine the boundary segmentation over each modality. LS-DeconvNet incorporates locally visual and geometric cues from the raw RGB-D data into each DeconvNet, which is able to learn to upsample the coarse convolutional maps with large context whilst recovering sharp object boundaries. Towards RGB-D fusion, we introduce a gated fusion layer to effectively combine the two LS-DeconvNets. This layer can learn to adjust the contributions of RGB and depth over each pixel for high-performance object recognition. Experiments on the large-scale SUN RGB-D dataset and the popular NYU-Depth v2 dataset show that our approach achieves new state-of-the-art results for RGB-D indoor semantic segmentation.
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Cites
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Multi-scale context aggregation by dilated convolutions
Yu, F., & Koltun, V. (2015) · arXiv · doi:10.48550/arxiv.1511.07122 · Cited by 7,738
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Conditional Random Fields as Recurrent Neural Networks
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“Inspired by recent CRF-RNN model [29], which leverages pixel-level cues such as intensity and location via conditional random fields (CRF) to refine label agreements of the large-context FCN maps, we try to benefit DeconvNet from pixel-level cues similarly but get rid of the complex training and inference of the CRF m…”
BackgroundMethodSUN RGB-D: A RGB-D scene understanding benchmark suite
Song, S., Lichtenberg, S. P., & Xiao, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298655 · Cited by 1,943
Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation
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Cited by
Image segmentation using deep learning: A survey
Minaee, S., Boykov, Y. Y., Porikli, F., Plaza, A. J., Kehtarnavaz, N., & Terzopoulos, D. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3059968 · Cited by 3,796
SO-Net: Self-Organizing Network for Point Cloud Analysis
Li, J., Chen, B. M., & Lee, G. H. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00979 · Cited by 985
Ahn, J., & Kwak, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00523 · Cited by 872
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation With Transformers
Zhang, J., Liu, H., Yang, K., Hu, X., Liu, R., & Stiefelhagen, R. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3300537 · Cited by 731
Illumination-aware faster R-CNN for robust multispectral pedestrian detection
Li, C., Song, D., Tong, R., & Tang, M. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2018.08.005 · Cited by 533
Metadata
- Authors
- Cheng, Y., Cai, R., Li, Z., Zhao, X., & Huang, K.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 1475-1483
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2609822318
- Cited by
- 229
- References
- 11
- FWCI
- 5.34
- Type
- conference paper
- Language
- en
- Open access
- closed
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