Recurrent Scene Parsing with Perspective Understanding in the Loop
Open accessAbstract
Objects may appear at arbitrary scales in perspective images of a scene, posing a challenge for recognition systems that process images at a fixed resolution. We propose a depth-aware gating module that adaptively selects the pooling field size in a convolutional network architecture according to the object scale (inversely proportional to the depth) so that small details are preserved for distant objects while larger receptive fields are used for those nearby. The depth gating signal is provided by stereo disparity or estimated directly from monocular input. We integrate this depth-aware gating into a recurrent convolutional neural network to perform semantic segmentation. Our recurrent module iteratively refines the segmentation results, leveraging the depth and semantic predictions from the previous iterations. Through extensive experiments on four popular large-scale datasets, we demonstrate this approach achieves competitive semantic segmentation performance with a model which is substantially more compact. We carry out extensive analysis of this architecture including variants that operate on monocular RGB but use depth as side-information during training, unsupervised gating as a generic attentional mechanism, and multi-resolution gating. We find that gated pooling for joint semantic segmentation and depth yields state-of-the-art results for quantitative monocular depth estimation.
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
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
DA-Gate, using a ResNet101 backbone, achieves an mIoU of 75.3 on the Cityscapes dataset for 2.5D semantic segmentation.
“| DA-Gate [103] | ResNet101 [16] | 75.3 |”
From Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives (2022) · cites this paper as [103]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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 · see citing works
Fully 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 · see citing works
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 · see citing works
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016) · arXiv · doi:10.48550/arxiv.1606.00915 · Cited by 22,392 · see citing works
Zhao, 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 · see citing works
The Cityscapes Dataset for Semantic Urban Scene Understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.350 · Cited by 12,433 · see citing works
Indoor segmentation and support inference from rgbd images
Silberman, N., Hoiem, D., Kohli, P., & Fergus, R. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33715-4_54 · Cited by 5,760 · see citing works
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
Eigen, D., Puhrsch, C., & Fergus, R. (2014) · arXiv · doi:10.48550/arxiv.1406.2283 · Cited by 3,442 · see citing works
Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
Krähenbühl, P., & Koltun, V. (2012) · arXiv · doi:10.48550/arxiv.1210.5644 · Cited by 2,908 · see citing works
Eigen, D., & Fergus, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.304 · Cited by 2,862 · see citing works
Feature detection with automatic scale selection
Lindeberg, T. (1998) · International Journal of Computer Vision · doi:10.1023/a:1008045108935 · Cited by 2,541 · see citing works
MatConvNet: Convolutional Neural Networks for MATLAB
Vedaldi, A., & Lenc, K. (2015) · Proceedings of the 23rd ACM international conference on Multimedia · doi:10.1145/2733373.2807412 · Cited by 2,444 · see citing works
Conditional Random Fields as Recurrent Neural Networks
Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., & Torr, P. H. S. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.179 · Cited by 2,386 · cited 1× · see citing works
“, [20, 39]).”
Deeper Depth Prediction with Fully Convolutional Residual Networks
Laina, I., Rupprecht, C., Belagiannis, V., Tombari, F., & Navab, N. (2016) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2016.32 · Cited by 2,061 · see citing works
SUN 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 · see citing works
Attention to Scale: Scale-Aware Semantic Image Segmentation
Chen, L.-C., Yang, Y., Wang, J., Xu, W., & Yuille, A. (2016) · 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.2016.396 · Cited by 1,462 · see citing works
The Cityscapes Dataset for Semantic Urban Scene Understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016) · arXiv · doi:10.48550/arxiv.1604.01685 · Cited by 1,174 · see citing works
(2005) · Cited by 981 · see citing works
FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-Based CNN Architecture
Hazirbas, C., Ma, L., Domokos, C., & Cremers, D. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-54181-5_14 · Cited by 902 · see citing works
Deep convolutional neural fields for depth estimation from a single image
Liu, F., Shen, C., & Lin, G. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299152 · Cited by 892 · see citing works
Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation
Lin, G., Shen, C., van den Hengel, A., & Reid, I. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.348 · Cited by 862 · see citing works
Resolving human object recognition in space and time
Cichy, R. M., Pantazis, D., & Oliva, A. (2014) · Nature Neuroscience · doi:10.1038/nn.3635 · Cited by 832 · see citing works
Putting Objects in Perspective
Hoiem, D., Efros, A. A., & Hebert, M. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-008-0137-5 · Cited by 803 · see citing works
Geometric context from a single image
Hoiem, D., Efros, A. A., & Hebert, M. (2005) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2005.107 · Cited by 666 · see citing works
Joint 2D-3D-Semantic Data for Indoor Scene UnderstandingPreprint
Armeni, I., (Sasha), A. S., Zamir, A., & Savarese, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.01105 · Cited by 622 · see citing works
Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images
Gupta, S., Arbeláez, P., & Malik, J. (2013) · 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.2013.79 · Cited by 601 · see citing works
Photo Aesthetics Ranking Network with Attributes and Content Adaptation
Kong, S., Shen, X., Lin, Z., Měch, R., & Fowlkes, C. C. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46448-0_40 · Cited by 509 · see citing works
Self-citationLaplacian 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 · see citing works
Self-citationBergholm, F. (1987) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1987.4767980 · Cited by 497 · see citing works
Pulling Things out of Perspective
Ladický, Ľ., Shi, J., & Pollefeys, M. (2014) · 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.2014.19 · Cited by 463 · see citing works
Top-down and bottom-up mechanisms in biasing competition in the human brain
Beck, D. M., & Kastner, S. (2009) · Vision Research · doi:10.1016/j.visres.2008.07.012 · Cited by 444 · see citing works
Geometric reasoning for single image structure recovery
Lee, D. C., Hebert, M., & Takeo Kanade, A. (2009) · 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.2009.5206872 · Cited by 414 · see citing works
3D Traffic Scene Understanding From Movable Platforms
Geiger, A., Lauer, M., Wojek, C., Stiller, C., & Urtasun, R. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.185 · Cited by 397 · see citing works
Low-Rank Bilinear Pooling for Fine-Grained Classification
Kong, S., & Fowlkes, C. C. (2017) · 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.2017.743 · Cited by 374 · see citing works
Self-citationRecurrent Human Pose Estimation
Belagiannis, V., & Zisserman, A. (2017) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2017.64 · Cited by 325 · see citing works
Recurrent Instance Segmentation
Romera‐Paredes, B., & Torr, P. H. S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46466-4_19 · Cited by 324 · see citing works
Unsupervised Learning of Long-Term Motion Dynamics for Videos
Luo, Z., Peng, B., Huang, D.-A., Alahi, A., & Fei-Fei, L. (2017) · 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.2017.751 · Cited by 189 · see citing works
Recurrent Pixel Embedding for Instance Grouping
Kong, S., & Fowlkes, C. C. (2018) · 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.2018.00940 · Cited by 168 · see citing works
Self-citationIterative Instance Segmentation
Li, K., Hariharan, B., & Malik, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.398 · Cited by 152 · see citing works
Unsupervised Learning of Long-Term Motion Dynamics for VideosPreprint
Luo, Z., Peng, B., Huang, D.-A., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.01821 · Cited by 31 · see citing works
Toward coherent object detection and scene layout understanding
Bao, S. Y., Sun, M., & Savarese, S. (2011) · Image and Vision Computing · doi:10.1016/j.imavis.2011.08.001 · Cited by 29 · see citing works
Recurrent Human Pose Estimation
Belagiannis, V., & Zisserman, A. (2016) · arXiv · doi:10.48550/arxiv.1605.02914
Flagged first, then by citations
Research works printed but not linked: 3
- D. Hoiem, A. A. Efros, and M. Hebert. Putting objects in per- spective. International Journal of Computer Vision, 80(1):3- 15, 2008. 3
- K. Li, B. Hariharan, and J. Malik. Iterative instance segmen- tation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016. 3
- G. Lin, A. Milan, C. Shen, and I. Reid. Refinenet: Multi- path refinement networks with identity mappings for high- resolution semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recog- nition, 2017. 2, 7
Cited by
Sorted by citations
Deep High-Resolution Representation Learning for Visual Recognition
Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2983686 · Cited by 4,894 · see citing works
Object-Contextual Representations for Semantic Segmentation
Yuan, Y., Chen, X., & Wang, J. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58539-6_11 · Cited by 1,285 · see citing works
Strip Pooling: Rethinking Spatial Pooling for Scene Parsing
Hou, Q., Li, Z., Cheng, M., & Feng, J. (2020) · 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/cvpr42600.2020.00406 · Cited by 876 · see citing works
Gated-SCNN: Gated Shape CNNs for Semantic Segmentation
Takikawa, T., Acuna, D., Jampani, V., & Fidler, S. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00533 · Cited by 870 · see citing works
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 · see citing works
High-Resolution Representations for Labeling Pixels and RegionsPreprint
Sun, K., Zhao, Y. S., Jiang, B., Cheng, T., Xiao, B., Liu, D., Mu, Y., Wang, X., Liu, W., & Wang, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.04514 · Cited by 841 · see citing works
Xu, D., Ouyang, W., Wang, X., & Sebe, N. (2018) · 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.2018.00077 · Cited by 541 · see citing works
Deep Dual-Resolution Networks for Real-Time and Accurate Semantic Segmentation of Traffic Scenes
Pan, H., Hong, Y., Sun, W., & Jia, Y. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3228042 · Cited by 856 · see citing works
Segmentation Transformer: Object-Contextual Representations for Semantic SegmentationPreprint
Yuan, Y., Chen, X., Chen, X., & Wang, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.11065 · Cited by 548 · see citing works
OCNet: Object Context Network for Scene ParsingPreprint
Yuan, Y., Huang, L., Guo, J., Zhang, C., Chen, X., & Wang, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.00916 · Cited by 520 · see citing works
Chen, X., Lin, K.-Y., Wang, J., Wu, W., Qian, C., Li, H., & Zeng, G. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58621-8_33 · Cited by 488 · see citing works
Semantic Flow for Fast and Accurate Scene Parsing
Li, X., You, A., Zhu, Z., Zhao, H., Yang, M., Yang, K., Tan, S., & Tong, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58452-8_45 · Cited by 471 · see citing works
Deep High-Resolution Representation Learning for Visual RecognitionPreprint
Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y. S., Liu, D., Mu, Y., Tan, M., Wang, X., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.07919 · Cited by 350 · see citing works
Polarized self-attention: Towards high-quality pixel-wise mapping
Liu, H., Liu, F., Fan, X., & Huang, D. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.07.054 · Cited by 509 · see citing works
Boundary-Aware Feature Propagation for Scene Segmentation
Ding, H., Jiang, X., Liu, A. Q., Magnenat‐Thalmann, N., & Wang, G. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00692 · Cited by 290 · see citing works
OCNet: Object Context for Semantic Segmentation
Yuan, Y., Huang, L., Guo, J., Zhang, C., Chen, X., & Wang, J. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01465-9 · Cited by 818 · see citing works
Task-Driven Super Resolution: Object Detection in Low-Resolution Images
Haris, M., Shakhnarovich, G., & Ukita, N. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-92307-5_45 · Cited by 220 · see citing works
Spatial Pyramid Based Graph Reasoning for Semantic Segmentation
Li, X., Yang, Y., Zhao, Q., Shen, T., Lin, Z., & Liu, H. (2020) · 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/cvpr42600.2020.00897 · Cited by 201 · see citing works
Gated Fully Fusion for Semantic Segmentation
Li, X., Zhao, H., Han, L., Tong, Y., Tan, S., & Yang, K. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6805 · Cited by 228 · see citing works
Semantic Correlation Promoted Shape-Variant Context for Segmentation
Ding, H., Jiang, X., Shuai, B., Liu, A. Q., & Wang, G. (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.2019.00909 · Cited by 203 · see citing works
Recurrent Pixel Embedding for Instance Grouping
Kong, S., & Fowlkes, C. C. (2018) · 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.2018.00940 · Cited by 168 · see citing works
Self-citationPolarized Self-Attention: Towards High-quality Pixel-wise RegressionPreprint
Liu, H., Liu, F., Fan, X., & Huang, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.00782 · Cited by 172 · see citing works
InverseForm: A Loss Function for Structured Boundary-Aware Segmentation
Borse, S., Wang, Y., Zhang, Y., & Porikli, F. (2021) · 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/cvpr46437.2021.00584 · Cited by 165 · see citing works
Dual Graph Convolutional Network for Semantic SegmentationPreprint
Li, Z., Li, X., Arnab, A., Yang, K., Tong, Y., & Torr, P. H. S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.06121 · Cited by 162 · see citing works
Leveraging Heterogeneous Auxiliary Tasks to Assist Crowd Counting
Zhao, M., Zhang, J., Zhang, C., & Zhang, W. (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.2019.01302 · Cited by 144 · see citing works
Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation
Wang, Q., Dai, D., Hoyer, L., Gool, L. V., & Fink, O. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00840 · Cited by 138 · see citing works
Towards Bridging Semantic Gap to Improve Semantic Segmentation
Pang, Y., Li, Y., Shen, J., & Shao, L. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00433 · Cited by 137 · see citing works
Interlaced Sparse Self-Attention for Semantic SegmentationPreprint
Huang, L., Yuan, Y., Guo, J., Zhang, C., Chen, X., & Wang, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.12273 · Cited by 158 · see citing works
SPGNet: Semantic Prediction Guidance for Scene Parsing
Cheng, B., Chen, L.-C., Wei, Y., Zhu, Y., Huang, Z., Xiong, J., Huang, T. S., Hwu, W., Shi, H., & Uiuc, U. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00532 · Cited by 130 · see citing works
Non-Local Aggregation for RGB-D Semantic Segmentation
Zhang, G., Xue, J., Xie, P., Yang, S., & Wang, G. (2021) · IEEE Signal Processing Letters · doi:10.1109/lsp.2021.3066071 · Cited by 101 · see citing works
TrSeg: Transformer for semantic segmentation
Jin, Y., Han, D. K., & Ko, H. (2021) · Pattern Recognition Letters · doi:10.1016/j.patrec.2021.04.024 · Cited by 101 · see citing works
Improving Semantic Segmentation via Efficient Self-Training
Zhu, Y., Zhang, Z., Wu, C., Zhang, Z., He, T., Zhang, H., Manmatha, R., Li, M., & Smola, A. J. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3138337 · Cited by 133 · see citing works
Context-Reinforced Semantic Segmentation
Zhou, Y., Sun, X., Zha, Z.-J., & Zeng, W. (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.2019.00417 · Cited by 67 · see citing works
Variational Context-Deformable ConvNets for Indoor Scene Parsing
Xiong, Z., Yuan, Y., Guo, N., & Wang, Q. (2020) · 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/cvpr42600.2020.00405 · Cited by 67 · see citing works
Rizzoli, G., Barbato, F., & Zanuttigh, P. (2022) · Technologies · doi:10.3390/technologies10040090 · Cited by 58 · see citing works
UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation
Kundu, J. N., Lakkakula, N., & Radhakrishnan, V. B. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00152 · Cited by 57 · see citing works
Pixel-Wise Attentional Gating for Scene Parsing
Kong, S., & Fowlkes, C. C. (2019) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2019.00114 · Cited by 54 · see citing works
Self-citationSelf-attention neural architecture search for semantic image segmentation
Fan, Z., Hu, G., Sun, X., Wang, G., Dong, J., & Su, C. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107968 · Cited by 53 · see citing works
KE-GAN: Knowledge Embedded Generative Adversarial Networks for Semi-Supervised Scene Parsing
Qi, M., Wang, Y., Qin, J., & Li, A. (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.2019.00538 · Cited by 50 · see citing works
Cross-modal attention fusion network for RGB-D semantic segmentation
Zhao, Q., Wan, Y., Xu, J., & Fang, L. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126389 · Cited by 49 · see citing works
Malleable 2.5D Convolution: Learning Receptive Fields Along the Depth-Axis for RGB-D Scene Parsing
Xing, Y., Wang, J., & Zeng, G. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58529-7_33 · Cited by 45 · see citing works
Two-Stage Cascaded Decoder for Semantic Segmentation of RGB-D Images
Yue, Y., Zhou, W., Lei, J., & Yu, L. (2021) · IEEE Signal Processing Letters · doi:10.1109/lsp.2021.3084855 · Cited by 43 · see citing works
Neogi, S., Hoy, M., Dang, K., Yu, H., & Dauwels, J. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2995166 · Cited by 40 · see citing works
2.5D Convolution for RGB-D Semantic Segmentation
Xing, Y., Wang, J., Chen, X., & Zeng, G. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803757 · Cited by 35 · see citing works
GFF: Gated Fully Fusion for Semantic SegmentationPreprint
Li, X., Zhao, H., Han, L., Tong, Y., & Yang, K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.01803 · Cited by 34 · see citing works
Cross Attention Network for Semantic Segmentation
Liu, M., & Yin, H. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803320 · Cited by 34 · see citing works
Yan, M., Wang, J., Li, J., Zhang, K., & Yang, Z. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.12.007 · Cited by 31 · see citing works
Improving Semantic Segmentation via Self-TrainingPreprint
Zhu, Y., Zhang, Z., Wu, C., Zhang, Z., He, T., Zhang, H., Manmatha, R., Li, M., & Smola, A. J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.14960 · Cited by 33 · see citing works
Coupling Two-Stream RGB-D Semantic Segmentation Network by Idempotent Mappings
Xing, Y., Wang, J. B., Chen, X., & Zeng, G. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803146 · Cited by 31 · see citing works
Pixel-wise Attentional Gating for Parsimonious Pixel LabelingPreprint
Kong, S., & Fowlkes, C. C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.01556 · Cited by 30 · see citing works
Self-citationAugmented FCN: rethinking context modeling for semantic segmentation
Zhang, D., Zhang, L., & Tang, J. (2023) · Science China Information Sciences · doi:10.1007/s11432-021-3590-1 · Cited by 28 · see citing works
Camera Pose Matters: Improving Depth Prediction by Mitigating Pose Distribution Bias
Zhao, Y., Kong, S., & Fowlkes, C. C. (2021) · 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/cvpr46437.2021.01550 · Cited by 27 · see citing works
Self-citationModularized Textual Grounding for Counterfactual Resilience
Fang, Z., Kong, S., Fowlkes, C. C., & Yang, Y. (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.2019.00654 · Cited by 25 · see citing works
Self-citationA Deep Extractor for Visual Rail Surface Inspection
Zhang, Z., Liang, M., & Wang, Z. (2021) · IEEE Access · doi:10.1109/access.2021.3055512 · Cited by 25 · see citing works
Bruno, D. R., Berri, R. A., Barbosa, F. M., & Osório, F. S. (2023) · IEEE Access · doi:10.1109/access.2023.3287491 · Cited by 25 · see citing works
Deep Feature Selection-And-Fusion for RGB-D Semantic Segmentation
Su, Y., Yuan, Y., & Jiang, Z. (2021) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme51207.2021.9428155 · Cited by 20 · see citing works
Zhou, F., Hu, Y., & Shen, X. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.11.042 · Cited by 20 · see citing works
Celeganser: Automated Analysis of Nematode Morphology and Age
Wang, L., Kong, S., Pincus, Z., & Fowlkes, C. C. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00492 · Cited by 20 · see citing works
Self-citationJiang, M., Zhai, F., & Kong, J. (2021) · The Visual Computer · doi:10.1007/s00371-021-02124-3 · Cited by 18 · see citing works
Chen, X., Han, Z., Liu, X., Li, Z., Fang, T., Huo, H., Li, Q., Zhu, M., Liu, M., & Yuan, H. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107511 · Cited by 16 · see citing works
Akita, K., & Ukita, N. (2023) · IEEE Access · doi:10.1109/access.2023.3329302 · Cited by 16 · see citing works
Memory-based Semantic Segmentation for Off-road Unstructured Natural Environments
Jin, Y., Han, D. K., & Ko, H. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636620 · Cited by 16 · see citing works
Learning Background Subtraction by Video Synthesis and Multi-scale Recurrent Networks
Choo, S., Seo, W., Jeong, D.-J., & Cho, N. I. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20876-9_23 · Cited by 15 · see citing works
Scene Parsing via Integrated Classification Model and Variance-Based Regularization
Shi, H., Li, H., Wu, Q., & Song, Z. (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.2019.00545 · Cited by 14 · see citing works
Image Reconstruction with Predictive Filter FlowPreprint
Kong, S., & Fowlkes, C. C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.11482 · Cited by 14 · see citing works
Self-citationMalgheet, J. R., Manshor, N. B., Affendey, L. S., & Halin, A. B. A. (2023) · IEEE Access · doi:10.1109/access.2023.3282547 · Cited by 16 · see citing works
TCANet: three-stream coordinate attention network for RGB-D indoor semantic segmentation
Jia, W., Yan, X., Liu, Q., Zhang, T., & Dong, X. (2023) · Complex & Intelligent Systems · doi:10.1007/s40747-023-01210-4 · Cited by 13 · see citing works
Enhanced Feature Pyramid Network for Semantic Segmentation
Ye, M., Ouyang, J., Chen, G., Zhang, J., & Yu, X. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9413224 · Cited by 13 · see citing works
Learning deep cross-scale feature propagation for indoor semantic segmentation
Huan, L., Zheng, X., Tang, S., & Gong, J. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.03.023 · Cited by 11 · see citing works
Clinical target volume segmentation for stomach cancer by stochastic width deep neural network
Xu, L., Hu, J., Song, Y., Bai, S., & Zhang, Y. (2021) · Medical Physics · doi:10.1002/mp.14733 · Cited by 10 · see citing works
Interweave features of Deep Convolutional Neural Networks for semantic segmentation
Bai, S., Gu, W., & Kong, L. (2021) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2021.104587 · Cited by 9 · see citing works
Learning to Reconstruct and Understand Indoor Scenes From Sparse Views
Yang, J., Xu, J., Li, K., Lai, Y., Yue, H., Lu, J., Wu, H., & Liu, Y. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2986712 · Cited by 8 · see citing works
ChoiceNet: CNN learning through choice of multiple feature map representations
Rayhan, F., Galata, A., & Cootes, T. F. (2021) · Pattern Analysis and Applications · doi:10.1007/s10044-021-01004-9 · Cited by 7 · see citing works
CASINet: Content-Adaptive Scale Interaction Networks for scene parsing
Jin, X., Lan, C., Zeng, W., Zhang, Z., & Chen, Z. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.08.014 · Cited by 12 · see citing works
Alcover‐Couso, R., SanMiguel, J. C., Escudero-Viñolo, M., & García‐Martín, Á. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14662-0 · Cited by 6 · see citing works
Semantic Segmentation for Aerial Images: A Literature Review
Sanjaya, Y. C., Gunawan, A. A. S., & Irwansyah, E. (2020) · Engineering MAthematics and Computer Science (EMACS) Journal · doi:10.21512/emacsjournal.v2i3.6737 · Cited by 6 · see citing works
Region-dependent Scale Proposals for Super-Resolution in Object Detection
Akita, K., Haris, M., & Ukita, N. (2020) · IEEE International Conference on Image Processing, Applications and Systems (IPAS) · doi:10.1109/ipas50080.2020.9334961 · Cited by 6 · see citing works
A Cross-Modal Feature Fusion Model Based on ConvNeXt for RGB-D Semantic Segmentation
Tang, X., Li, B., Guo, J., Chen, W., Zhang, D., & Huang, F. (2023) · Mathematics · doi:10.3390/math11081828 · Cited by 6 · see citing works
Domain Adaptive Semantic Segmentation with Self-Supervised Depth EstimationPreprint
Wang, Q., Dai, D., Hoyer, L., Gool, L. V., & Fink, O. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.13613 · Cited by 6 · see citing works
Self-Enhanced Feature Fusion for RGB-D Semantic Segmentation
Xiang, P., Yao, B., Jiang, Z., & Peng, C. (2024) · IEEE Signal Processing Letters · doi:10.1109/lsp.2024.3475352 · Cited by 5 · see citing works
Channel2DTransformer: A Multi-level Features Self-attention Fusion Module for Semantic Segmentation
Liu, W., & Wu, J. (2024) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-024-00630-5 · Cited by 5 · see citing works
Distributed Iterative Gating Networks for Semantic Segmentation
Karim, R., Islam, A., & Bruce, N. D. B. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093441 · Cited by 5 · see citing works
Empirical Study of Multi-Task Hourglass Model for Semantic Segmentation Task
Pilco, D. D. S., & Rivera, A. R. (2021) · IEEE Access · doi:10.1109/access.2021.3085218 · Cited by 5 · see citing works
An anisotropic non-local attention network for image segmentation
Yuan, F., Zhu, Y., Li, K., Fang, Z., & Shi, J. (2022) · Machine Vision and Applications · doi:10.1007/s00138-021-01265-8 · Cited by 4 · see citing works
Intra-inter Modal Attention Blocks for RGB-D Semantic Segmentation
Choi, S., Zhang, Y., & Hong, S. (2023) · International Conference on Multimedia Retrieval · doi:10.1145/3591106.3592235 · Cited by 4 · see citing works
Foreground Detection and Segmentation in RGB-D Images
Cong, R., Chen, H., Zhu, H., & Fu, H. (2019) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-030-28603-3_10 · Cited by 4 · see citing works
Encoder–decoder with double spatial pyramid for semantic segmentation
Kong, H., Hu, J., Fan, L., Zhang, X., & Fang, Y. (2019) · Journal of Electronic Imaging · doi:10.1117/1.jei.28.6.063007 · Cited by 4 · see citing works
ColorRL: Reinforced Coloring for End-to-End Instance Segmentation
Tuấn, T. A., Khoa, N. T., Quan, T. M., & Jeong, W. (2021) · 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/cvpr46437.2021.01645 · Cited by 4 · see citing works
CaseNet: Content-Adaptive Scale Interaction Networks for Scene ParsingPreprint
Jin, X., Lan, C., Zeng, W., Zhang, Z., & Chen, Z. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.08170 · Cited by 4 · see citing works
TriangleNet: Edge Prior Augmented Network for Semantic Segmentation through Cross‐Task Consistency
Zhang, D., Zheng, R., Luosang, G., & Yang, P. H. (2023) · International Journal of Intelligent Systems · doi:10.1155/2023/1616055 · Cited by 3 · see citing works
Pyramid Context Contrast for Semantic Segmentation
Chen, Y., Lin, Y., Niu, Y., Ke, X., & Huang, T. (2019) · IEEE Access · doi:10.1109/access.2019.2956216 · Cited by 3 · see citing works
GSTO: Gated Scale-Transfer Operation for Multi-Scale Feature Learning in Semantic Segmentation
Wang, Z., Wang, Y., Tang, Z., Li, Y., Chen, Y., Ling, H., & Lin, W. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412965 · Cited by 3 · see citing works
Sketch-and-Fill Network for Semantic Segmentation
Jin, Y., Eum, S., Han, D. K., & Ko, H. (2021) · IEEE Access · doi:10.1109/access.2021.3088854 · Cited by 3 · see citing works
Gao, L., Li, X., Liu, Y., Chen, X., Liu, Y., Xu, J., & Zhang, M. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3597372 · Cited by 2 · see citing works
Super LiDAR Intensity for Robotic Perception
Gao, W., Zhang, J., Zhao, M., Zhang, Z., Kong, S., Ghaffari, M., Song, D., Xu, C., & Kong, H. (2026) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2026.3664533 · Cited by 2 · see citing works
Self-citationInception Donut Convolution for Top-down Semantic Segmentation
Guan, H., Zhang, Z., & Tan, T. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8545386 · Cited by 2 · see citing works
RGB-D Semantic Segmentation for Indoor Modeling Using Deep Learning: A Review
Rached, I., Hajji, R., & Landes, T. (2024) · Lecture notes in geoinformation and cartography · doi:10.1007/978-3-031-43699-4_36 · Cited by 2 · see citing works
Semi-Supervised Perception Augmentation for Aerial Photo Topologies Understanding
Zhang, L., Pan, Z., & Shao, L. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3079820 · Cited by 2 · see citing works
FineSegNeRF: Semantic NeRF for fine semantic segmentation
Liu, W., Wang, S. W., Wu, J., & Lu, Q. (2026) · Biomimetic Intelligence and Robotics · doi:10.1016/j.birob.2026.100302 · Cited by 1 · see citing works
EBANet: Efficient Boundary-Aware Network for RGB-D Semantic Segmentation
Wang, R., Jia, Q., Shen, Y., Huang, Z., Chen, G., & Fei, J. (2022) · Communications in computer and information science · doi:10.1007/978-981-16-9247-5_16 · Cited by 1 · see citing works
- Authors
- Kong, S., & Fowlkes, C.
- Affiliations
- University of California, Irvine (US)
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 956-965
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2617969033
- Cited by
- 114
- References
- 40
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 4 · 2023: 14 · 2022: 5 · 2021: 29 · 2020: 21 · 2019: 29 · 2018: 6
- FWCI
- 6.40
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1253538, 1262547, 1618806)
- Nvidia
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- adaptive pooling, recurrent convolutional neural network, semantic segmentation, monocular depth estimation, stereo disparity, perspective images
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
