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Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation
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“For fair comparisons with original RPNs [29], we run two baselines (Table 1(a, b)) using the single-scale map of C 4 (the same as [16]) or C 5 , both using the same hyper-parameters as ours, including using 5 scale anchors of { 32 2 , 64 2 , 128 2 , 256 2 , 512 2 } .”
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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
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
Lecture Notes in Computer Science 1205
(1999) · Industrial Robot the international journal of robotics research and application · doi:10.1108/ir.1999.04926fae.001 · Cited by 39,228
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
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
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016) · arXiv · doi:10.48550/arxiv.1606.00915 · Cited by 22,392
Visualizing and understanding convolutional networks
Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839
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
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
Research works printed but not linked: 2
- Dai, J., He, K., Sun, J.: Convolutional feature masking for joint object and stuff segmentation (2014). arXiv preprint arXiv:1412.1283 · arXiv
- Uhrig, J., Cordts, M., Franke, U., Brox, T.: Pixel-level encoding and depth layering for instance-level semantic labeling (2016). arXiv preprint arXiv:1604.05096 · arXiv
Cited by
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Items 401–500 of 500
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The Impact of Parameters on Semantic Segmentation: A Case Study on the CamVid Dataset
Zhang, Y., Yang, R., Wang, J., Chen, N., & Dai, Q. (2021) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-dss-smartcity-dependsys53884.2021.00289 · Cited by 3
Leveraging Pyramidal Feature Hierarchy for 3D Reconstruction
Mahad, F. S., Iwamura, M., & Kise, K. (2020) · Communications in computer and information science · doi:10.1007/978-981-15-4818-5_26 · Cited by 3
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Hu, H., Fang, B., Duo, B., Wei, X., Yan, J., Xian, W., & Li, D. (2025) · Symmetry · doi:10.3390/sym17111807 · Cited by 2
Dynamic Residual Filtering With Laplacian Pyramid for Instance Segmentation
Song, M., Um, G., Lee, H. K., Seo, J., & Kim, W. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3215306 · Cited by 2
Lin, Z., Yang, Z., & Wang, Y. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00403 · Cited by 2
Chen, L., Chen, H., Xie, Y., He, T., Ye, J. B., & Zheng, Y. (2023) · Forests · doi:10.3390/f14112271 · Cited by 2
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DPNet: A Dual Path Network for Road Scene Semantic Segmentation
Lu, Y., Zhu, J., Zhou, W., Duan, T., Sugianto, S., Agordzo, G. K., Yeboah, D., & Kevin, M. T. (2020) · Lecture notes in computer science · doi:10.1007/978-3-662-61510-2_6 · Cited by 2
A Real-Time Application-Oriented Cyber-Physical Systems for Surface Defects Detection
Tong, X., Zhu, Z., Bai, D., Tao, B., Yun, J., Liu, X., & Zhao, G. (2023) · IEEE Systems Journal · doi:10.1109/jsyst.2023.3293119 · Cited by 2
DBRS2: dense boundary regression for semantic segmentation
Yang, J., Zhang, J., Li, M., & Wang, M. (2018) · Journal of Electronic Imaging · doi:10.1117/1.jei.27.5.053033 · Cited by 2
Inception 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
Dense In Dense: Training Segmentation from Scratch
Hu, T. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20876-9_29 · Cited by 2
Automatic Hair Segmentation in Complex Background
Zhu, H., Nanjing, Y. L., & Liu, Y. K. (2019) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio49542.2019.8961465 · Cited by 2
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PPNet : pooling position attention network for semantic segmentation
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Position attention optimized deep semantic segmentation
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Brain Tumor Segmentation Based on The Learning Statistical Texture
Guo, Y., Chang, F., Chen, X., Sun, F., & Wang, Z. (2023) · Journal of Artificial Intelligence and Technology · doi:10.37965/jait.2023.0442 · Cited by 2
Semantic segmentation with multi-path refinement and pyramid pooling dilated-resnet
Cui, Z., Zhang, Q., Geng, S., Niu, X., Yang, J., & Qiao, Y. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8296853 · Cited by 2
Performance Assessment of Convolutional Neural Networks for Semantic Image Segmentation
Leipnitz, A., Strutz, T., & Jokisch, O. (2019) · Computer Science Research Notes · doi:10.24132/csrn.2019.2901.1.4 · Cited by 2
DNNs for multi-map semantic segmentation
Kurnikov, P. A., & Sholomov, D. L. (2021) · International Conference on Machine Vision · doi:10.1117/12.2587178 · Cited by 2
High-Order Paired-ASPP Networks for Semantic SegmenationPreprint
Zhang, Y., Sun, X., Dong, J., Chen, C., & Shen, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.07371 · Cited by 2
Automatic Image Labelling at Pixel LevelPreprint
Zhang, X., Zhang, W., Peng, J., & Jianping Fan, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.07415 · Cited by 2
Recurrent attention convolutional neural network optimise track foreign body detection
Linfeng, W., Heng, W., Tang, X., Dan, X., & Liu, J. (2022) · IET Communications · doi:10.1049/cmu2.12426 · Cited by 1
License Plate Detection with Attention-Guided Dual Feature Pyramid Networks in Complex Environments
Xiong, Y.-J., Gao, Y., Zhang, J.-Q., & Ren, J.-X. (2022) · Electronics · doi:10.3390/electronics11233895 · Cited by 1
GCAENet: global-class context with advanced edge network for single human parsing
Zhang, X., Liu, W., Xing, W., & Wei, X. (2022) · The Visual Computer · doi:10.1007/s00371-022-02735-4 · Cited by 1
Fuzhi, W., & Song, C. (2024) · IEEE Access · doi:10.1109/access.2024.3430931 · Cited by 1
Real-Time Long-Wave Infrared Semantic Segmentation With Adaptive Noise Reduction and Feature Fusion
Bae, H., Kang, D., Chang, M., Jeong, K. Y., & Song, B. C. (2025) · IEEE Access · doi:10.1109/access.2025.3552782 · Cited by 1
Ji, X., Feng, Y., Lu, H., Chu, D., & Han, Q. (2026) · Nutrients · doi:10.3390/nu18132119 · Cited by 1
A novel framework for semantic segmentation with generative adversarial network
Zhang, X., Zhu, X., Zhang, N., Li, H., Cai, Q., & Wang, L. (2018) · International Conference on Internet Multimedia Computing and Service · doi:10.1145/3240876.3240891 · Cited by 1
A Deep Convolutional Networks for Monocular Road Segmentation
Yang, G., Yu, Z., & Wang, Y. (2018) · Chinese Automation Congress · doi:10.1109/cac.2018.8623642 · Cited by 1
Liu, C., Liu, J., Zhang, Y., & Huang, L. (2021) · IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · doi:10.1109/dasc-picom-cbdcom-cyberscitech52372.2021.00021 · Cited by 1
A Survey of Image Semantic Segmentation Algorithm Based on Deep Learning
Chen, J., & Luo, F. (2023) · Academic Journal of Science and Technology · doi:10.54097/ajst.v5i1.5248 · Cited by 1
TCNet: tensor and covariance attention network for semantic segmentation
Xu, H., Liu, Y., Wang, W., Zhou, W., Ding, F., Han, F. S., & Peng, W. (2024) · Soft Computing · doi:10.1007/s00500-024-09638-7 · Cited by 1
Enhanced single shot detector for small object detection in drone-capture scenarios
Shi, Y., Liu, Y., & Liu, Y. (2025) · Soft Computing · doi:10.1007/s00500-025-10539-6 · Cited by 1
Structural inference embedded adversarial networks for scene parsing
Wang, Z., Wu, Y., Bu, S., Han, P., & Zhang, G. (2018) · PLoS ONE · doi:10.1371/journal.pone.0195114 · Cited by 1
Attention and Multi-layer Fusion for Real-time Semantic Segmentation
Cheng, Q., Zhang, C., Li, Z., & Wang, Z. (2019) · International Symposium on Computational Intelligence and Design · doi:10.1109/iscid.2019.00039 · Cited by 1
Xiong, Z., Wang, Z., Li, J., Yu, Z., & Gu, X. (2020) · IEEE Access · doi:10.1109/access.2020.3041748 · Cited by 1
Convolutional Neural Networks for Approximation of Internal Non-Newtonian Multiphase Flow Fields
Birkenmaier, C., & Krenkel, L. (2021) · WCCM-ECCOMAS Congress · doi:10.23967/wccm-eccomas.2020.107 · Cited by 1
Convolutional Neural Networks for Approximation of Blood Flow in Artificial Lungs
Birkenmaier, C., & Krenkel, L. (2021) · Notes on numerical fluid mechanics and multidisciplinary design · doi:10.1007/978-3-030-79561-0_43 · Cited by 1
An End-to-End Mutual Enhancement Network Toward Image Compression and Semantic Segmentation
Chen, J., Yao, C., Liu, M., & Zhao, Y. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-88007-1_51 · Cited by 1
Li, X., Huang, L., Yu, S. Y., Wu, C., Li, W., & Ji, X. (2022) · Journal of Electronic Imaging · doi:10.1117/1.jei.31.5.053005 · Cited by 1
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Dias, P. A., & Medeiros, H. P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.05856 · Cited by 1
Hierarchical joint-guided networks for semantic image segmentation
Wang, C.-Y., Li, J.-H., Mathulaprangsan, S., Chiang, C.-C., & Wang, J. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7952484
Chen, D., Wu, J., & Gao, S. (2018) · International Conference on Vision, Image and Signal Processing (ICVISP) · doi:10.1145/3271553.3271612
Discriminative Features Reconstruction Network for Semantic Segmentation
Zhou, Q., Ma, Y., Lu, H., Chen, X., & Zhao, Y. H. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8683459
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Mazzini, D., & Schettini, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.09033
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Chen, W., Chen, P., & Lin, B.-C. (2019) · Communications in computer and information science · doi:10.1007/978-981-13-9190-3_9
Pyramidal region context module for semantic segmentation
Liang, T., Zhao, Q., Wang, Z., Shan, K., Zhang, H., Wang, Y., & Tang, Z. (2019) · ACM Turing Celebration Conference - China (ACM TURC) · doi:10.1145/3321408.3322635
Classification Assisted Segmentation Network for Human Parsing
Liu, Z., Li, C., Liu, Y., Lian, Z., & Wu, Y. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803089
Convex and Non-convex Optimization Methods for Machine Learning
Zohrizadeh, F. (2019)
Sequential image processing methods for improving semantic video segmentation algorithmsPreprint
Sırmaçek, B., Botteghi, N., & Sanchez-Escalonilla, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.13348
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Xiong, Z., Wang, Z., Yu, Z., & Gu, X. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.08149
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Han, Q., & Zheng, J. G. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-37731-1_48
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Azimi, S. M., Bahmanyar, R., Henry, C., & Kurz, F. (2020) · arXiv (Cornell University)
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He, Y., Schiele, B., & Fritz, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.08849
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Xu, K., An, Z., Zhu, H., Hu, X., & Xu, Y. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207012
Adversarial Multi-scale Feature Learning for Person Re-identificationPreprint
Wang, X. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.14061
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Azimi, S. M., Kiefl, R., Gstaiger, V., Bahmanyar, R., Merkle, N., Henry, C., Rosenbaum, D., & Kurz, F. (2021) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xliii-b2-2021-433-2021 · Cited by 1
Salient feature network for semantic segmentation
Zhou, Y., Zhou, Z., Zhou, H., Mu, J., & Wang, D. (2021) · Signal Image and Video Processing · doi:10.1007/s11760-021-02016-y
Simpler Does It: Generating Semantic Labels with Objectness Guidance
Islam, M. A., Kowal, M. D., Jia, S., Derpanis, K. G., & Bruce, N. D. B. (2021) · British Machine Vision Conference (BMVC) · doi:10.5244/c.35.220
Scene segmentation with deep neural networks
Shuai, B. (2018) · doi:10.32657/10356/73331
Scene parsing with deep neural networks
Ding, H. (2020) · doi:10.32657/10356/142935
Nguyen, V.-D., Nham, D.-H.-N., Pham, V.-T., & Tran, T.-T. (2022) · International Conference on Communications and Electronics (ICCE) · doi:10.1109/icce55644.2022.9852083
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Jiang, Z., Yan, K., Lü, L., & Xu, M. (2023) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi53787.2023.10230353
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Han, X., Li, T., & Yang, H. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4564024
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Lv, X., Wang, C., & Zhang, Y. (2023) · Research Square · doi:10.21203/rs.3.rs-3388990/v1
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Heng, Y., Wu, Y., Dasmahapatra, S., & Kim, H. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-45725-8_4
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Huang, Q. Y., Sun, S., & Zhang, B. (2023) · doi:10.1117/12.3013258
Kim, J.-H., Kwon, H.-M., Lim, S. Y., & Kim, W. (2024) · Multimedia Systems · doi:10.1007/s00530-023-01216-5
Dong, Y., Li, N., & Wang, C. (2024) · International Conference on High Performance Computing and Communication Engineering (HPCCE) · doi:10.1117/12.3026331
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Aljaddouh, B., Malathi, D., & Alaswad, F. (2023) · International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) · doi:10.1109/icccis60361.2023.10425725
A Review of Traffic Scene Reconstruction Based on Images and Point Clouds
Dong, X. (2024) · International Journal of Computer Science and Information Technology · doi:10.62051/ijcsit.v3n1.13
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Priya, S. S., & Kumar, V. D. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-6489-1_24
Zhao, X., Liu, B. Y., Zheng, L., Bao, Y., Tong, G., & Xue, Z. (2024) · Research Square · doi:10.21203/rs.3.rs-5327126/v1
Texture-attention discriminator for real-world image super-resolution
Hao, Y. (2024) · International Conference on Computer Vision and Information Technology (CVIT) · doi:10.1117/12.3055583
Multiclass certainty mapped network for high-precision segmentation of high-altitude imagery
Oğuz, T., & Akgün, T. (2025) · doi:10.1117/12.3041825
Wang, Y., Xie, H., Huang, Q., Wang, Y., Yan, X., Tong, X., Liu, S., Ye, Z., Liu, S., Xu, X., et al. (2025) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2025.3550461
Multilevel Dual Attention Residual Network for Scene Segmentation
Yu, X. (2025) · International Symposium on Computer Applications and Information Systems (ISCAIS) · doi:10.1109/iscait64916.2025.11010558
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Hu, S., Wang, W., Huang, T., & Feng, W. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-6969-1_18
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Luo, Q., & Cao, X. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-6957-8_4
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Zhang, X., & Liu, W. (2025) · doi:10.1117/12.3092829
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Luo, Y., Liu, J., & Zeng, S. (2026) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-032-14681-6_3
Huang, J., & Liu, J. (2026) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001426500096
Computer Vision for Healthcare and Medicine: Unlocking Insights from Visual Data
Ray, A., Firouzi, F., Farahani, B., & Chakrabarty, K. (2026) · Springer eBooks · doi:10.1007/978-3-032-06286-4_5
Fourure, D. (2017) · theses.fr (ABES) · doi:10.70675/b54dd90bz6e92z43fez8df3z313ad61607ea
Video inpainting and semi-supervised object removal
Le, T. T. (2019) · theses.fr (ABES) · doi:10.70675/1cf4c899z91b9z479czafcdz6dce7a3b070a
Deep learning for visual semantic segmentation
Chen, Y. (2020) · theses.fr (ABES) · doi:10.70675/1b540d0dz3268z4288z98b9ze62b80e56da9
Segmentation of road scene omnidirectional images
Sekkat, A. R. (2022) · theses.fr (ABES) · doi:10.70675/6c5add14zaebez4287z800dzab48102c8f8b
LapDINO: A DINOv3 and Laplacian Pyramid-Based Approach for Outdoor Terrain Segmentation
Ling, S., Qin, X., Wenkang, X., Fu, M., Huang, H., Ma, S., & Liu, Z. (2026) · Sensors · doi:10.3390/s26123843
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Aljaddouh, B., Malathi, D., & Alaswad, F. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.06.167
LapCR-Net: A Lightweight Monocular Depth Estimation Network via Laplacian Residual Reconstruction
Li, L., Zhao, Y., Qin, K., Lu, D., Yang, H., & Wang, X. (2026) · Sensors · doi:10.3390/s26154912
Metadata
- Authors
- Ghiasi, G., & Fowlkes, C. C.
- Affiliations
- University of California, Irvine (US)
- Corresponding author
- Ghiasi, Golnaz
- ORCID
- Fowlkes, Charless C.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 519-534
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2508741746
- Cited by
- 502
- References
- 41
- Citations by year
- 2026: 8 · 2025: 20 · 2024: 29 · 2023: 37 · 2022: 47 · 2021: 67 · 2020: 76 · 2019: 75 · 2018: 79 · 2017: 54 · 2016: 10
- FWCI
- 25.20
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1253538, 1262547)
- Nvidia
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- semantic segmentation, Laplacian pyramid, multi-resolution reconstruction, sub-pixel localization, skip connections, boundary refinement, spatial pooling, feature maps
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 ›
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