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Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation
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“Similar to [29], we find that sharing features improves accuracy by a small margin.”
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“For these reasons, Fast and Faster R-CNN [11, 29] opt to not use featurized image pyramids under default settings.”
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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
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Items 101–200 of 500
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Soylu, B. E., Güzel, M. S., Bostancı, E., Ekinci, F., Aşuroğlu, T., & Açıcı, K. (2023) · Electronics · doi:10.3390/electronics12122730 · Cited by 127
Attention Deeplabv3+: Multi-level Context Attention Mechanism for Skin Lesion Segmentation
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Multitask Semantic Boundary Awareness Network for Remote Sensing Image Segmentation
Li, A., Jiao, L., Zhu, H., Li, L., & Liu, F. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3050885 · Cited by 108
Building Extraction in Very High Resolution Imagery by Dense-Attention Networks
Yang, H., Wu, P., Yao, X., Wu, Y., Wang, B., & Xu, Y. (2018) · Remote Sensing · doi:10.3390/rs10111768 · Cited by 105
Welling, M., Sebe, N., Matas, J., & Leibe, B. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46487-9 · Cited by 99
Video Deblurring via Semantic Segmentation and Pixel-Wise Non-linear Kernel
Ren, W., Pan, J., Cao, X., & Yang, M. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.123 · Cited by 99
Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation
Hoyer, L., Dai, D., Chen, Y., Köring, A., Saha, S., & Gool, L. V. (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/cvpr46437.2021.01098 · Cited by 101
Survey of recent progress in semantic image segmentation with CNNs
Geng, Q., Zhou, Z., & Cao, X. (2017) · Science China Information Sciences · doi:10.1007/s11432-017-9189-6 · Cited by 96
Deep Dual Learning for Semantic Image Segmentation
Luo, P., Wang, G., Lin, L., & Wang, X. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.296 · Cited by 96
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Liu, S., Ding, W., Liu, C., Liu, Y., Wang, Y., & Li, H. (2018) · Remote Sensing · doi:10.3390/rs10091339 · Cited by 94
Deep gated attention networks for large-scale street-level scene segmentation
Zhang, P., Liu, W., Wang, H., Lei, Y., & Lu, H. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.12.021 · Cited by 95
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Comelli, A., Dahiya, N., Stefano, A., Vernuccio, F., Portoghese, M., Cutaia, G., Bruno, A., Salvaggio, G., & Yezzi, A. J. (2021) · Applied Sciences · doi:10.3390/app11020782 · Cited by 93
Zhang, X., Zhu, K., Chen, G., Tan, X., Zhang, L., Dai, F., Liao, P., & Gong, Y. (2019) · Remote Sensing · doi:10.3390/rs11070755 · Cited by 92
SCN: Switchable Context Network for Semantic Segmentation of RGB-D Images
Lin, D., Zhang, R., Ji, Y., Li, P., & Huang, H. F. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2885062 · Cited by 86
Multiple attentional path aggregation network for marine object detection
Yu, H., Li, X., Feng, Y., & Han, S. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03622-0 · Cited by 89
Image segmentation review: Theoretical background and recent advances
Brar, K. K., Goyal, B., Dogra, A., Mustafa, M. A., Majumdar, R., Alkhayyat, A. H., & Kukreja, V. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102608 · Cited by 102
RailNet: A Segmentation Network for Railroad Detection
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Learning Deep Spatio-Temporal Dependence for Semantic Video Segmentation
Qiu, Z., Yao, T., & Mei, T. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2759504 · Cited by 82
Generalized Deep Image to Image Regression
Santhanam, V., Morariu, V. I., & Davis, L. S. (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.2017.573 · Cited by 77
Slow Flow: Exploiting High-Speed Cameras for Accurate and Diverse Optical Flow Reference Data
Janai, J., Güney, F., Wulff, J., Black, M. J., & Geiger, A. (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.2017.154 · Cited by 83
FPANet: Feature pyramid aggregation network for real-time semantic segmentation
Wu, Y. J., Jiang, J., Huang, Z., & Tian, Y. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02603-z · Cited by 79
Sun, Y. J., Tian, Y. W., & Xu, Y. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.11.051 · Cited by 77
Laplacian Feature Pyramid Network for Object Detection in VHR Optical Remote Sensing Images
Zhang, W., Jiao, L., Li, Y., Huang, Z., & Wang, H. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3072488 · Cited by 76
Multiscale Deep Learning for Detection and Recognition: A Comprehensive Survey
Jiao, L., Wang, M., Liu, X., Li, L., Liu, F., Feng, Z., Yang, S., & Hou, B. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3389454 · Cited by 83
Li, Q., Yang, W., Liu, W., Yu, Y., & He, S. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00716 · Cited by 71
Understanding the effective receptive field in semantic image segmentation
Liu, Y., Yu, J., & Han, Y. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-5704-3 · Cited by 70
Stacked U-Nets for Ground Material Segmentation in Remote Sensing Imagery
Ghosh, A., Ehrlich, M., Shah, S., Davis, L. S., & Chellappa, R. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00047 · Cited by 70
Active Boundary Loss for Semantic Segmentation
Wang, C., Zhang, Y., Cui, M., Ren, P., Yang, Y., Xie, X., Hua, X., Bao, H., & Xu, W. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i2.20139 · Cited by 75
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
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MCANet: Medical Image Segmentation with Multi-scale Cross-axis Attention
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Embedding Attention and Residual Network for Accurate Salient Object Detection
Chen, S., Wang, B., Tan, X., & Hu, X. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2879859 · Cited by 67
Hitchhiker's Guide to Super-Resolution: Introduction and Recent Advances
Moser, B. B., Raue, F., Frolov, S., Palacio, S., Hees, J., & Dengel, A. R. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3243794 · Cited by 73
Boundary-Aware Multitask Learning for Remote Sensing Imagery
Wang, Y., Ding, W., Zhang, R., & Li, H. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.3043442 · Cited by 67
Automatic liver segmentation by integrating fully convolutional networks into active contour models
Guo, X., Schwartz, L. H., & Zhao, B. (2019) · Medical Physics · doi:10.1002/mp.13735 · Cited by 61
Self-attention feature fusion network for semantic segmentation
Zhou, Z., Zhou, Y., Wang, D., Mu, J., & Zhou, H. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.04.106 · Cited by 62
Weak Micro-Scratch Detection Based on Deep Convolutional Neural Network
Song, L., Lin, W., Yang, Y.-G., Zhu, X., Guo, Q., & Xi, J. (2019) · IEEE Access · doi:10.1109/access.2019.2894863 · Cited by 61
Sun, M., Zhang, G., Dang, H., Qi, X., Zhou, X., & Chang, Q. H. (2019) · IEEE Access · doi:10.1109/access.2019.2918800 · Cited by 61
Revisiting Image Pyramid Structure for High Resolution Salient Object Detection
Kim, T., Kim, K., Lee, J., Cha, D., Lee, J., & Kim, D. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26293-7_16 · Cited by 60
Semantic Line Detection and Its Applications
Lee, J.-T., Kim, H., Lee, C., & Kim, C. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.350 · Cited by 60
Contextual deconvolution network for semantic segmentation
Fu, J., Liu, J., Li, Y., Bao, Y., Yan, W., Fang, Z., & Lu, H. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2019.107152 · Cited by 58
Semantic Image Segmentation: Two Decades of Research
Csurka, G., Volpi, R., & Chidlovskii, B. (2022) · Foundations and Trends® in Computer Graphics and Vision · doi:10.1561/0600000095 · Cited by 84
Hierarchical Density-Aware Dehazing Network
Zhang, J., Ren, W., Zhang, S., Zhang, H., Nie, Y., Xue, Z., & Cao, X. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3070310 · Cited by 57
Cheng, H., Wu, H., Zheng, J., Qi, K., & Liu, W. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.10.001 · Cited by 57
Deep Blur Mapping: Exploiting High-Level Semantics by Deep Neural Networks
Ma, K., Fu, H., Liu, T., Wang, Z., & Tao, D. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2847421 · Cited by 57
FoveaNet: Perspective-Aware Urban Scene Parsing
Li, X. Y., Jie, Z., Wang, W., Liu, C., Yang, J., Shen, X., Lin, Z., Chen, Q., Yan, S., & Feng, J. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.91 · Cited by 57
Optimized building extraction from high-resolution satellite imagery using deep learning
Raghavan, R., Verma, D. C., Pandey, D., Anand, R., Pandey, B. K., & Singh, H. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13493-9 · Cited by 55
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Toft, C., Olsson, C., & Kahl, F. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.83 · Cited by 55
Efficient Ladder-Style DenseNets for Semantic Segmentation of Large Images
Krešo, I., Krapac, J., & Šegvić, S. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2984894 · Cited by 59
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Kong, S., & Fowlkes, C. C. (2019) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2019.00114 · Cited by 52
Self-citationNovel Method of Semantic Segmentation Applicable to Augmented Reality
Taeyoung, K., & Lee, S.-H. (2020) · Sensors · doi:10.3390/s20061737 · Cited by 53
Segmenting areas of potential contamination for adaptive robotic disinfection in built environments
Hu, D., Zhong, H., Li, S., Tan, J., & He, Q. (2020) · Building and Environment · doi:10.1016/j.buildenv.2020.107226 · Cited by 56
Learning to Map Vehicles into Bird’s Eye View
Palazzi, A., Borghi, G., Abati, D., Calderara, S., & Cucchiara, R. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-68560-1_21 · Cited by 52
STC-GAN: Spatio-Temporally Coupled Generative Adversarial Networks for Predictive Scene Parsing
Qi, M., Wang, Y., Li, A., & Luo, J. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2983567 · Cited by 53
Multi-feature fusion network for road scene semantic segmentation
Sun, J., & Li, Y. (2021) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2021.107155 · Cited by 56
Ladder-Style DenseNets for Semantic Segmentation of Large Natural Images
Krapac, J., & Segvic, I. K. S. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.37 · Cited by 51
Learning Object Interactions and Descriptions for Semantic Image Segmentation
Wang, G., Luo, P., Lin, L., & Wang, X. (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.2017.556 · Cited by 51
Multi-Level Contextual RNNs With Attention Model for Scene Labeling
Fan, H., Mei, X., Prokhorov, D. V., & Ling, H. (2018) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2775628 · Cited by 51
A Survey on Deep Learning Methods for Robot VisionPreprint
Ruíz-del-Solar, J., Loncomilla, P., & Soto, N. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.10862 · Cited by 73
A novel framework for semantic segmentation with generative adversarial network
Zhu, X., Zhang, X., Zhang, X., Xue, Z., & Wang, L. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2018.11.020 · Cited by 50
Score-based mask edge improvement of Mask-RCNN for segmentation of fruit and vegetables
Hameed, K., Chai, D., & Rassau, A. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116205 · Cited by 50
A novel embedded cross framework for high-resolution salient object detection
Wang, B., Yang, M., Cao, P., & Liu, Y. (2025) · Applied Intelligence · doi:10.1007/s10489-024-06073-x · Cited by 50
End-to-End Instance Segmentation and Counting with Recurrent Attention.Preprint
Ren, M., & Zemel, R. S. (2016) · arXiv (Cornell University) · Cited by 50
Qin, N., Hu, X., & Dai, H. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.03.011 · Cited by 50
Dense Convolutional Networks for Semantic Segmentation
Han, C., Duan, Y., Tao, X., & Lü, J. (2019) · IEEE Access · doi:10.1109/access.2019.2908685 · Cited by 51
Panoptic Feature Pyramid NetworksPreprint
Kirillov, A., Girshick, R., He, K., & Dollár, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.02446 · Cited by 66
Zhang, X., Li, L., Di, D., Wang, J., Chen, G., Jing, W., & Emam, M. (2022) · Remote Sensing · doi:10.3390/rs14194770 · Cited by 48
Parallel Fully Convolutional Network for Semantic Segmentation
Ji, J., Lu, X., Luo, M., Yin, M., Miao, Q., & Liu, X. (2020) · IEEE Access · doi:10.1109/access.2020.3042254 · Cited by 48
Comelli, A., Coronnello, C., Dahiya, N., Benfante, V., Palmucci, S., Basile, A., Vancheri, C., Russo, G. I., Yezzi, A. J., & Stefano, A. (2020) · Journal of Imaging · doi:10.3390/jimaging6110125 · Cited by 48
Xu, C., Jiang, H., Yuen, P. W. T., Ahmad, K. Z., & Chen, Y. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105375 · Cited by 46
Liu, J., Wang, X., & Tai, X. (2022) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-022-01087-x · Cited by 53
Degradation-Invariant Enhancement of Fundus Images via Pyramid Constraint Network
Liu, H., Li, H., Fu, H., Xiao, R., Gao, Y., Hu, Y., & Liu, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16434-7_49 · Cited by 45
Learning Fully Dense Neural Networks for Image Semantic Segmentation
Zhen, M., Wang, J., Zhou, L., Fang, T., & Quan, L. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33019283 · Cited by 45
VLASE: Vehicle Localization by Aggregating Semantic Edges
Yu, X., Chaturvedi, S., Feng, C., Taguchi, Y., Lee, T., Fernandes, C., & Ramalingam, S. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8594358 · Cited by 44
Zhuang, Y., Yang, F., Li, T., Ma, C., Zhang, Z., Li, Y., Jia, H., Xie, X., & Gao, W. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451830 · Cited by 41
Chiu, T.-Y., & Gurari, D. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00303 · Cited by 43
An Improved Image Semantic Segmentation Method Based on Superpixels and Conditional Random Fields
Zhao, W., Fu, Y., Wei, X., & Wang, H. (2018) · Applied Sciences · doi:10.3390/app8050837 · Cited by 43
Intrinsic Image Transformation via Scale Space Decomposition
Cheng, L., Zhang, C., & Liao, Z. (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.2018.00075 · Cited by 42
Deep Inception-Residual Laplacian Pyramid Networks for Accurate Single-Image Super-Resolution
Tang, Y., Gong, W., Chen, X., & Li, W. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2920852 · Cited by 42
UCTNet: Uncertainty-Aware Cross-Modal Transformer Network for Indoor RGB-D Semantic Segmentation
Ying, X., & Chuah, M. C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20056-4_2 · Cited by 42
Deep Video DeblurringPreprint
Su, S., Delbracio, M., Wang, J., Sapiro, G., Heidrich, W., & Wang, O. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.08387 · Cited by 42
LHNet: Laplacian Convolutional Block for Remote Sensing Image Scene Classification
Zhang, W., Jiao, L., Liu, F., Liu, J., & Cui, Z. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3192321 · Cited by 41
Multi-scale Microaneurysms Segmentation Using Embedding Triplet Loss
Sarhan, M. H., Albarqouni, S., Yiğitsoy, M., Navab, N., & Eslami, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32239-7_20 · Cited by 41
Dynamic attention network for semantic segmentation
Wu, F., Chen, F., Jing, X., Hu, C., Ge, Q., & Ji, Y. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.12.042 · Cited by 41
Path aggregation U-Net model for brain tumor segmentation
Lin, F., Wu, Q., Liu, J., Wang, D., & Kong, X. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-08795-9 · Cited by 41
Learning and Adapting Robust Features for Satellite Image Segmentation on Heterogeneous Data Sets
Ghassemi, S., Fiandrotti, A., Francini, G., & Magli, E. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2906689 · Cited by 43
Exploiting Operation Importance for Differentiable Neural Architecture Search
Zhou, Y., Xie, X., & Kung, S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3072950 · Cited by 43
Toward Achieving Robust Low-Level and High-Level Scene Parsing
Shuai, B., Ding, H., Liu, T., Wang, G., & Jiang, X. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2878975 · Cited by 38
MSDF-Net: Multi-Scale Deep Fusion Network for Stroke Lesion Segmentation
Liu, X., Yang, H., Qi, K., Dong, P., Liu, Q., Liu, X., Wang, R., & Wang, S. (2019) · IEEE Access · doi:10.1109/access.2019.2958384 · Cited by 38
PCANet: Pyramid convolutional attention network for semantic segmentation
Sang, H., Zhou, Q., & Zhao, Y. J. (2020) · Image and Vision Computing · doi:10.1016/j.imavis.2020.103997 · Cited by 37
Park, S., & Heo, Y. S. (2020) · Sensors · doi:10.3390/s20164616 · Cited by 39
A framework of wound segmentation based on deep convolutional networks
Liu, X., Wang, C., Li, F., Zhao, X. J., Zhu, E., & Peng, Y. (2017) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei.2017.8302184 · Cited by 37
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Liu, Z., Shi, Q., & Ou, J. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3215852 · Cited by 37
Yin, X.-X., Hadjiloucas, S., Zhang, Y., & Tian, Z. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106510 · Cited by 36
Hierarchical objectness network for region proposal generation and object detection
Wang, J., Tao, X., Xu, M., Duan, Y., & Lü, J. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.05.009 · Cited by 35
SEMEDA: Enhancing segmentation precision with semantic edge aware loss
Chen, Y., Dapogny, A., & Cord, M. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107557 · Cited by 34
Bangunharcana, A., Magd, A., & Kim, K.-S. (2023) · 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/cvpr52729.2023.00077 · Cited by 35
Scaling CNNs for High Resolution Volumetric Reconstruction from a Single Image
Johnston, A., Garg, R., Carneiro, G., Reid, I. D., & van den Hengel, A. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.114 · Cited by 35
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Yao, X., Guo, J., Hu, J., & Cao, Q. (2019) · IEEE Access · doi:10.1109/access.2019.2905546 · Cited by 35
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
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