The DOI matches its Crossref record; 37 of the 41 references it cites were found in a registry or an index.
Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation
Open accessOverview
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
Verification
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Similar to [29], we find that sharing features improves accuracy by a small margin.”
From Feature Pyramid Networks for Object Detection (2017) · cites this paper as [29]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“For these reasons, Fast and Faster R-CNN [11, 29] opt to not use featurized image pyramids under default settings.”
From Feature Pyramid Networks for Object Detection (2017) · cites this paper as [29]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“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 } .”
From Feature Pyramid Networks for Object Detection (2017) · cites this paper as [29]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Sorted by citations
Items 201–300 of 500
Zig-Zag Network for Semantic Segmentation of RGB-D Images
Lin, D., & Huang, H. F. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2923513 · Cited by 34
Zhao, H., Lu, M., Yao, A., Chen, Y., & Zhang, L. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01263-4 · Cited by 34
Transferring and Regularizing Prediction for Semantic Segmentation
Zhang, Y., Qiu, Z., Yao, T., Ngo, C., Liu, D., & Mei, T. (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/cvpr42600.2020.00964 · Cited by 34
Global-residual and Local-boundary Refinement Networks for Rectifying Scene Parsing Predictions
Zhang, R., Tang, S., Lin, M., Li, J., & Yan, S. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/479 · Cited by 34
Lin, X., Sánchez-Escobedo, D., Casas, J. R., & Pardàs, M. (2019) · Sensors · doi:10.3390/s19081795 · Cited by 33
MarsNet: Multi-Label Classification Network for Images of Various Sizes
Park, J., Hwang, Y., Lee, D., & Kim, J.-H. (2020) · IEEE Access · doi:10.1109/access.2020.2969217 · Cited by 33
Human Parsing With Pyramidical Gather-Excite Context
Zhang, S., Qi, G.-J., Cao, X., Song, Z., & Zhou, J. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.2990531 · Cited by 32
Remote sensing image denoising based on improved semi-soft threshold
Lei, S., Lu, M., Lin, J., Zhou, X., & Yang, X. (2020) · Signal Image and Video Processing · doi:10.1007/s11760-020-01722-3 · Cited by 31
Amiruzzaman, M., Curtis, A., Zhao, Y., Jamonnak, S., & Ye, X.-Y. (2021) · Journal of Computational Social Science · doi:10.1007/s42001-021-00107-x · Cited by 29
Multi-Exit Semantic Segmentation Networks
Kouris, A., Venieris, S. I., Laskaridis, S., & Lane, N. D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19803-8_20 · Cited by 36
DPANet: Dual Pooling‐aggregated Attention Network for fish segmentation
Zhang, W., Wu, C., & Bao, Z. (2021) · IET Computer Vision · doi:10.1049/cvi2.12065 · Cited by 34
Semantic Segmentation Refinement by Monte Carlo Region Growing of High Confidence Detections
Dias, P. A., & Medeiros, H. P. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20890-5_9 · Cited by 3
Xing, S., Dong, Q., & Hu, Z. (2022) · Remote Sensing · doi:10.3390/rs14092252 · Cited by 30
MPE-DETR: A multiscale pyramid enhancement network for object detection in low-light images
Xue, R., Duan, J., & Du, Z. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.105202 · Cited by 32
LSNet: Real-time attention semantic segmentation network with linear complexity
Sheng, P., Shi, Y., Liu, X., & Jin, H. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.08.049 · Cited by 30
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 35
Self-citationOctNet: Learning Deep 3D Representations at High ResolutionsPreprint
Riegler, G., Ulusoy, A. O., & Geiger, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.05009 · Cited by 30
Building Detail-Sensitive Semantic Segmentation Networks With Polynomial Pooling
Wei, Z., Zhang, J., Liu, L., Zhu, F., Shen, F., Zhou, Y., Liu, S., Sun, Y., & Shao, L. (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.2019.00728 · Cited by 29
Resolution Learning in Deep Convolutional Networks Using Scale-Space Theory
Pintea, S. L., Tömen, N., Goes, S. F., Loog, M., & van Gemert, J. C. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3115001 · Cited by 29
Pixel-by-Pixel Cross-Domain Alignment for Few-Shot Semantic Segmentation
Tavera, A., Cermelli, F., Masone, C., & Barbara Caputo, B. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00202 · Cited by 29
FR-NET: Focal Loss Constrained Deep Residual Networks for Segmentation of Cardiac MRI
Chen, M., Fang, L., & Liu, H. (2019) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2019.8759556 · Cited by 28
Medical image segmentation based on active fusion-transduction of multi-stream features
Shu, Y., Zhang, J., Xiao, B., & Li, W. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106950 · Cited by 28
Vieira, P. M., Freitas, N. R., Lima, V. B., Costa, D., Rolanda, C., & Lima, C. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102141 · Cited by 28
Convolutional Neural Pyramid for Image ProcessingPreprint
Shen, X., Chen, Y.-C., Tao, X., & Jia, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.02071 · Cited by 31
BiAttnNet: Bilateral Attention for Improving Real-Time Semantic Segmentation
Li, G., Li, L., & Zhang, J. (2021) · IEEE Signal Processing Letters · doi:10.1109/lsp.2021.3124186 · Cited by 29
Perspective-Adaptive Convolutions for Scene Parsing
Zhang, R., Tang, S., Zhang, Y., Li, J., & Yan, S. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2890637 · Cited by 25
MoE-SPNet: A mixture-of-experts scene parsing network
Fu, H., Gong, M., Wang, C., & Tao, D. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.07.020 · Cited by 27
Real-time semantic segmentation via sequential knowledge distillation
Wu, J., Ji, R., Liu, J., Xu, M., Zheng, J., Shao, L., & Tian, Q. C. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.01.086 · Cited by 26
Ahmed, N. A., Tan, X., & Ma, L. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13618-0 · Cited by 28
Swin-Fusion: Swin-Transformer with Feature Fusion for Human Action Recognition
Chen, T., & Mo, L. (2023) · Neural Processing Letters · doi:10.1007/s11063-023-11367-1 · Cited by 26
Rahman, Z.-U., Zhang, R., & Bhutto, J. A. (2023) · Symmetry · doi:10.3390/sym15101912 · Cited by 26
Deep Learning For Face Recognition: A Critical AnalysisPreprint
Shepley, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.12739 · Cited by 43
On the Road to Online Adaptation for Semantic Image Segmentation
Volpi, R., de Jorge, P., Larlus, D., & Csurka, G. (2022) · 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/cvpr52688.2022.01859 · Cited by 28
Exploring New Backbone and Attention Module for Semantic Segmentation in Street Scenes
Fan, L., Wang, W.-C., Zha, F.-Y., & Yan, J. (2018) · IEEE Access · doi:10.1109/access.2018.2880877 · Cited by 25
Ashraf, M., Chen, L., Innab, N., Umer, M., Baili, J., Kim, T.-H., & Ashraf, I. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3422078 · Cited by 24
Oluwasanmi, A., Aftab, M. U., Qin, Z., Son, N. T., Doan, T. V., Nguyen, S. B., Nguyen, S. H., & Nguyen, G. (2021) · Complexity · doi:10.1155/2021/5538927 · Cited by 25
Zhang, L., Dong, D., Zhong, L., Li, C., Hu, C. Y., Yang, X., Liu, Z., Wang, R., Zhou, J., & Tian, J. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3087634 · Cited by 24
Chamola, V., Chougule, A., Sam, A., Hussain, A., & Yu, F. R. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3362851 · Cited by 26
Wu, Y., Shah, S. K., & Kakadiaris, I. A. (2017) · Image and Vision Computing · doi:10.1016/j.imavis.2017.12.002 · Cited by 23
Semantic Segmentation With Global Encoding and Dilated Decoder in Street Scenes
Fan, L., Kong, H., Wang, W.-C., & Yan, J. (2018) · IEEE Access · doi:10.1109/access.2018.2868801 · Cited by 24
Spatial Sampling Network for Fast Scene Understanding
Mazzini, D., & Schettini, R. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00168 · Cited by 23
Deep Multiphase Level Set for Scene Parsing
Zhang, P., Liu, W., Lei, Y., Wang, H., & Lu, H. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2957915 · Cited by 23
Classification of hyperspectral imagery using spectrally partitioned HyperUnet
Paul, A., & Bhoumik, S. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06532-3 · Cited by 23
Mixed spatial pyramid pooling for semantic segmentation
Xia, Z., & Kim, J. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106209 · Cited by 22
Human Parsing With Part-Aware Relation Modeling
Zhang, X., Chen, Y., Tang, M., Wang, J., Zhu, X., & Lei, Z. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3148595 · Cited by 22
Context-Integrated and Feature-Refined Network for Lightweight Object Parsing
Jiang, B., Tu, W., Yang, C., & Yuan, J. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2978583 · Cited by 21
Large-scale tissue histopathology image segmentation based on feature pyramid
Qin, P., Chen, J., Zeng, J., Chai, R., & Wang, L. (2018) · EURASIP Journal on Image and Video Processing · doi:10.1186/s13640-018-0320-8 · Cited by 21
Residual Pyramid Learning for Single-Shot Semantic Segmentation
Chen, X., Lou, X., Bai, L., & Han, J. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2922252 · Cited by 21
A Visual Intelligence Scheme for Hard Drive Disassembly in Automated Recycling Routines
Yildiz, E., Brinker, T., Renaudo, E., Hollenstein, J., Haller-Seeber, S., Piater, J., & Wörgötter, F. (2020) · International Conference on Robotics, Computer Vision and Intelligent Systems (ROBOVIS) · doi:10.5220/0010016000170027 · Cited by 20
Improving Pedestrian Attribute Recognition with Multi-Scale Spatial Calibration
Zhong, J., Qiao, H., Chen, L., Shang, M., & Liu, Q. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533647 · Cited by 20
Borse, S., Park, H., Cai, H., Das, D., Garrepalli, R., & Porikli, F. (2022) · 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/cvpr52688.2022.00133 · Cited by 20
Xu, G.-X., & Ren, C.-X. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.12.039 · Cited by 20
Pyramid-Based Fully Convolutional Networks for Cell Segmentation
Zhao, T., & Yin, Z. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00937-3_77 · Cited by 20
Ontology-based conditional random fields for object recognition
Ruiz-Sarmiento, J.-R., Galindo, C., Monroy, J., Moreno, F.-Á., & González-Jiménez, J. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.01.005 · Cited by 20
Zhou, F., Hu, Y., & Shen, X. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.11.042 · Cited by 20
Improving Fully Convolution Network for Semantic SegmentationPreprint
Shuai, B., Liu, T., & Wang, G. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.08986 · Cited by 20
Hierarchical Semantic Broadcasting Network for Real-Time Semantic Segmentation
Li, G., Li, L., & Zhang, J. (2021) · IEEE Signal Processing Letters · doi:10.1109/lsp.2021.3129418 · Cited by 21
Content-Style Decoupling for Unsupervised Makeup Transfer without Generating Pseudo Ground Truth
Sun, Z., Xiong, S., Chen, Y., & Rong, Y. (2024) · 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/cvpr52733.2024.00726 · Cited by 19
Training-Free, Single-Image Super-Resolution Using a Dynamic Convolutional Network
Bhowmik, A., Shit, S., & Seelamantula, C. S. (2017) · IEEE Signal Processing Letters · doi:10.1109/lsp.2017.2752806 · Cited by 19
Preserving details in semantics-aware context for scene parsing
Ma, S., Pang, Y., Pan, J. S., & Shao, L. (2020) · Science China Information Sciences · doi:10.1007/s11432-019-2738-y · Cited by 19
InstanceCut: from Edges to Instances with MultiCutPreprint
Kirillov, A., Levinkov, E., Andres, B., Savchynskyy, B., & Rother, C. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.08272 · Cited by 25
Global-Guided Selective Context Network for Scene Parsing
Jiang, J., Liu, J., Fu, J., Zhu, X., Li, Z., & Lu, H. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3043808 · Cited by 18
GDN: Guided down-sampling network for real-time semantic segmentation
Luo, D., Kang, H., Long, J., Zhang, J., Liu, X., & Quan, T. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.11.075 · Cited by 19
MS-IRTNet: Multistage information interaction network for RGB-T semantic segmentation
Zhang, Z., Liu, Y., & Xue, W. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119442 · Cited by 17
Learning to Reconstruct 3D Structures for Occupancy Mapping
Guizilini, V., & Ramos, F. T. (2017) · Robotics: Science and Systems · doi:10.15607/rss.2017.xiii.009 · Cited by 18
RelationNet: Learning Deep-Aligned Representation for Semantic Image Segmentation
Zhuang, Y., Li, T., Yang, F., Ma, C., Zhang, Z., Jia, H., & Xie, X. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8545708 · Cited by 18
MaskSplit: Self-supervised Meta-learning for Few-shot Semantic Segmentation
Amac, M. S., Sencan, A., Baran, O. B., İkizler-Cinbiş, N., & Cinbiş, R. G. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00050 · Cited by 18
Li, X., Tan, Y., Liu, K., Wang, X., & Zhou, X. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3544402 · Cited by 20
Drivable Road Detection Based on Dilated FPN with Feature Aggregation
Liu, X., Deng, Z., & Yang, G. (2017) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2017.00172 · Cited by 17
CNN-Based Illumination Estimation with Semantic Information
Choi, H.-H., Kang, H., & Yun, B. (2020) · Applied Sciences · doi:10.3390/app10144806 · Cited by 17
Shi, F., & Zhang, T. (2021) · Remote Sensing · doi:10.3390/rs13142656 · Cited by 17
Gated Feedback Refinement Network for Coarse-to-Fine Dense Semantic Image LabelingPreprint
Islam, M. A., Rochan, M., Naha, S., Bruce, N. D. B., & Wang, Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.11266 · Cited by 17
Erroneous pixel prediction for semantic image segmentation
Gong, L., Zhang, Y.-Q., Zhang, Y.-K., Yang, Y., & Xu, W. (2021) · Computational Visual Media · doi:10.1007/s41095-021-0235-7 · Cited by 14
Modeling the Background for Incremental and Weakly-Supervised Semantic Segmentation
Cermelli, F., Mancini, M., Bulo, S. R., Ricci, E., & Barbara Caputo, B. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3133954 · Cited by 16
Sarhan, M. H., Albarqouni, S., Yiğitsoy, M., Navab, N., & Eslami, A. (2020) · IET Image Processing · doi:10.1049/iet-ipr.2019.0804 · Cited by 17
Bai, S., & Wang, C. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106843 · Cited by 15
Instance-Level Segmentation of Vehicles by Deep Contours
van den Brand, J., Ochs, M., & Mester, R. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-54407-6_32 · Cited by 15
Real-time segmentation network for accurate weld detection in large weldments
Wu, Z., Gao, P., Han, J., Bai, L., Lu, J., & Zhao, Z. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105008 · Cited by 15
Proximal Splitting Networks for Image Restoration
Aljadaany, R., Pal, D. K., & Savvides, M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-27202-9_1 · Cited by 15
Research Advances in Deep Learning for Image Semantic Segmentation Techniques
Xiao, Z., Chai, T., Li, N., Shen, X., Guan, T., Tian, J., & Li, X. (2024) · IEEE Access · doi:10.1109/access.2024.3496723 · Cited by 18
Image Segmentation Using Sparse Subset Selection
Zohrizadeh, F., Kheirandishfard, M., & Kamangar, F. A. (2018) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2018.00165 · Cited by 15
A Comparative Study of Object Classification Methods Using 3D Zernike Moment on 3D Point Clouds
Özbay, E., & Çınar, A. (2019) · Traitement du signal · doi:10.18280/ts.360610 · Cited by 14
Region of interest selection for functional features
Wang, Q., Lu, Y., Zhang, X., & Hahn, J. K. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.10.009 · Cited by 14
High Resolution Feature Recovering for Accelerating Urban Scene Parsing
Zhang, R., Tang, S., Liu, L., Zhang, Y., Li, J., & Yan, S. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/161 · Cited by 14
Large Deformation Image Registration with Anatomy-Aware Laplacian Pyramid Networks
Mok, T. C. W., & Chung, A. C. S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-71827-5_7 · Cited by 14
Structural Priors Guided Network for the Corneal Endothelial Cell Segmentation
Zhang, Y., Xi, R., Zeng, L., Towey, D., Bai, R., Higashita, R., & Liu, J. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3300656 · Cited by 13
Chen, J., Chen, G., Wang, L., Fang, B., Zhou, P., & Zhu, M. (2020) · Sensors · doi:10.3390/s20247032 · Cited by 13
Co-Net: A Collaborative Region-Contour-Driven Network for Fine-to-Finer Medical Image Segmentation
Liu, A., Huang, X., Li, T., & Ma, P. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00177 · Cited by 13
Superpixel-Enhanced Pairwise Conditional Random Field for Semantic Segmentation
Sulimowicz, L., Ishfaq Ahmad, D., & Aved, A. J. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451218 · Cited by 13
DSNet: Joint Learning for Scene Segmentation and Disparity Estimation
Zhan, W., Ou, X., Yang, Y., & Chen, L. F. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793573 · Cited by 13
Multimodal Frequeny Spectrum Fusion Schema for RGB-T Image Semantic Segmentation
Liu, H., Zhang, W., Dai, T., Yin, L., & Ren, G. (2024) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn61486.2024.10637614 · Cited by 12
The defect detection for X-ray images based on a new lightweight semantic segmentation network
Xin, Y., Chen, P., Zhang, Z., & Xiao, L. (2022) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2022193 · Cited by 12
An enhancement model based on dense atrous and inception convolution for image semantic segmentation
Zhou, E., Xu, X., Xu, B., & Wu, H. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03448-w · Cited by 12
Pooling Attention-based Encoder–Decoder Network for semantic segmentation
Xu, H., Huang, Y., Hancock, E. R., Wang, S., Xuan, Q., & Zhou, W. (2021) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2021.107260 · Cited by 12
Semi-automatic image annotation of street scenes
Petrovai, A., Costea, A. D., & Nedevschi, S. (2017) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2017.7995759 · Cited by 12
ÇELİK, M., & İni̇k, Ö. (2025) · Displays · doi:10.1016/j.displa.2025.103225 · Cited by 15
Fusion Scheme for Semantic and Instance-level Segmentation
Costea, A. D., Petrovai, A., & Nedevschi, S. (2018) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2018.8570006 · Cited by 11
Hybridnet for Depth Estimation and Semantic Segmentation
Sánchez-Escobedo, D., Lin, X., Casas, J. R., & Pardàs, M. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8462433 · Cited by 11
Scene Parsing Via Dense Recurrent Neural Networks With Attentional Selection
Fan, H., Chu, P., Latecki, L. J., & Ling, H. (2019) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2019.00198 · Cited by 11
Deep Dual-Stream Network with Scale Context Selection Attention Module for Semantic Segmentation
Liu, Y., Xu, C., Chen, Z., Chen, C., Zhao, H., & Jin, X. (2020) · Neural Processing Letters · doi:10.1007/s11063-019-10148-z · Cited by 11
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 ›
Are you an author, or do you see a problem with this page? Request a review
