Deep-recursive residual network for image semantic segmentation
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Multimodal fusion strategies can be categorized into early, mid, and late fusion based on fusion strategy.
“As illustrated in Figure 3, multimodal fusion strategies can be categorized into early fusion, mid fusion, and late fusion based on the fusion strategy [38,39].”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [38]
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
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684 · see citing works
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138 · see citing works
He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.322 · Cited by 29,137 · see citing works
MobileNetV2: Inverted Residuals and Linear Bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00474 · Cited by 28,755 · see citing works
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105 · 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
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017) · arXiv · doi:10.48550/arxiv.1704.04861 · Cited by 19,015 · 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
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
Lafferty, J., McCallum, A., & Pereira, F. C. N. (2001) · ScholarlyCommons (University of Pennsylvania) · Cited by 12,953 · see citing works
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.19 · Cited by 12,696 · 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
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 · see citing works
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
Zhang, X., Zhou, X., Lin, M., & Sun, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00716 · Cited by 9,982 · see citing works
Self-citationDeeply-Recursive Convolutional Network for Image Super-Resolution
Kim, J., Lee, J. K., & Lee, K. M. (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.2016.181 · Cited by 3,122 · see citing works
(2011) · Cited by 2,752 · see citing works
Image Super-Resolution via Deep Recursive Residual Network
Tai, Y. Y., Yang, J. J., & Liu, 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.2017.298 · Cited by 2,494 · 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 2× · see citing works
“[41] further reformulated CRFs as a constrained recursive neural network (CRF, RNN) that performs approximate variational inference and found that end-to-end learning of a CRF-RNN on top of a feed-forward convolutional network improved upon previous state-of-the-art results.”
BackgroundMethodLinkNet: Exploiting encoder representations for efficient semantic segmentation
Chaurasia, A., & Culurciello, E. (2017) · IEEE International Conference on Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip.2017.8305148 · Cited by 1,972 · see citing works
DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations
Liu, Z., Luo, P., Qiu, S., Wang, X., & Tang, X. (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.2016.124 · Cited by 1,837 · see citing works
Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation
Denton, R., Zaremba, W., Bruna, J., LeCun, Y., & Fergus, R. (2014) · arXiv · doi:10.48550/arxiv.1404.0736 · Cited by 1,414 · see citing works
Deep Neural Networks for Object Detection
Szegedy, C., Toshev, A., & Erhan, D. (2013) · Cited by 1,313 · see citing works
Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition
Ranzato, M., Huang, F., Boureau, Y.-L., & LeCun, Y. (2007) · 2007 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2007.383157 · Cited by 1,186 · see citing works
Scalable Object Detection Using Deep Neural Networks
Erhan, D., Szegedy, C., Toshev, A., & Anguelov, D. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.276 · Cited by 1,161 · cited 1× · see citing works
“Benefitting from recent achievement in machine learning, convolutional neural networks have become ubiquitous in different learning tasks in computer vision and pattern recognition areas, which include image recognition [19, 31, 34], object detection [7, 9, 27, 33], semantic segmentation [4, 10, 40], instance segmenta…”
BackgroundDeep Watershed Transform for Instance Segmentation
Bai, M., & Urtasun, R. (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.2017.305 · Cited by 563 · see citing works
Data-free Parameter Pruning for Deep Neural Networks
Srinivas, S., & Babu, R. V. (2015) · British Machine Vision Conference (BMVC) · doi:10.5244/c.29.31 · Cited by 518 · see citing works
Be Your Own Prada: Fashion Synthesis with Structural Coherence
Zhu, S., Fidler, S., Urtasun, R., Lin, D., & Loy, C. C. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.186 · Cited by 299 · see citing works
Feature Space Optimization for Semantic Video Segmentation
Kundu, A., Vineet, V., & Koltun, V. (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.2016.345 · Cited by 177 · see citing works
Low-Rank Matrix Factorization under General Mixture Noise Distributions
Cao, X., Chen, Y., Zhao, Q., Meng, D., Wang, Y., Wang, D., & Xu, Z. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.175 · Cited by 92 · see citing works
Flagged first, then by citations
2 of the 35 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Residual-driven Fuzzy C-Means Clustering for Image Segmentation
Wang, C., Pedrycz, W., Li, Z., & Zhou, M. (2021) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2020.1003420 · Cited by 119 · see citing works
Deep-Learning-Based Approaches for Semantic Segmentation of Natural Scene Images: A Review
Soylu, B. E., Güzel, M. S., Bostancı, E., Ekinci, F., Aşuroğlu, T., & Açıcı, K. (2023) · Electronics · doi:10.3390/electronics12122730 · Cited by 110 · see citing works
Multi-scale strip pooling feature aggregation network for cloud and cloud shadow segmentation
Chen, L., Xia, M., & Lin, H. (2022) · Neural Computing and Applications · doi:10.1007/s00521-021-06802-0 · Cited by 55 · see citing works
Lin, M., Hou, B., Liu, L., Gordon, M. O., Kass, M. A., Wang, F., Tassel, S. H. V., & Peng, Y. (2022) · Scientific Reports · doi:10.1038/s41598-022-17753-4 · Cited by 43 · see citing works
Self-citationComparative Study on Noise-Estimation-Based Fuzzy C-Means Clustering for Image Segmentation
Wang, C., Zhou, M., Pedrycz, W., & Li, Z. (2022) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2022.3217897 · Cited by 37 · see citing works
Artificial intelligence in tumor subregion analysis based on medical imaging: A review
Lin, M., Wynne, J. F., Zhou, B., Wang, T., Lei, Y., Curran, W. J., Liu, T., & Yang, X. (2021) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.13321 · Cited by 32 · see citing works
Self-citationClothes image caption generation with attribute detection and visual attention model
Li, X., Ye, Z., Zhang, Z., & Zhao, M. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.12.001 · Cited by 29 · see citing works
Self-citationZero-shot classification with unseen prototype learning
Ji, Z., Cui, B., Yu, Y., Pang, Y., & Zhang, Z. M. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-05746-9 · Cited by 18 · see citing works
MFGAN: multi feature guided aggregation network for remote sensing image
Chu, S., Li, P., & Xia, M. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-06999-8 · Cited by 17 · see citing works
An End-to-End Framework for Clothing Collocation Based on Semantic Feature Fusion
Zhao, M., Liu, Y., Li, X., Zhang, Z., & Zhang, Y. (2020) · IEEE Multimedia · doi:10.1109/mmul.2020.3024221 · Cited by 17 · see citing works
Self-citationID-Net: an improved mask R-CNN model for intrusion detection under power grid surveillance
Gao, F., Ji, S., Guo, J., Li, Q., Ji, Y., Liu, Y., Feng, S., Wei, H., Wang, N., & Yang, B. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-05688-2 · Cited by 17 · see citing works
Primary Open-Angle Glaucoma Diagnosis from Optic Disc Photographs Using a Siamese Network
Lin, M., Liu, L., Gordon, M. O., Kass, M. A., Wang, F., Tassel, S. H. V., & Peng, Y. (2022) · Ophthalmology Science · doi:10.1016/j.xops.2022.100209 · Cited by 15 · see citing works
Self-citationGraph Convolutional Networks for Semi-Supervised Image Segmentation
Fabijańska, A. (2022) · IEEE Access · doi:10.1109/access.2022.3210533 · Cited by 13 · see citing works
ÇELİK, M., & İni̇k, Ö. (2025) · Displays · doi:10.1016/j.displa.2025.103225 · Cited by 14 · see citing works
DASGC-Unet: An Attention Network for Accurate Segmentation of Liver CT Images
Zhang, X., Chen, Y., Pu, L., He, Y., Zhou, Y., & Sun, H. (2023) · Neural Processing Letters · doi:10.1007/s11063-023-11421-y · Cited by 6 · see citing works
Tang, J., Wang, L., Huang, J. Y., Shi, A., & Xu, L. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/2439371 · Cited by 6 · see citing works
MFFLNet: lightweight semantic segmentation network based on multi-scale feature fusion
Depeng, W., & Huabin, W. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16782-z · Cited by 5 · see citing works
Audio-Visual Spatial Integration and Recursive Attention for Robust Sound Source Localization
Um, S. J., Kim, D., & Kim, J. U. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3611722 · Cited by 4 · see citing works
Li, X., Rong, Y., Zhao, M., & Fan, J. (2021) · Communications in computer and information science · doi:10.1007/978-981-16-5188-5_41 · Cited by 3 · see citing works
Self-citationKanchanamala, P., Bhamidipati, K., Arunachalam, R., Kumar, B. R., Gampala, V., & Merugula, S. (2026) · Ultrasonic Imaging · doi:10.1177/01617346251413799 · Cited by 2 · see citing works
Gurav, Y. B., Alagirisamy, M., & Selvaperumal, S. K. (2025) · Intelligent Decision Technologies · doi:10.1177/18724981251331118 · Cited by 2 · see citing works
AI Deep Learning Methods for Segmenting 3D-Brain Tumors Images using U-Net
Appavu, N. (2025) · International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) · doi:10.1109/icdsaai65575.2025.11011740 · Cited by 2 · see citing works
Maneesha, L., & Bharti, P. (2026) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2026.111739 · Cited by 1 · see citing works
Zhu, D., Shao, X., Zhang, K., Min, X., Zhai, G., & Yang, X. (2023) · Applied Intelligence · doi:10.1007/s10489-023-04714-1 · Cited by 1 · see citing works
Research progress and challenges in real-time semantic segmentation for deep learning
Wang, Z., & Qu, S. (2024) · Journal of Image and Graphics · doi:10.11834/jig.230605 · Cited by 3 · see citing works
Review on Deep Learning Network Architectures for Image Reconstruction
Lokula, B., Prasad, L. V. N., & Tirumuri, R. (2024) · International Conference on Image Processing and Capsule Networks (ICIPCN) · doi:10.1109/icipcn63822.2024.00024 · Cited by 1 · see citing works
Research on Intelligent Acquisition Techniques for Field Grassland Vegetation CoverPreprint
Liu, H., Shen, C., Bao, A., Gulakhmadov, A., & Shaimuradov, F. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5473206
- Authors
- Zhang, Y., Li, X., Lin, M., Chiu, B., & Zhao, M.
- Affiliations
- Donghua University (CN)
- City University of Hong Kong (HK)
- Corresponding author
- Zhao, Mingbo
- Venue
- Neural Computing and Applications
- Published
- 2020-01-24
- Volume
- 32
- Issue
- 16
- Pages
- 12935-12947
- ISSN
- 0941-0643 (ISSN-L)
- 1433-3058
- OpenAlex
- W3002840510
- Cited by
- 27
- References
- 35
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 2 · 2023: 5 · 2022: 7 · 2021: 5 · 2020: 2
- FWCI
- 1.85
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neural Computing and Applications (publishedVersion)
- Funders
- National Natural Science Foundation of China (61971121, 61601112)
- 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 › Engineering › Media Technology › Remote-Sensing Image Classification
- Keywords
- semantic segmentation, recursive convolution, parameter reduction, residual blocks, multi-scale feature fusion, instance segmentation, Mask R-CNN, lightweight neural networks
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
