CCNet: Criss-Cross Attention for Semantic Segmentation
Abstract
Contextual information is vital in visual understanding problems, such as semantic segmentation and object detection. We propose a criss-cross network (CCNet) for obtaining full-image contextual information in a very effective and efficient way. Concretely, for each pixel, a novel criss-cross attention module harvests the contextual information of all the pixels on its criss-cross path. By taking a further recurrent operation, each pixel can finally capture the full-image dependencies. Besides, a category consistent loss is proposed to enforce the criss-cross attention module to produce more discriminative features. Overall, CCNet is with the following merits: 1) GPU memory friendly. Compared with the non-local block, the proposed recurrent criss-cross attention module requires 11× less GPU memory usage. 2) High computational efficiency. The recurrent criss-cross attention significantly reduces FLOPs by about 85 percent of the non-local block. 3) The state-of-the-art performance. We conduct extensive experiments on semantic segmentation benchmarks including Cityscapes, ADE20K, human parsing benchmark LIP, instance segmentation benchmark COCO, video segmentation benchmark CamVid. In particular, our CCNet achieves the mIoU scores of 81.9, 45.76 and 55.47 percent on the Cityscapes test set, the ADE20K validation set and the LIP validation set respectively, which are the new state-of-the-art results. The source codes are available at https://github.com/speedinghzl/CCNethttps://github.com/speedinghzl/CCNet.
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?
Axial Attention is a simple, yet effective technique to run self-attention on large inputs organized as multidimensional tensors.
“Axial Attention (Huang et al., 2020; Ho et al., 2019) is a simple, yet effective technique to run selfattention on large inputs that are organized as multidimensional tensors.”
From An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (2020) · cites this paper as Huang et al., 2020
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
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · 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
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 · 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
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
Semi-supervised classification with graph convolutional networks
Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853 · 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
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_49 · Cited by 20,054 · see citing works
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2644615 · Cited by 19,631 · 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
Self-citationThe 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
Wang, X., Girshick, R., Gupta, A., & He, K. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00813 · Cited by 11,596 · see citing works
Self-citationAzuma, R. T. (1997) · Presence: Teleoperators and Virtual Environments · doi:10.1162/pres.1997.6.4.355 · Cited by 10,121 · see citing works
The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2005605 · Cited by 9,582 · see citing works
Multi-scale context aggregation by dilated convolutions
Yu, F., & Koltun, V. (2015) · arXiv · doi:10.48550/arxiv.1511.07122 · Cited by 7,738 · see citing works
Rethinking Atrous Convolution for Semantic Image Segmentation
Chen, L.-C., Papandreou, G., Schroff, F., & Adam, H. (2017) · arXiv · doi:10.48550/arxiv.1706.05587 · Cited by 7,517 · see citing works
Deformable Convolutional Networks
Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., & Wei, Y. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.89 · Cited by 7,513 · see citing works
Self-citationDual Attention Network for Scene Segmentation
Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., & Lu, H. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00326 · Cited by 7,351 · see citing works
Neural Message Passing for Quantum ChemistryPreprint
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., & Dahl, G. E. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.01212 · Cited by 5,414 · see citing works
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2014) · arXiv · doi:10.48550/arxiv.1412.7062 · Cited by 4,213 · see citing works
Scene Parsing through ADE20K Dataset
Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., & Torralba, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.544 · Cited by 3,326 · see citing works
RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation
Lin, G., Milan, A., Shen, C., & Reid, I. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.549 · Cited by 3,287 · see citing works
CCNet: Criss-Cross Attention for Semantic Segmentation
Huang, Z., Wang, X., Huang, L., Huang, C., Wei, Y., & Liu, W. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00069 · Cited by 3,045 · see citing works
Self-citationBiSeNet: Bilateral Segmentation Network for Real-Time Semantic Segmentation
Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., & Sang, N. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01261-8_20 · Cited by 2,968 · 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
“[36], [45], Markov random field (MRF) [31] were also utilized to capture long-range dependencies for semantic segmentation.”
MethodLearning Convolutional Neural Networks for GraphsPreprint
Niepert, M., Ahmed, M., & Kutzkov, K. (2016) · International Conference on Machine Learning · doi:10.48550/arxiv.1605.05273 · Cited by 2,311 · see citing works
Unified Perceptual Parsing for Scene Understanding
Xiao, T., Liu, Y., Zhou, B., Jiang, Y., & Sun, J. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01228-1_26 · Cited by 2,201 · cited 1× · see citing works
“Among the approaches, most of methods [7], [10], [12], [29], [65], [66] adopt theResNet-101 as backbone andRefineNet [27] adopts amore powerful network, i.”
MethodUnderstanding Convolution for Semantic Segmentation
Wang, P., Chen, P., Yuan, Y., Liu, D., Huang, Z., Hou, X., & Cottrell, G. W. (2018) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2018.00163 · Cited by 2,127 · see citing works
Self-citationA new model for learning in graph domains
Gori, M., Monfardini, G., & Scarselli, F. (2006) · Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. · doi:10.1109/ijcnn.2005.1555942 · Cited by 2,075 · see citing works
Large Kernel Matters — Improve Semantic Segmentation by Global Convolutional Network
Peng, C., Zhang, X., Yu, G., Luo, G., & Sun, J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.189 · Cited by 1,750 · see citing works
Wider or Deeper: Revisiting the ResNet Model for Visual Recognition
Wu, Z., Shen, C., & van den Hengel, A. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.01.006 · Cited by 1,742 · see citing works
DenseASPP for Semantic Segmentation in Street Scenes
Yang, M., Yu, K., Zhang, C., Li, Z., & Yang, K. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00388 · Cited by 1,674 · see citing works
Context Encoding for Semantic Segmentation
Zhang, H., Dana, K., Shi, J., Zhang, Z., Wang, X., Tyagi, A., & Agrawal, A. (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.2018.00747 · Cited by 1,510 · see citing works
Self-citationAttention 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
Semantic object classes in video: A high-definition ground truth database
Brostow, G., Fauqueur, J., & Cipolla, R. (2008) · Pattern Recognition Letters · doi:10.1016/j.patrec.2008.04.005 · Cited by 1,400 · see citing works
Deep Convolutional Networks on Graph-Structured Data
Henaff, M., Bruna, J., & LeCun, Y. (2015) · arXiv · doi:10.48550/arxiv.1506.05163 · Cited by 1,255 · see citing works
PSANet: Point-wise Spatial Attention Network for Scene Parsing
Zhao, H., Yi, Z., Liu, S., Shi, J., Loy, C. C., Lin, D., & Jia, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01240-3_17 · Cited by 1,233 · see citing works
Long Short-Term Memory-Networks for Machine Reading
Cheng, J.-P., Dong, L., & Lapata, M. (2016) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1053 · Cited by 1,200 · see citing works
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
Liu, C., Chen, L.-C., Schroff, F., Adam, H., Hua, W., Yuille, A., & Fei-Fei, 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.2019.00017 · Cited by 1,076 · see citing works
Segmentation and Recognition Using Structure from Motion Point Clouds
Brostow, G. J., Shotton, J., Fauqueur, J., & Cipolla, R. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-88682-2_5 · Cited by 1,050 · see citing works
Learning a Discriminative Feature Network for Semantic Segmentation
Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., & Sang, 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.2018.00199 · Cited by 923 · see citing works
A new performance measure and evaluation benchmark for road detection algorithms
Fritsch, J., Kuhnl, T., & Geiger, A. (2013) · 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013) · doi:10.1109/itsc.2013.6728473 · Cited by 690 · see citing works
CayleyNets: Graph Convolutional Neural Networks With Complex Rational Spectral Filters
Levie, R., Monti, F., Bresson, X., & Bronstein, M. M. (2019) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2018.2879624 · Cited by 685 · see citing works
Semantic Image Segmentation via Deep Parsing Network
Liu, Z., Li, X., Luo, P., Loy, C.-C., & Tang, X. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.162 · Cited by 675 · see citing works
Supervised neural networks for the classification of structures
Sperduti, A., & Starita, A. (1997) · IEEE Transactions on Neural Networks · doi:10.1109/72.572108 · Cited by 666 · see citing works
Diffusion-Convolutional Neural Networks
Atwood, J. R., & Towsley, D. (2016) · neural information processing systems · Cited by 566 · see citing works
Graph-Based Global Reasoning Networks
Chen, Y., Rohrbach, M., Yan, Z., Shuicheng, Y., Feng, J., & Kalantidis, 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.2019.00052 · Cited by 538 · 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
Self-citationAdaptive Pyramid Context Network for Semantic Segmentation
He, J., Deng, Z., Zhou, L., Wang, Y., & Qiao, 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.2019.00770 · Cited by 447 · see citing works
Context Contrasted Feature and Gated Multi-scale Aggregation for Scene Segmentation
Ding, H., Jiang, X., Shuai, B., Liu, A. Q., & Wang, G. (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.2018.00254 · Cited by 403 · see citing works
Look into Person: Joint Body Parsing & Pose Estimation Network and a New Benchmark
Liang, X., Gong, K., Shen, X., & Lin, L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2820063 · Cited by 366 · see citing works
Searching for Efficient Multi-Scale Architectures for Dense Image Prediction
Chen, L.-C., Collins, M. D., Zhu, Y., Papandreou, G., Zoph, B., Schroff, F., Adam, H., & Shlens, J. (2018) · arXiv · doi:10.48550/arxiv.1809.04184 · Cited by 347 · see citing works
Devil in the Details: Towards Accurate Single and Multiple Human Parsing
Ruan, T., Liu, T., Huang, Z., Wei, Y., Wei, S., & Zhao, Y. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33014814 · Cited by 278 · see citing works
Self-citationSemantic Instance Segmentation for Autonomous Driving
Brabandere, B. D., Neven, D., & Gool, L. V. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.66 · Cited by 246 · see citing works
Learning Affinity via Spatial Propagation NetworksPreprint
Liu, S., Mello, S. D., Gu, J., Zhong, G., Yang, M., & Kautz, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.01020 · Cited by 226 · see citing works
Adaptive Affinity Fields for Semantic Segmentation
Ke, T.-W., Hwang, J.-J., Liu, Z., & Yu, S. X. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01246-5_36 · Cited by 205 · see citing works
Scale-Adaptive Convolutions for Scene Parsing
Zhang, R., Tang, S., Zhang, Y., Li, J., & Yan, S. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.224 · Cited by 195 · cited 6× · see citing works
“Performance on val set DeepLabv3 [8] ResNet-101 79.3 DeepLabv3+ [24] Xception-65 79.1 DPC [40] y Xception-71 80.8 CCNet ResNet-101 80.5 Performance on test set DeepLab-v2 [6] ResNet-101 70.4 RefineNet [27] z ResNet-101 73.6 SAC [29] z ResNet-101 78.1 GCN [49] z ResNet-101 76.9 DUC [63] z ResNet-101 77.6 ResNet-38 [64]…”
MethodInfluentialMulti-scale Context Intertwining for Semantic Segmentation
Lin, D., Ji, Y., Lischinski, D., Cohen‐Or, D., & Huang, H. F. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01219-9_37 · Cited by 182 · see citing works
Dynamic-Structured Semantic Propagation Network
Liang, X., Xing, E. P., & Zhou, H. (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.2018.00085 · Cited by 154 · see citing works
Dense Decoder Shortcut Connections for Single-Pass Semantic Segmentation
Biliński, P., & Prisacariu, V. A. (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.2018.00690 · Cited by 146 · 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
Self-citationEfficient Uncertainty Estimation for Semantic Segmentation in Videos
Huang, P., Hsu, W., Chiu, C.-Y., Wu, T., & Sun, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01246-5_32 · Cited by 123 · see citing works
Deep Spatio-Temporal Random Fields for Efficient Video Segmentation
Chandra, S., Couprie, C., & Kokkinos, I. (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.2018.00929 · Cited by 75 · see citing works
Semantic Image Segmentation by Scale-Adaptive Networks
Huang, Z., Wang, C., Wang, X., Liu, W., & Wang, J. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2941644 · Cited by 31 · see citing works
Self-citationAdobe Photoshop CS3 for Photographers
Evening, M. (2012) · Routledge eBooks · doi:10.4324/9780080550220 · Cited by 2 · see citing works
Chen, T., Wang, Z., Li, L., Li, D., Li, Z., Chang, X., Bian, P., Liang, G., Wang, Q., & Xie, T. (2025) · Schloss Dagstuhl – Leibniz-Zentrum für Informatik · doi:10.4230/lipics.ecoop.2025.6
Flagged first, then by citations
Cited by
Sorted by citations
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 21,793 · see citing works
Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
Wang, W., Xie, E., Li, X., Fan, D.-P., Song, K., Liang, D., Lu, T., Luo, P., & Shao, L. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00061 · Cited by 5,259 · see citing works
Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., Torr, P. H. S., et al. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00681 · Cited by 4,008 · see citing works
Transformers in Vision: A Survey
Khan, S., Naseer, M., Hayat, M., Zamir, S. W., Khan, F. S., & Shah, M. (2022) · ACM Computing Surveys · doi:10.1145/3505244 · Cited by 3,595 · see citing works
ViViT: A Video Vision Transformer
Arnab, A., Dehghani, M., Heigold, G., Sun, C., Lucic, M., & Schmid, C. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00676 · Cited by 2,767 · see citing works
Attention mechanisms in computer vision: A survey
Guo, M.-H., Xu, T.-X., Liu, J.-J., Liu, Z.-N., Jiang, P.-T., Mu, T.-J., Zhang, S.-H., Martin, R. R., Cheng, M.-M., & Hu, S.-M. (2022) · Computational Visual Media · doi:10.1007/s41095-022-0271-y · Cited by 2,566 · see citing works
CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows
Dong, X., Bao, J., Chen, D., Zhang, W., Yu, N., Yuan, L., Chen, D., & Guo, B. (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/cvpr52688.2022.01181 · Cited by 1,458 · see citing works
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J. M., & Luo, P. (2021) · arXiv · doi:10.48550/arxiv.2105.15203 · Cited by 5,090 · see citing works
Incorporating Convolution Designs into Visual Transformers
Yuan, K., Guo, S., Liu, Z., Zhou, A., Yu, F., & Wu, W. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00062 · Cited by 611 · see citing works
Exploring Cross-Image Pixel Contrast for Semantic Segmentation
Wang, W., Zhou, T., Yu, F., Dai, J., Konukoglu, E., & Gool, L. V. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00721 · Cited by 604 · 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
A Novel Transformer Based Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Images
Wang, L., Li, R., Duan, C., Zhang, C., Meng, X., & Fang, S. (2022) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2022.3143368 · Cited by 428 · see citing works
Context-aware Cross-level Fusion Network for Camouflaged Object Detection
Sun, Y., Chen, G., Zhou, T., Zhang, Y., & Liu, N. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/142 · Cited by 356 · 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
Self-citationMonoScene: Monocular 3D Semantic Scene Completion
Cao, A.-Q., & de Charette, R. (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/cvpr52688.2022.00396 · Cited by 301 · see citing works
Boundary-Preserving Mask R-CNN
Cheng, T., Wang, X., Huang, L., & Liu, W. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58568-6_39 · Cited by 288 · see citing works
Self-citationPLOP: Learning without Forgetting for Continual Semantic Segmentation
Douillard, A., Chen, Y., Dapogny, A., & Cord, M. (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/cvpr46437.2021.00403 · Cited by 274 · see citing works
CTNet: Context-Based Tandem Network for Semantic Segmentation
Li, Z., Sun, Y., Zhang, L., & Tang, J. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3132068 · Cited by 269 · see citing works
Building Extraction With Vision Transformer
Wang, L., Fang, S., Meng, X., & Li, R. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3186634 · Cited by 261 · see citing works
MFFENet: Multiscale Feature Fusion and Enhancement Network For RGB–Thermal Urban Road Scene Parsing
Zhou, W., Lin, X., Lei, J., Yu, L., & Hwang, J. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3086618 · Cited by 251 · see citing works
Image retrieval from remote sensing big data: A survey
Li, Y., Ma, J., & Zhang, Y. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.10.008 · Cited by 236 · see citing works
Transformer-Based Attention Networks for Continuous Pixel-Wise Prediction
Yang, G., Tang, H., Ding, M., Sebe, N., & Ricci, E. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01596 · Cited by 222 · see citing works
Antibody structure prediction using interpretable deep learning
Ruffolo, J. A., Sulam, J., & Gray, J. J. (2022) · Patterns · doi:10.1016/j.patter.2021.100406 · Cited by 216 · see citing works
CSWin-UNet: Transformer UNet with cross-shaped windows for medical image segmentation
Liu, X., Gao, P., Yu, T., Wang, F., & Yuan, R.-Y. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102634 · Cited by 216 · see citing works
Nwoye, C. I., Yu, T., Gonzalez, C. A., Seeliger, B., Mascagni, P., Mutter, D., Marescaux, J., & Padoy, N. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102433 · Cited by 216 · see citing works
Choi, S., Kim, J. T., & Choo, 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/cvpr42600.2020.00939 · Cited by 197 · see citing works
AlignSeg: Feature-Aligned Segmentation Networks
Huang, Z., Wei, Y., Wang, X., Shi, H., Liu, W., & Huang, T. S. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3062772 · Cited by 186 · 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
Mining Latent Classes for Few-shot Segmentation
Yang, L., Zhuo, W., Qi, L., Shi, Y., & Gao, Y. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00860 · Cited by 161 · see citing works
Wang, Z., Wang, J., Yang, K., Wang, L., Su, F., & Chen, X. (2021) · Computers & Geosciences · doi:10.1016/j.cageo.2021.104969 · Cited by 151 · see citing works
Content-Consistent Matching for Domain Adaptive Semantic Segmentation
Li, G., Kang, G., Liu, W., Wei, Y., & Yang, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58568-6_26 · Cited by 141 · see citing works
Self-citationInterlaced 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
Self-citationShuffle Transformer: Rethinking Spatial Shuffle for Vision TransformerPreprint
Huang, Z., Ben, Y., Luo, G., Cheng, P., Yu, G., & Fu, B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.03650 · Cited by 170 · see citing works
Self-citationCycleMLP: A MLP-like Architecture for Dense PredictionPreprint
Chen, S., Xie, E., Ge, C., Chen, R., Liang, D., & Luo, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.10224 · Cited by 128 · see citing works
CycleMLP: A MLP-Like Architecture for Dense Visual Predictions
Chen, S., Xie, E., Ge, C., Chen, R., Liang, D., & Luo, P. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3303397 · Cited by 276 · see citing works
AI-Based Protein Structure Prediction in Drug Discovery: Impacts and Challenges
Schauperl, M., & Denny, R. A. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00026 · Cited by 122 · see citing works
VSPW: A Large-scale Dataset for Video Scene Parsing in the Wild
Miao, J., Wei, Y., Wu, Y., Chen, L., Li, G., & Yang, Y. (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/cvpr46437.2021.00412 · Cited by 117 · see citing works
Self-citationSeMask: Semantically Masked Transformers for Semantic Segmentation
Jain, J., Singh, A. T., Orlov, N., Huang, Z., Li, J., Walton, S., & Shi, H. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00083 · Cited by 111 · see citing works
Self-citationLi, X., Xie, L., Wang, C., Miao, J., Shen, H., & Zhang, L. (2024) · GIScience & Remote Sensing · doi:10.1080/15481603.2024.2356355 · Cited by 114 · see citing works
CubeNet: X-shape connection for camouflaged object detection
Zhuge, M., Lu, X., Guo, Y., Cai, Z., & Chen, S. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108644 · Cited by 110 · see citing works
Inter-Image Communication for Weakly Supervised Localization
Zhang, X., Wei, Y., & Yang, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58529-7_17 · Cited by 112 · see citing works
Self-citationRelational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans.Preprint
Xie, W., Jacobs, C., Charbonnier, J., & van Ginneken, B. (2020) · PubMed · Cited by 102 · see citing works
TSA-Net: Tube Self-Attention Network for Action Quality Assessment
Wang, S., Yang, D., Zhai, P., Chen, C., & Zhang, L. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475438 · Cited by 99 · see citing works
Beyond the Prototype: Divide-and-conquer Proxies for Few-shot Segmentation
Lang, C., Tu, B., Cheng, G., & Han, J. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/143 · Cited by 96 · 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
Wang, G., Zhao, Y., Tang, C., Luo, C., & Zeng, W. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i2.20142 · Cited by 90 · see citing works
Wang, Z., Wu, Y., Wang, J., Xu, J., & Shao, W. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3139654 · Cited by 85 · see citing works
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 94 · see citing works
FsaNet: Frequency Self-Attention for Semantic Segmentation
Zhang, F., Panahi, A., & Gao, G. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3305090 · Cited by 78 · see citing works
A Comprehensive Review on Sensor-Based Electronic Nose for Food Quality and Safety
Sanislav, T., Mois, G. D., Zeadally, S., Folea, S. C., Radoni, T. C., & Al-Suhaimi, E. A. (2025) · Sensors · doi:10.3390/s25144437 · Cited by 77 · see citing works
Small Object Detection in Traffic Scenes Based on Attention Feature Fusion
Lian, J., Yin, Y., Li, L., Wang, Z., & Zhou, Y. (2021) · Sensors · doi:10.3390/s21093031 · Cited by 76 · see citing works
Large-scale Video Panoptic Segmentation in the Wild: A Benchmark
Miao, J., Wang, X., Wu, Y., Li, W., Zhang, X., Wei, Y., & Yang, Y. (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/cvpr52688.2022.02036 · Cited by 73 · see citing works
Self-citationLi, 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 · see citing works
Self-citationAn attention mechanism module with spatial perception and channel information interaction
Wang, Y., Wang, W., Li, Y., Jia, Y., Yu, X., Yu, L., & Ma, J. (2024) · Complex & Intelligent Systems · doi:10.1007/s40747-024-01445-9 · Cited by 74 · see citing works
Detection of Camellia oleifera fruit maturity in orchards based on modified lightweight YOLO
Zhu, X., Chen, F., Zheng, Y., Chen, C., & Peng, X. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.109471 · Cited by 70 · see citing works
UVid-Net: Enhanced Semantic Segmentation of UAV Aerial Videos by Embedding Temporal Information
Girisha, S., Verma, U., Pai, M. M. M., & Pai, R. M. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3069909 · Cited by 68 · see citing works
Stage-Aware Feature Alignment Network for Real-Time Semantic Segmentation of Street Scenes
Weng, X., Yan, Y., Chen, S., Xue, J., & Wang, H. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3121680 · Cited by 65 · see citing works
Meng, S., Zhang, C., Shi, Q., Chen, Z., Hu, W., & Lu, F. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3295932 · Cited by 64 · see citing works
AINet: Association Implantation for Superpixel Segmentation
Wang, Y., Wei, Y., Qian, X., Zhu, L., & Yang, Y. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00699 · Cited by 62 · see citing works
Self-citationContour-enhanced attention CNN for CT-based COVID-19 segmentation
Karthik, R., Menaka, R., Hariharan, M., & Won, D. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108538 · Cited by 62 · see citing works
A Thorough Benchmark and a New Model for Light Field Saliency Detection
Gao, W., Fan, S., Li, G., & Lin, W. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3235415 · Cited by 62 · see citing works
Semantic Segmentation With Light Field Imaging and Convolutional Neural Networks
Jia, C., Shi, F., Zhao, M. Y., Zhang, Y., Cheng, X., Wang, M., & Chen, S. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3115204 · Cited by 61 · see citing works
FeedFormer: Revisiting Transformer Decoder for Efficient Semantic Segmentation
Shim, J.-H., Yu, H., Kong, K., & Kang, S. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i2.25321 · Cited by 61 · see citing works
Xiong, H., Cai, W., & Liu, Q. (2021) · Infrared Physics & Technology · doi:10.1016/j.infrared.2020.103628 · Cited by 60 · see citing works
Zhang, Z., Wu, H., Zhao, H. Q., Shi, Y., Wang, J., Bai, H., & Sun, B. (2023) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-023-00585-9 · Cited by 60 · see citing works
AirSOD: A Lightweight Network for RGB-D Salient Object Detection
Zeng, Z., Liu, H., Chen, F., & Tan, X. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3295588 · Cited by 59 · see citing works
High-Resolution Deep Image Matting
Yu, H., Xu, N., Huang, Z., Zhou, Y., & Shi, H. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i4.16432 · Cited by 57 · see citing works
Self-citationAerial Person Detection for Search an d Rescue: Survey and Benchmarks
Zhang, X., Feng, Y., Wang, N., Lü, G., & Mei, S. (2025) · Journal of Remote Sensing · doi:10.34133/remotesensing.0474 · Cited by 57 · see citing works
MANet: Multi-Scale Aware-Relation Network for Semantic Segmentation in Aerial Scenes
He, P., Jiao, L., Shang, R., Wang, S., Liu, X., Quan, D., Yang, K., & Zhao, D. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3179379 · Cited by 56 · see citing works
See more than once: Kernel-sharing atrous convolution for semantic segmentation
Huang, Y., Wang, Q., Jia, W., Lu, Y., Li, Y., & He, X. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.02.091 · Cited by 54 · see citing works
VTON-SCFA: A Virtual Try-On Network Based on the Semantic Constraints and Flow Alignment
Du, C., Yu, F., Jiang, M., Hua, A., Xiong, W., Peng, T., & Hu, X. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3152367 · Cited by 53 · see citing works
Discriminative Region-based Multi-Label Zero-Shot Learning
Narayan, S., Gupta, A., Khan, S. H., Khan, F. S., Shao, L., & Shah, M. A. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00861 · Cited by 52 · see citing works
Cerón, J. C. Á., Ochoa‐Ruiz, G., Chang, L., & Ali, S. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102569 · Cited by 52 · see citing works
He, Q., Sun, X., Diao, W., Yan, Z., Yin, D., & Fu, K. (2022) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.08.010 · Cited by 51 · see citing works
RetractedDai, Y., Pan, P., Qian, Y., Li, Y., Li, X., Yang, J., & Wang, H. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3458896 · Cited by 80 · see citing works
MCS-GAN: A Different Understanding for Generalization of Deep Forgery Detection
Xiao, S., Lan, G., Yang, J., Lu, W. B., Meng, Q., & Gao, X. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3279993 · Cited by 51 · see citing works
Rethinking the U-Shape Structure for Salient Object Detection
Liu, J., Liu, Z., Peng, P., & Cheng, M. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3122093 · Cited by 50 · see citing works
Strip Attention for Image Restoration
Cui, Y., Tao, Y., Jing, L., & Knoll, A. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/72 · Cited by 49 · see citing works
Balancing Logit Variation for Long-Tailed Semantic Segmentation
Wang, Y., Fei, J., Wang, H., Li, W., Bao, T., Wu, L., Zhao, R., & Shen, Y. (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/cvpr52729.2023.01874 · Cited by 49 · see citing works
A Lightweight Network for Accurate Coronary Artery Segmentation Using X-Ray Angiograms
Tao, X., Dang, H., Zhou, X., Xu, X., & Xiong, D. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.892418 · Cited by 47 · see citing works
Contextual Spatial-Channel Attention Network for Remote Sensing Scene Classification
Hou, Y., Yang, K., Dang, L., & Liu, Y. (2023) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2023.3304645 · Cited by 47 · see citing works
SSCNet: A Spectrum-Space Collaborative Network for Semantic Segmentation of Remote Sensing Images
Li, X., Xu, F., Yong, X., Chen, D., Xia, R., Ye, B., Gao, H., Chen, Z., & Lyu, X. (2023) · Remote Sensing · doi:10.3390/rs15235610 · Cited by 45 · see citing works
An integration–competition network for bridge crack segmentation under complex scenes
Sun, L., Yang, Y., Zhou, G., Chen, A., Zhang, Y., Cai, W., & Li, L. (2023) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.13113 · Cited by 44 · see citing works
PrGCN: Probability prediction with graph convolutional network for person re-identification
Liu, H., Xiao, Z., Fan, B., Zeng, H., Zhang, Y., & Jiang, G. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.10.019 · Cited by 43 · see citing works
Adaptive multi-scale dual attention network for semantic segmentation
Wang, W., Wang, S., Li, Y., & Jin, Y. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.06.068 · Cited by 41 · see citing works
Zhuang, M., Zhong, X., Gu, D., Feng, L., Zhong, X., & Hu, H. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.07.019 · Cited by 40 · see citing works
Lee, J., Kim, E., Mok, J., & Yoon, S. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3166916 · Cited by 45 · see citing works
Adaptive Short-Temporal Induced Aware Fusion Network for Predicting Attention Regions Like a Driver
Li, Q., Liu, C., Chang, F., Li, S., Liu, H., & Liu, Z. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3165619 · Cited by 40 · see citing works
Huang, Y., Kang, D., Jia, W., Liu, L., & He, X. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i1.19985 · Cited by 45 · see citing works
Wang, Z., Yu, Y., Liu, J., Zhang, Q., Guo, X., Yang, Y., & Shi, Y. H. (2024) · Food Chemistry · doi:10.1016/j.foodchem.2024.138915 · Cited by 39 · see citing works
Panoptic Segmentation of Individual Pigs for Posture Recognition
Brünger, J., Gentz, M., Traulsen, I., & Koch, R. (2020) · Sensors · doi:10.3390/s20133710 · Cited by 40 · see citing works
Deep graph cut network for weakly-supervised semantic segmentation
Feng, J., Wang, X., & Liu, W. (2021) · Science China Information Sciences · doi:10.1007/s11432-020-3065-4 · Cited by 38 · see citing works
Self-citationGao, H., Wang, Z., & Ji, S. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403065 · Cited by 36 · see citing works
Spatial Frequency Modulation for Semantic Segmentation
Chen, L., Fu, Y., Gu, L., Zheng, D., & Dai, J. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3592621 · Cited by 39 · see citing works
SANet: A Self-Attention Network for Agricultural Hyperspectral Image Classification
Zhang, B., Chen, Y., Li, Z., Xiong, S., & Lu, X. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3341473 · Cited by 33 · see citing works
Kim, D. N., McNaughton, A. D., & Kumar, N. (2024) · Bioengineering · doi:10.3390/bioengineering11020185 · Cited by 34 · see citing works
Hu, W., Wang, T., Wang, Y., Chen, Z., & Huang, G. (2021) · The Visual Computer · doi:10.1007/s00371-021-02210-6 · Cited by 33 · see citing works
Multi-Relation Attention Network for Image Patch Matching
Quan, D., Wang, S., Li, Y., Yang, B., Huyan, N., Chanussot, J., Hou, B., & Jiao, L. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3101414 · Cited by 33 · see citing works
SAA-Net: U-shaped network with Scale-Axis-Attention for liver tumor segmentation
Zhang, C., Lu, J., Hua, Q., Li, C.-G., & Wang, P.-W. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103460 · Cited by 33 · see citing works
DeepLab2: A TensorFlow Library for Deep LabelingPreprint
Weber, M., Wang, H., Qiao, S., Xie, J., Collins, M. D., Zhu, Y., Yuan, L., Kim, D., Yu, Q., Cremers, D., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.09748 · Cited by 47 · see citing works
- Authors
- Huang, Z., Wang, X., Wei, Y., Huang, L., Shi, H., Liu, W., & Huang, T. S.
- Affiliations
- Huazhong University of Science and Technology (CN)
- University of Technology Sydney (AU)
- Horizon Robotics (China) (CN)
- University of Illinois Urbana-Champaign (US)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2023-06-01
- Volume
- 45
- Issue
- 6
- Pages
- 6896-6908
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W3040304705
- Cited by
- 524
- References
- 66
- Citations by year
- 2026: 35 · 2025: 102 · 2024: 110 · 2023: 83 · 2022: 69 · 2021: 106 · 2020: 17 · 2019: 2
- FWCI
- 26.73
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (61733007, 61876212)
- ARC DECRA (DE190101315, ARC DP200100938)
- HUST-Horizon Computer Vision Research Center
- Center for Cognitive Computing Systems Research
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- criss-cross attention, semantic segmentation, contextual information, recurrent attention, GPU memory efficiency, computational efficiency, non-local block, instance segmentation, video segmentation, human parsing
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
