The DOI matches its Crossref record; 94 of the 97 references it cites were found in a registry or an index.
Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene 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 check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“It also performs better than MAE on ImageNet-C, but falls behind DINO [12] and MoCo-v3 [17].”
From MultiMAE: Multi-modal Multi-task Masked Autoencoders (2022) · cites this paper as [12]
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
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
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
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
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
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
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
Self-citationXception: Deep Learning with Depthwise Separable Convolutions
Chollet, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.195 · Cited by 20,380
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
Self-citation
Research works printed but not linked: 4
- Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015) · arXiv
- Lee, D.H.: Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks. In: ICML Workshop (2013)
- Liu, W., Rabinovich, A., Berg, A.C.: Parsenet: looking wider to see better. arXiv:1506.04579 (2015) · arXiv
- Qi, H., et al.: Deformable convolutional networks - COCO detection and segmentation challenge 2017 entry. In: ICCV COCO Challenge Workshop (2017)
Cited by
Sorted by citations
Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision
Chen, X., Yuan, Y., Zeng, G., & Wang, J. (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/cvpr46437.2021.00264 · Cited by 1,389
DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution
Qiao, S., Chen, L.-C., & Yuille, A. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01008 · Cited by 1,245
Self-citationMaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers
Wang, H., Zhu, Y., Adam, H., Yuille, A., & Chen, L.-C. (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/cvpr46437.2021.00542 · Cited by 567
Self-citationRethinking Pre-training and Self-trainingPreprint
Zoph, B., Ghiasi, G., Lin, T.-Y., Cui, Y., Liu, H., Cubuk, E. D., & Le, Q. V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.06882 · Cited by 579
Self-citationExtract Free Dense Labels from CLIP
Zhou, C., Loy, C. C., & Dai, B. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-19815-1_40 · Cited by 513
AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem?
Ma, J., Zhang, Y., Gu, S., Zhu, C. G., Ge, C., Zhang, Y., An, X., Wang, C., Wang, Q., Liu, X., et al. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3100536 · Cited by 429
DACS: Domain Adaptation via Cross-domain Mixed Sampling
Tranheden, W., Olsson, V., Pinto, J., & Svensson, L. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00142 · Cited by 440
ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning
Olsson, V., Tranheden, W., Pinto, J., & Svensson, L. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00141 · Cited by 444
A Review on Machine Learning Styles in Computer Vision—Techniques and Future Directions
Mahadevkar, S. V., Khemani, B., Patil, S., Kotecha, K. V., Vora, D. R., Abraham, A., & Gabralla, L. A. (2022) · IEEE Access · doi:10.1109/access.2022.3209825 · Cited by 304
MultiMAE: Multi-modal Multi-task Masked Autoencoders
Bachmann, R., Mizrahi, D., Atanov, A., & Zamir, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19836-6_20 · Cited by 268
PseudoSeg: Designing Pseudo Labels for Semantic SegmentationPreprint
Zou, Y., Zhang, Z., Zhang, H., Li, C., Bian, X., Huang, J., & Pfister, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.09713 · Cited by 268
A Survey on Deep Learning Technique for Video Segmentation
Zhou, T., Porikli, F., Crandall, D., Gool, L. V., & Wang, W. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3225573 · Cited by 291
Self-supervised Augmentation Consistency for Adapting Semantic Segmentation
Araslanov, N., & Roth, S. (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/cvpr46437.2021.01513 · Cited by 255
Pixel Contrastive-Consistent Semi-Supervised Semantic Segmentation
Zhong, Y., Yuan, B., Wu, H. Q., Yuan, Z., Peng, J., & Wang, Y.-X. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00718 · Cited by 211
Contrastive Learning for Label Efficient Semantic Segmentation
Zhao, X., Vemulapalli, R., Mansfield, P. A., Gong, B., Green, B., Shapira, L., & Wu, Y. N. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01045 · Cited by 192
InverseForm: A Loss Function for Structured Boundary-Aware Segmentation
Borse, S., Wang, Y., Zhang, Y., & Porikli, F. (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/cvpr46437.2021.00584 · Cited by 169
Pointly-Supervised Instance Segmentation
Cheng, B., Parkhi, O., & Kirillov, A. (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/cvpr52688.2022.00264 · Cited by 165
Self-citationViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation
Qiao, S., Zhu, Y., Adam, H., Yuille, A., & Chen, L.-C. (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/cvpr46437.2021.00399 · Cited by 156
Self-citationYu, Q., Wang, H., Qiao, S., Collins, M. D., Zhu, Y., Adam, H., Yuille, A., & Chen, L.-C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19818-2_17 · Cited by 123
Self-citationDenoising Pretraining for Semantic Segmentation
Brempong, E. A., Kornblith, S., Chen, T., Parmar, N., Minderer, M., & Norouzi, M. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00462 · Cited by 120
Ghorbanali, A., Sohrabi, M. K., & Yaghmaee, F. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.102929 · Cited by 108
Milestones in Autonomous Driving and Intelligent Vehicles—Part II: Perception and Planning
Chen, L. F., Teng, S., Li, B., Na, X., Li, Y., Li, Z., Wang, J., Cao, D., Zheng, N., & Wang, F. (2023) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2023.3283021 · Cited by 112
Self-citationJozdani, S. E., Chen, D., Pouliot, D., & Johnson, B. A. (2022) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2022.102734 · Cited by 106
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
Deep-learning-based visual data analytics for smart construction management
Pal, A., & Hsieh, S. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103892 · Cited by 103
Robust Semantic Segmentation With Multi-Teacher Knowledge Distillation
Amirkhani, A., Khosravian, A., Masih‐Tehrani, M., & Kashiani, H. (2021) · IEEE Access · doi:10.1109/access.2021.3107841 · Cited by 87
Zhou, Y., Xu, H., Zhang, W., Gao, B., & Heng, P. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00695 · Cited by 80
Multi-Task Self-Training for Learning General Representations
Ghiasi, G., Zoph, B., Cubuk, E. D., Le, Q. V., & Lin, T.-Y. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00873 · Cited by 84
Self-citationWang, Z., Li, T., Zheng, J.-Q., & Huang, B. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25082-8_28 · Cited by 64
Semi-Supervised Semantic Segmentation with Cross Pseudo SupervisionPreprint
Chen, X., Yuan, Y., Zeng, G., & Wang, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.01226 · Cited by 62
A Closer Look at Self-training for Zero-Label Semantic Segmentation
Pastore, G., Cermelli, F., Xian, Y., Mancini, M., Akata, Z., & Barbara Caputo, B. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00303 · Cited by 61
Is Context-Aware CNN Ready for the Surroundings? Panoramic Semantic Segmentation in the Wild
Yang, K., Hu, X., & Stiefelhagen, R. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3048682 · Cited by 57
DSC-PoseNet: Learning 6DoF Object Pose Estimation via Dual-scale Consistency
Yang, Z., Yu, X., & 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/cvpr46437.2021.00390 · Cited by 52
STEP: Segmenting and Tracking Every PixelPreprint
Weber, M., Xie, J., Collins, M. D., Zhu, Y., Voigtlaender, P., Adam, H., Green, B., Geiger, A., Leibe, B., Cremers, D., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.11859 · Cited by 59
Self-citationA comprehensive survey on deep learning-based approaches for multimodal sentiment analysis
Ghorbanali, A., & Sohrabi, M. K. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10555-8 · Cited by 54
MGNet: Monocular Geometric Scene Understanding for Autonomous Driving
Schön, M., Buchholz, M., & Dietmayer, K. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01551 · Cited by 54
Scalable Scene Flow From Point Clouds in the Real World
Jund, P., Sweeney, C., Abdo, N., Chen, Z., & Shlens, J. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3139542 · Cited by 48
Self-citationConservative-Progressive Collaborative Learning for Semi-Supervised Semantic Segmentation
Fan, S., Zhu, F., Feng, Z., Lv, Y., Song, M., & Wang, F. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3242819 · Cited by 45
Domain Adaptive Video Segmentation via Temporal Consistency Regularization
Guan, D., Huang, J., Xiao, A., & Lu, S. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00795 · Cited by 45
Chen, J., Sun, B., Wang, L., Fang, B., Chang, Y., Li, Y., Zhang, J., Lyu, X., & Chen, G. (2022) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2022.102881 · Cited by 46
Pseudo-Label Refinement Using Superpixels for Semi-Supervised Brain Tumour Segmentation
Thompson, B. H., Caterina, G. D., & Voisey, J. P. (2022) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi52829.2022.9761681 · Cited by 43
Zhang, Y., Borse, S., Cai, H., & Porikli, F. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00269 · Cited by 36
Exploring Event-Driven Dynamic Context for Accident Scene Segmentation
Zhang, J., Yang, K., & Stiefelhagen, R. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3134828 · Cited by 38
PolyphonicFormer: Unified Query Learning for Depth-Aware Video Panoptic Segmentation
Yuan, H., Li, X., Yang, Y., Cheng, G., Zhang, J., Tong, Y., Zhang, L., & Tao, D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19812-0_34 · Cited by 37
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 56
Self-citationKedra, J., Davergne, T., Braithwaite, B., Servy, H., & Gossec, L. (2021) · Expert Review of Clinical Immunology · doi:10.1080/1744666x.2022.2017773 · Cited by 33
Miao, W., Xu, Z., Geng, J., & Jiang, W. R. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3330490 · Cited by 31
Semi-supervised Cardiac Image Segmentation via Label Propagation and Style Transfer
Zhang, Y., Yang, J., Hou, F., Liu, Y., Wang, Y., Tian, J., Zhang, Y., Zhang, Y., & He, Z. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-68107-4_22 · Cited by 31
Fuzzy Positive Learning for Semi-Supervised Semantic Segmentation
Qiao, P., Wei, Z., Wang, Y., Wang, Z., Song, G., Xu, F., Ji, X., Liu, C., & Chen, J. (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/cvpr52729.2023.01484 · Cited by 33
MaX-DeepLab: End-to-End Panoptic Segmentation with Mask TransformersPreprint
Wang, H., Zhu, Y., Adam, H., Yuille, A., & Chen, L.-C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.00759 · Cited by 32
Self-citationSemi-supervised Semantic Segmentation with Mutual Knowledge Distillation
Yuan, J., Ge, J., Wang, Z., & Liu, Y. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3611906 · Cited by 30
Nambiar, K. G., Morgenshtern, V. I., Hochreuther, P., Seehaus, T., & Braun, M. (2022) · Remote Sensing · doi:10.3390/rs14081825 · Cited by 29
PanopticDepth: A Unified Framework for Depth-aware Panoptic Segmentation
Gao, N., He, F., Jia, J., Shan, Y., Zhang, H., Zhao, X., & Huang, K. (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/cvpr52688.2022.00168 · Cited by 32
Yang, Y., Tang, X., Ma, J., Zhang, X., Pei, S., & Jiao, L. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3370236 · Cited by 31
Semi-supervised body parsing and pose estimation for enhancing infant general movement assessment
Ni, H., Xue, Y., Ma, L., Zhang, Q., Li, X., & Huang, X. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102654 · Cited by 31
Video Shadow Detection via Spatio-Temporal Interpolation Consistency Training
Lu, X. B., Cao, Y., Liu, S., Long, C., Chen, Z., Zhou, X., Yang, Y., & Xiao, C. (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/cvpr52688.2022.00312 · Cited by 27
Scaling Wide Residual Networks for Panoptic SegmentationPreprint
Chen, L.-C., Wang, H., & Qiao, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.11675 · Cited by 42
Self-citationChun, D., Lee, S., & Kim, H. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3348662 · Cited by 25
ComplexMix: Semi-Supervised Semantic Segmentation Via Mask-Based Data Augmentation
Chen, Y., Ouyang, X., Zhu, K., & Gady Agam, B. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506602 · Cited by 25
Multimodal Knowledge Expansion
Xue, Z., Ren, S., Gao, Z., & Zhao, H. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00089 · Cited by 25
JUPITER – ROS based Vehicle Platform for Autonomous Driving Research
Haselberger, J., Pelzer, M., Schick, B., & Müller, S. (2022) · IEEE International Symposium on Robotic and Sensors Environments · doi:10.1109/rose56499.2022.9977434 · Cited by 25
Guided Filter Network for Semantic Image Segmentation
Zhang, X., Zhao, W., Zhang, W., Peng, J., & Jianping Fan, B. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3160399 · Cited by 25
Instance-wise Occlusion and Depth Orders in Natural Scenes
Lee, H.-M., & Park, J. (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/cvpr52688.2022.02053 · Cited by 26
Slot-VPS: Object-centric Representation Learning for Video Panoptic Segmentation
Zhou, Y., Zhang, H., Lee, H., Sun, S., Li, P., Zhu, Y., Yoo, B., Qi, X., & Han, J. (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/cvpr52688.2022.00310 · Cited by 22
Ghelmani, A., & Hammad, A. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.105001 · Cited by 27
Video Mask Transfiner for High-Quality Video Instance Segmentation
Ke, L., Ding, H., Danelljan, M., Tai, Y., Tang, C., & Yu, F. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19815-1_42 · Cited by 22
Pseudo-labeling for Scalable 3D Object DetectionPreprint
Caine, B., Roelofs, R., Vasudevan, V., Ngiam, J., Chai, Y., Chen, Z., & Shlens, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.02093 · Cited by 32
Self-citationDenseCLIP: Extract Free Dense Labels from CLIPPreprint
Zhou, C., Loy, C. C., & Dai, B. (2021) · arXiv (Cornell University) · Cited by 21
Urban Scene Semantic Segmentation with Low-Cost Coarse Annotation
Das, A., Xian, Y., He, Y., Akata, Z., & Schiele, B. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00592 · Cited by 20
Locating Noise is Halfway Denoising for Semi-Supervised Segmentation
Fang, Y., Zhu, F., Cheng, B., Liu, L., Zhao, Y., & Wei, Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01523 · Cited by 20
Self-citationFew-Shot Panoptic Segmentation With Foundation Models
Käppeler, M., Petek, K., Vödisch, N., Burgard, W., & Valada, A. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611624 · Cited by 24
A Survey on Deep Learning Methods for Semantic Image Segmentation in Real-TimePreprint
Takos, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.12942 · Cited by 23
Hu, S., Liu, C.-H., Dutta, J., Chang, M., Lyu, S., & Ramakrishnan, N. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00485 · Cited by 19
To Adapt or Not to Adapt? Real-Time Adaptation for Semantic Segmentation
Colomer, M. B., Dovesi, P. L., Panagiotakopoulos, T., Carvalho, J. F., Härenstam-Nielsen, L., Azizpour, H., Kjellström, H., Cremers, D., & Poggi, M. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01517 · Cited by 19
U-LanD: Uncertainty-Driven Video Landmark Detection
Jafari, M. H., Luong, C., Tsang, M., Gu, A. N., Woudenberg, N. V., Rohling, R., Tsang, T., & Abolmaesumi, P. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3123547 · Cited by 20
Online Domain Adaptation for Semantic Segmentation in Ever-Changing Conditions
Panagiotakopoulos, T., Dovesi, P. L., Härenstam-Nielsen, L., & Poggi, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19830-4_8 · Cited by 17
Semi-Supervised Video Semantic Segmentation with Inter-Frame Feature Reconstruction
Zhuang, J., Wang, Z., & Gao, 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/cvpr52688.2022.00326 · Cited by 17
Video-kMaX: A Simple Unified Approach for Online and Near-Online Video Panoptic Segmentation
Shin, I., Kim, D., Yu, Q., Xie, J., Kim, H.-S., Green, B., Kweon, I. S., Yoon, K., & Chen, L.-C. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00030 · Cited by 18
Self-citationVideo Instance Segmentation by Instance Flow Assembly
Li, X., Wang, J., Li, X., & Lu, Y. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3222643 · Cited by 16
Domain Adaptive Video Segmentation via Temporal Pseudo Supervision
Xing, Y., Guan, D., Huang, J., & Lu, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20056-4_36 · Cited by 16
Warp-Refine Propagation: Semi-Supervised Auto-labeling via Cycle-consistency
Ganeshan, A., Vallet, A., Kudo, Y., Maeda, S., Kerola, T., Ambruş, R., Park, D., & Gaidon, A. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01521 · Cited by 15
Zhang, M., Zheng, Y., & Lu, F. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3130302 · Cited by 15
Deep Tri-Training for Semi-Supervised Image Segmentation
An, S., Zhu, H., Zhang, J., Ye, J., Wang, S., Yin, J., & Zhang, H. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3185768 · Cited by 15
Triple-View Feature Learning for Medical Image Segmentation
Wang, Z., & Voiculescu, I. D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16876-5_5 · Cited by 15
MLC: Multi-level consistency learning for semi-supervised left atrium segmentation
Shi, Z., Jiang, M., Li, Y., Wei, B., Wang, Z., Wu, Y., Tan, T., & Yang, G. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122903 · Cited by 15
Jozdani, S. E., Chen, D., Chen, W., Leblanc, S. G., Prévost, C., Lovitt, J., He, L., & Johnson, B. A. (2021) · Remote Sensing · doi:10.3390/rs13142658 · Cited by 15
Robust Mutual Learning for Semi-supervised Semantic SegmentationPreprint
Zhang, P., Zhang, B., Zhang, T., Chen, D., & Wen, F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.00609 · Cited by 17
Semi-supervised Dual-Domain Adaptation for Semantic Segmentation
Chen, Y., Ouyang, X., Zhu, K., & Gady Agam, B. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956524 · Cited by 14
ReFs: A hybrid pre-training paradigm for 3D medical image segmentation
Xie, Y., Zhang, J., Liu, L., Wang, H., Ye, Y., Verjans, J. W. H., & Xia, Y. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.103023 · Cited by 14
Liu, J., Jiang, Z., Cao, T., Chen, Z., Zhang, C., & Gui, W. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3230451 · Cited by 13
SegMatch: semi-supervised surgical instrument segmentation
Wei, M., Budd, C., García-Peraza-Herrera, L. C., Dorent, R., Shi, M., & Vercauteren, T. K. (2025) · Scientific Reports · doi:10.1038/s41598-025-94568-z · Cited by 13
Joint Global and Dynamic Pseudo Labeling for Semi-Supervised Point Cloud Sequence Segmentation
Liu, J., Chen, Y., Ni, B., & Yu, Z. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3253210 · Cited by 13
Towards Deeply Unified Depth-aware Panoptic Segmentation with Bi-directional Guidance Learning
He, J., Wang, Y., Wang, L., Lu, H., Luo, B., He, J.-Y., Lan, J.-P., Geng, Y., & Xie, X. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00380 · Cited by 12
Weakly-supervised Semantic Segmentation in Cityscape via Hyperspectral Image
Huang, Y., Shen, Q., Fu, Y., & You, S. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00131 · Cited by 12
Li, C., Li, Y., Liu, R., Wang, G., Lv, J., Jin, Y., Si, W., & Heng, P. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3342222 · Cited by 11
Owen, D., Grammatikopoulou, M., Luengo, I., & Stoyanov, D. V. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87202-1_31 · Cited by 11
Park, S., Kim, J., & Heo, Y. S. (2022) · Sensors · doi:10.3390/s22072623 · Cited by 12
DST-Det: Open-Vocabulary Object Detection via Dynamic Self-Training
Xu, S., Li, X., Wu, S., Zhang, W., Tong, Y., & Loy, C. C. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3520734 · Cited by 26
Learning to Better Segment Objects from Unseen Classes with Unlabeled Videos
Du, Y., Xiao, Y., & Lepetit, V. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00336 · Cited by 10
Metadata
- Authors
- Chen, L.-C., Lopes, R. G., Cheng, B., Collins, M. D., Cubuk, E. D., Zoph, B., Adam, H., & Shlens, J.
- Affiliations
- Google (United States) (US)
- University of Illinois Urbana-Champaign (US)
- Corresponding author
- Chen, Liang-Chieh
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 695-714
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3110002552
- Cited by
- 165
- References
- 97
- Citations by year
- 2026: 2 · 2025: 9 · 2024: 19 · 2023: 34 · 2022: 44 · 2021: 50 · 2020: 7
- FWCI
- 10.41
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- 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 › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- semi-supervised learning, urban scene segmentation, video sequences, pseudo-labeling, instance segmentation, panoptic segmentation, semantic segmentation, cityscape, label propagation, unlabeled data
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
