Mix-and-Match Tuning for Self-Supervised Semantic Segmentation
Open accessAbstract
Deep convolutional networks for semantic image segmentation typically require large-scale labeled data, e.g., ImageNet and MS COCO, for network pre-training. To reduce annotation efforts, self-supervised semantic segmentation is recently proposed to pre-train a network without any human-provided labels. The key of this new form of learning is to design a proxy task (e.g., image colorization), from which a discriminative loss can be formulated on unlabeled data. Many proxy tasks, however, lack the critical supervision signals that could induce discriminative representation for the target image segmentation task. Thus self-supervision’s performance is still far from that of supervised pre-training. In this study, we overcome this limitation by incorporating a "mix-and-match" (M&M) tuning stage in the self-supervision pipeline. The proposed approach is readily pluggable to many self-supervision methods and does not use more annotated samples than the original process. Yet, it is capable of boosting the performance of target image segmentation task to surpass fully-supervised pre-trained counterpart. The improvement is made possible by better harnessing the limited pixel-wise annotations in the target dataset. Specifically, we first introduce the "mix" stage, which sparsely samples and mixes patches from the target set to reflect rich and diverse local patch statistics of target images. A ‘match’ stage then forms a class-wise connected graph, which can be used to derive a strong triplet-based discriminative loss for finetuning the network. Our paradigm follows the standard practice in existing self-supervised studies and no extra data or label is required. With the proposed M&M approach, for the first time, a self-supervision method can achieve comparable or even better performance compared to its ImageNet pretrained counterpart on both PASCAL VOC2012 dataset and CityScapes dataset.
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
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?
Existing USS methods focus on small datasets and a few easy categories, so the advantages of unsupervised learning of rich representations from large-scale data are not explored.
“1) Existing methods focus on small datasets [28], [29], [30], [31] and a few (e.g., 20+) easy categories [28] (e.g., sky and ground). Because of the insufficient data, the advantages of unsupervised learning of rich representations from large-scale data are not explored.”
From Large-Scale Unsupervised Semantic Segmentation (2023) · cites this paper as [29]
The Introduction (p. 1) says pixel-wise annotations for fine-tuning are available in only thousands of images, which supports a narrower point about limited labeled data. But the Experiments section (p. 5) says the self-supervised methods used 1.3 million unlabeled ImageNet images for training, contrary to the claim that large-scale data was not explored. The Introduction (p. 1) attributes their performance gap mainly to a mismatch between the proxy and target tasks, not insufficient data. (LLM, conf=0.87)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredExisting unsupervised segmentation methods mainly deal with small data with limited number of categories and small diversity, and are not suitable for the LUSS task.
“Due to insufficient data, a few unsupervised segmentation methods [28], [29], [30], [31] mainly deal with small data with limited number of categories and small diversity, thus are not suitable for the LUSS task.”
From Large-Scale Unsupervised Semantic Segmentation (2023) · cites this paper as [29]
The Introduction (p. 1) discusses limited pixel-wise annotations for fine-tuning, and the Experiments (p. 5) report results on PASCAL VOC 2012. Neither those passages nor the passages found by searching for dataset size, category limits, diversity, and LUSS say that unsupervised segmentation methods are confined to small, low-diversity datasets or are unsuitable for LUSS. (LLM, conf=0.72)
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.
Cites
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
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
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · 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
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 · 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
(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599 · 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
FaceNet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., & Philbin, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298682 · Cited by 15,267 · 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
Vision meets robotics: The KITTI dataset
Geiger, A., Lenz, P., Stiller, C., & Urtasun, R. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913491297 · Cited by 9,913 · see citing works
Context Encoders: Feature Learning by Inpainting
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., & Efros, A. A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.278 · Cited by 5,691 · see citing works
Zhang, R., Isola, P., & Efros, A. A. (2016) · European Conference on Computer Vision · doi:10.1007/978-3-319-46487-9_40 · Cited by 3,803 · see citing works
Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles
Noroozi, M., & Favaro, P. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46466-4_5 · Cited by 2,775 · see citing works
Unsupervised Visual Representation Learning by Context Prediction
Doersch, C., Gupta, A., & Efros, A. A. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.167 · Cited by 2,629 · see citing works
A Neural Algorithm of Artistic Style
Gatys, L., Ecker, A., & Bethge, M. (2016) · Journal of Vision · doi:10.1167/16.12.326 · Cited by 2,612 · see citing works
Fully Convolutional Networks for Semantic SegmentationPreprint
Long, J., Shelhamer, E., & Darrell, T. J. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1411.4038 · Cited by 2,490 · see citing works
Adversarial Feature LearningPreprint
Donahue, J., Krähenbühl, P., & Darrell, T. (2016) · International Conference on Learning Representations · doi:10.48550/arxiv.1605.09782 · Cited by 1,982 · see citing works
Hypercolumns for object segmentation and fine-grained localization
Hariharan, B., Arbelaez, P., Girshick, R., & Malik, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298642 · Cited by 1,511 · see citing works
ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation
Lin, D., Dai, J., Jia, J., He, K., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.344 · Cited by 1,129 · see citing works
BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation
Dai, J., He, K., & Sun, J. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.191 · Cited by 1,118 · see citing works
Weakly-and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation
Papandreou, G., Chen, L.-C., Murphy, K. P., & Yuille, A. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.203 · Cited by 1,041 · see citing works
Unsupervised Learning of Visual Representations Using Videos
Wang, X., & Gupta, A. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.320 · Cited by 1,005 · see citing works
What’s the Point: Semantic Segmentation with Point Supervision
Bearman, A., Russakovsky, O., Ferrari, V., & Fei-Fei, L. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46478-7_34 · Cited by 986 · see citing works
Learning Representations for Automatic Colorization
Larsson, G., Maire, M., & Shakhnarovich, G. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_35 · Cited by 946 · see citing works
Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks
Dosovitskiy, A., Fischer, P., Springenberg, J. T., Riedmiller, M., & Brox, T. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2496141 · Cited by 762 · see citing works
Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction
Zhang, R., Isola, P., & Efros, A. A. (2016) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2017.76 · Cited by 724 · see citing works
What makes Paris look like Paris?
Doersch, C., Singh, S., Gupta, A., Šivic, J., & Efros, A. A. (2012) · ACM Transactions on Graphics · doi:10.1145/2185520.2185597 · Cited by 693 · 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
Self-citationLi, L.-J., Socher, R., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206718 · Cited by 568 · see citing works
Learning Features by Watching Objects Move
Pathak, D., Girshick, R. B., Dollár, P., Darrell, T., & Hariharan, B. (2016) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2017.638 · Cited by 549 · see citing works
Colorization as a Proxy Task for Visual Understanding
Larsson, G., Maire, M., & Shakhnarovich, G. (2017) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2017.96 · Cited by 527 · see citing works
Unsupervised Discovery of Mid-Level Discriminative Patches
Singh, S., Gupta, A., & Efros, A. A. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33709-3_6 · Cited by 517 · see citing works
Not All Pixels Are Equal: Difficulty-Aware Semantic Segmentation via Deep Layer Cascade
Li, X., Liu, Z., Luo, P., Loy, C. C., & Tang, 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.2017.684 · Cited by 268 · see citing works
Self-citationUnsupervised Visual Representation Learning by Context PredictionPreprint
Doersch, C., Gupta, A., & Efros, A. A. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.05192 · Cited by 245 · see citing works
Data-dependent Initializations of Convolutional Neural NetworksPreprint
Krähenbühl, P., Doersch, C., Donahue, J., & Darrell, T. (2015) · International Conference on Learning Representations · doi:10.48550/arxiv.1511.06856 · Cited by 211 · see citing works
Semantic Image Segmentation via Deep Parsing NetworkPreprint
Liu, Z., Li, X., Luo, P., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1509.02634 · Cited by 179 · see citing works
Self-citationDeep Learning Markov Random Field for Semantic Segmentation
Liu, Z., Li, X., Luo, P., Loy, C. C., & Tang, X. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2737535 · Cited by 178 · see citing works
Self-citationWeakly Supervised Semantic Segmentation Using Web-Crawled Videos
Hong, S., Yeo, D., Kwak, S., Lee, H., & Han, B. (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.2017.239 · Cited by 158 · see citing works
Harvesting Mid-level Visual Concepts from Large-Scale Internet Images
Li, Q., Wu, J., & Tu, Z. (2013) · 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.2013.115 · Cited by 141 · see citing works
Video Object Segmentation with Re-identificationPreprint
Li, X., Qi, Y., Wang, Z., Chen, K., Liu, Z., Shi, J., Luo, P., Tang, X., & Loy, C. C. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.00197 · Cited by 70 · see citing works
Self-citationWhat's the Point: Semantic Segmentation with Point SupervisionPreprint
Bearman, A., Russakovsky, O., Ferrari, V., & Fei-Fei, L. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.02106 · Cited by 46 · see citing works
Deep Learning Markov Random Field for Semantic SegmentationPreprint
Liu, Z., Li, X., Luo, P., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.07230 · Cited by 20 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals
Van Gansbeke, W., Vandenhende, S., Georgoulis, S., & Van Gool, L. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00990 · Cited by 231 · see citing works
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 208 · see citing works
A survey of semi- and weakly supervised semantic segmentation of images
Zhang, M., Zhou, Y. Z., Zhao, J., Man, Y., Liu, B., & Yao, R. (2019) · Artificial Intelligence Review · doi:10.1007/s10462-019-09792-7 · Cited by 144 · see citing works
Large-Scale Unsupervised Semantic Segmentation
Gao, S., Li, Z.-Y., Yang, M.-H., Cheng, M.-M., Han, J., & Torr, P. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3218275 · Cited by 88 · see citing works
Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition
Zhan, X., Liu, Z., Yan, J., Lin, D., & Loy, C. C. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01240-3_35 · Cited by 84 · see citing works
Self-citationSelf-Supervised Drivable Area and Road Anomaly Segmentation Using RGB-D Data For Robotic Wheelchairs
Wang, H., Sun, Y., & Liu, M. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2932874 · Cited by 74 · see citing works
Unsupervised Salient Object Detection with Spectral Cluster Voting
Shin, G., Albanie, S., & Xie, W. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00442 · Cited by 76 · see citing works
Shim, S., Kim, J., Cho, G.-C., & Lee, S.-W. (2020) · IEEE Access · doi:10.1109/access.2020.3022786 · Cited by 67 · see citing works
Multi-Task Self-Supervised Object Detection via Recycling of Bounding Box Annotations
Lee, W., Na, J., & Kim, G. (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.2019.00512 · Cited by 52 · see citing works
Zeng, X., Wen, L., Yang, X., & Ji, C. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105700 · Cited by 39 · see citing works
Exploring Robustness of Unsupervised Domain Adaptation in Semantic Segmentation
Yang, J., Li, C., An, W., Ma, H., Guo, Y., Rong, Y., Zhao, P., & Huang, J. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00906 · Cited by 34 · see citing works
Martins, R., Bersan, D., Campos, M. F. M., & Nascimento, E. R. (2020) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-019-01136-5 · Cited by 32 · see citing works
Probabilistic Prompt Learning for Dense Prediction
Kwon, H., Song, T., Jeong, S., Kim, J., Jang, J., & Sohn, K. (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/cvpr52729.2023.00654 · Cited by 23 · see citing works
Improving fine-tuning of self-supervised models with Contrastive Initialization
Pan, H., Guo, Y. F., Deng, Q., Yang, H., Chen, J., & Chen, Y. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.12.012 · Cited by 22 · see citing works
Coherent, super-resolved radar beamforming using self-supervised learning
Orr, I., Cohen, M., Damari, H., Halachmi, M., Raifel, M., & Zalevsky, Z. (2021) · Science Robotics · doi:10.1126/scirobotics.abk0431 · Cited by 18 · see citing works
MatchGAN: A Self-supervised Semi-supervised Conditional Generative Adversarial Network
Sun, J., Bhattarai, B., & Kim, T. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-69538-5_37 · Cited by 15 · see citing works
Xu, Y., Yu, T., & Yang, B. F. (2018) · IET Generation Transmission & Distribution · doi:10.1049/iet-gtd.2018.5520 · Cited by 15 · see citing works
Pixel-wise triplet learning for enhancing boundary discrimination in medical image segmentation
Wen, Y., Chen, L., Deng, Y., Zhang, Z., & Zhou, C. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108424 · Cited by 14 · see citing works
Adversarial Lagrangian integrated contrastive embedding for limited size datasets
Jalali, A., & Lee, M. (2023) · Neural Networks · doi:10.1016/j.neunet.2022.12.023 · Cited by 10 · see citing works
Hayoun, S. Y., Halachmi, M., Serebro, D., Twizer, K., Medezinski, E., Korkidi, L., Cohen, M., & Orr, I. (2024) · Scientific Reports · doi:10.1038/s41598-024-53009-z · Cited by 9 · see citing works
Self-supervised Pre-training for Mirror Detection
Lin, J.-Y., & Lau, R. W. H. (2023) · IEEE International Conference on Computer Vision · doi:10.1109/iccv51070.2023.01123 · Cited by 10 · see citing works
Human-in-the-loop image segmentation and annotation
Zhang, X., Wang, L., Xie, J., & Zhu, P. (2020) · Science China Information Sciences · doi:10.1007/s11432-019-2759-y · Cited by 7 · see citing works
Are We Hungry for 3D LiDAR Data for Semantic SegmentationPreprint
Gao, B., Pan, Y., Li, C., Geng, S., & Zhao, H. (2020) · arXiv (Cornell University) · Cited by 7 · see citing works
Zhou, N., Chen, J., & Huang, D. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00149 · Cited by 7 · see citing works
SPA2Net: Structure-Preserved Attention Activated Network for Weakly Supervised Object Localization
Chen, D., Pan, X., Tang, F., Dong, W., & Xu, C. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3323793 · Cited by 5 · see citing works
Human–object interaction detection via recycling of ground-truth annotations
Lin, X., Zou, Q., & Xu, X. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110897 · Cited by 5 · see citing works
Zhang, Y., & Leonard, J. J. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636330 · Cited by 5 · see citing works
Cross-Modal Contrastive Masked AutoEncoder for Compressed Video Pre-Training
Li, B., Chen, J., Li, G., Zhang, D., Bao, X., & Huang, D. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3583168 · Cited by 5 · see citing works
Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey and Experimental StudyPreprint
Gao, B., Pan, Y., Li, C., Geng, S., & Zhao, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.04307 · Cited by 4 · see citing works
Bridge Segmentation Performance Gap Via Evolving Shape Prior
Chen, C., Yang, X., Dou, H., Huang, R., Huang, X., Wang, X., Duan, C., Li, S., Xue, W., Heng, P., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3026073 · Cited by 3 · see citing works
Auto-Annotation Quality Prediction for Semi-Supervised Learning with Ensembles
Simon, D., Farber, M., & Goldenberg, R. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00465 · Cited by 3 · see citing works
Attention-Based Network for Semantic Image Segmentation via Adversarial Learning
Ding, X., Tian, Y., Wang, C., Li, Y., Yang, H., & Wang, K. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60636-7_9 · Cited by 2 · see citing works
Enhancing Semi Supervised Semantic Segmentation Through Cycle-Consistent Label Propagation in Video
Addanki, V., Yerramreddy, D. R., Durgapu, S., Boddu, S. S. N., & Durgapu, V. (2024) · Neural Processing Letters · doi:10.1007/s11063-024-11459-6 · Cited by 1 · see citing works
Algorithms Used for Scene Perception in Driverless Cars
Tiwari, A., & Mathew, R. R. (2020) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-030-43192-1_17
- Authors
- Zhan, X., Liu, Z., Luo, P., Tang, X., & Loy, C.
- Affiliations
- Chinese University of Hong Kong (HK)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2018-04-27
- Volume
- 32
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2962902289
- Cited by
- 34
- References
- 42
- Citations by year
- 2025: 1 · 2024: 3 · 2023: 5 · 2022: 4 · 2021: 6 · 2020: 10 · 2019: 3 · 2018: 2
- FWCI
- 2.14
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Chinese University of Hong Kong
- SenseTime Group
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- proxy tasks, triplet loss, unlabeled data, pre-training, cityscape, Pascal VOC 2012
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
