AuxNet: Auxiliary Tasks Enhanced Semantic Segmentation for Automated Driving
Open accessAbstract
Decision making in automated driving is highly specific to the environment and thus semantic segmentation plays a key role in recognizing the objects in the environment around the car. Pixel level classification once considered a challenging task which is now becoming mature to be productized in a car. However, semantic annotation is time consuming and quite expensive. Synthetic datasets with domain adaptation techniques have been used to alleviate the lack of large annotated datasets. In this work, we explore an alternate approach of leveraging the annotations of other tasks to improve semantic segmentation. Recently, multi-task learning became a popular paradigm in automated driving which demonstrates joint learning of multiple tasks improves overall performance of each tasks. Motivated by this, we use auxiliary tasks like depth estimation to improve the performance of semantic segmentation task. We propose adaptive task loss weighting techniques to address scale issues in multi-task loss functions which become more crucial in auxiliary tasks. We experimented on automotive datasets including SYNTHIA and KITTI and obtained 3% and 5% improvement in accuracy respectively.
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Motion segmentation has been explored in [49,57,48] for narrow angle cameras.
“This has been explored in [49,57,48] for narrow angle cameras.”
From WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving (2019) · cites this paper as [48]
The Introduction (p. 1) describes semantic segmentation for automated driving, and the Auxiliary Task Learning section (p. 3) illustrates optical flow as a possible task. Searches for motion segmentation and narrow-angle cameras found no passage saying this paper explored motion segmentation with such cameras. (LLM, conf=0.73)
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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
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · 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
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
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
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
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
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46723-8_49 · Cited by 7,945 · see citing works
Caruana, R. (1997) · Machine Learning · doi:10.1023/a:1007379606734 · Cited by 6,636 · see citing works
Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
Cipolla, R., Gal, Y., & Kendall, 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.2018.00781 · Cited by 3,657 · see citing works
Eigen, D., & Fergus, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.304 · Cited by 2,862 · 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
An Overview of Multi-Task Learning in Deep Neural NetworksPreprint
Ruder, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.05098 · Cited by 2,409 · see citing works
Ros, G., Sellart, L., Materzynska, J., Vazquez, D., & Lopez, A. M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.352 · Cited by 2,338 · see citing works
The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes
Neuhold, G., Ollmann, T., Bulo, S. R., & Kontschieder, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.534 · Cited by 1,434 · 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
End-To-End Multi-Task Learning With Attention
Liu, S., Johns, E., & Davison, A. J. (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.2019.00197 · Cited by 1,299 · see citing works
VirtualWorlds as Proxy for Multi-object Tracking Analysis
Gaidon, A., Wang, Q., Cabon, Y., & Vig, E. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.470 · Cited by 1,079 · see citing works
FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-Based CNN Architecture
Hazirbas, C., Ma, L., Domokos, C., & Cremers, D. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-54181-5_14 · Cited by 902 · see citing works
End-to-End Learning of Driving Models from Large-Scale Video Datasets
Xu, H., Gao, Y., Yu, F., & Darrell, T. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.376 · Cited by 884 · see citing works
Multi-Task Learning as Multi-Objective Optimization
Sener, O., & Koltun, V. (2018) · arXiv · doi:10.48550/arxiv.1810.04650 · Cited by 852 · see citing works
GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask NetworksPreprint
Chen, Z., Badrinarayanan, V., Lee, C., & Rabinovich, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.02257 · Cited by 797 · see citing works
MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
Teichmann, M., Weber, M., Zollner, M., Cipolla, R., & Urtasun, R. (2018) · 2018 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/ivs.2018.8500504 · Cited by 681 · see citing works
Kokkinos, I. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.579 · Cited by 657 · see citing works
Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation
Sankaranarayanan, S., Balaji, Y., Jain, A., Lim, S. N., & Chellappa, R. (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.2018.00395 · Cited by 488 · see citing works
Dynamic Task Prioritization for Multitask Learning
Guo, M., Haque, A., Huang, D.-A., Yeung, S. Y., & Fei-Fei, L. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01270-0_17 · Cited by 377 · see citing works
A convex formulation for learning task relationships in multi-task learning
Zhang, Y., & Yeung, D. (2010) · Cited by 332 · see citing works
Virtual Worlds as Proxy for Multi-Object Tracking AnalysisPreprint
Gaidon, A., Wang, Q., Cabon, Y., & Vig, E. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.06457 · Cited by 302 · see citing works
GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks
Zhao, C., Badrinarayanan, V., Lee, C., & Rabinovich, A. (2018) · International Conference on Machine Learning · Cited by 275 · see citing works
Auxiliary Tasks in Multi-task LearningPreprint
Liebel, L., & Körner, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.06334 · Cited by 269 · see citing works
TorontoCity: Seeing the World with a Million Eyes
Wang, S., Bai, M., Mattyus, G., Chu, H., Luo, W., Yang, B., Liang, J., Cheverie, J., Fidler, S., & Urtasun, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.327 · Cited by 186 · see citing works
Synscapes: A Photorealistic Synthetic Dataset for Street Scene ParsingPreprint
Wrenninge, M., & Unger, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.08705 · Cited by 186 · see citing works
Deep semantic segmentation for automated driving: Taxonomy, roadmap and challenges
Siam, M., Elkerdawy, S., Jagersand, M., & Yogamani, S. (2017) · 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2017.8317714 · Cited by 178 · see citing works
Self-citationJoint Semantic Segmentation and Depth Estimation with Deep Convolutional Networks
Mousavian, A., Pirsiavash, H., & Košecká, J. (2016) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2016.69 · Cited by 147 · see citing works
Effnet: An Efficient Structure for Convolutional Neural Networks
Freeman, I., Roese-Koerner, L., & Kummert, A. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451339 · Cited by 128 · see citing works
Multitask Learning with Low-Level Auxiliary Tasks for Encoder-Decoder Based Speech Recognition
Toshniwal, S., Tang, H., Lu, L., & Livescu, K. (2017) · Interspeech (USB) · doi:10.21437/interspeech.2017-1118 · Cited by 121 · see citing works
Fast Scene Understanding for Autonomous DrivingPreprint
Neven, D., Brabandere, B. D., Georgoulis, S., Proesmans, M., & Gool, L. V. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.02550 · Cited by 66 · see citing works
Joint Semantic Segmentation and Depth Estimation with Deep Convolutional Networks
Mousavian, A., Pirsiavash, H., & Košecká, J. (2016) · Maryland Shared Open Access Repository (USMAI Consortium) · doi:10.13016/m2uswx-e8bj · Cited by 61 · see citing works
Analyzing modular CNN architectures for joint depth prediction and semantic segmentation
Jafari, O. H., Groth, O., Kirillov, A., Yang, M. Y., & Rother, C. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989537 · Cited by 60 · see citing works
Chen, L., Yang, Z., Ma, J., & Luo, Z. (2018) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2018.00145 · Cited by 54 · see citing works
Monocular Fisheye Camera Depth Estimation Using Sparse LiDAR Supervision
Kumar, V. R., Milz, S., Witt, C., Simón, M., Amende, K., Petzold, J., Yogamani, S. K., & Pech, T. (2018) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2018.8569665 · Cited by 42 · see citing works
Self-citationParthasarathy, S., & Busso, C. (2018) · arXiv (Cornell University) · doi:10.21437/interspeech.2018-1391 · Cited by 40 · see citing works
Multiple-Gradient Descent Algorithm (MGDA)
Désidéri, J. (2009) · Cited by 31 · see citing works
End-to-End Multi-Task Learning with AttentionPreprint
Liu, S., Johns, E., & Davison, A. J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.10704 · Cited by 22 · see citing works
Monocular Depth Estimation by Learning from Heterogeneous Datasets
Gurram, A., Urfalıoǧlu, O., Halfaoui, I., Bouzaraa, F., & López, A. M. (2018) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2018.8500683 · Cited by 19 · see citing works
Exploration on Deep Drug Discovery: Representation and Learning
Liu, S. (2018) · Minds at UW (University of Wisconsin) · Cited by 5 · see citing works
Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation
Sankaranarayanan, S., Balaji, Y., Jain, A., Lim, S. N., & Chellappa, R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.06969 · Cited by 2
Flagged first, then by citations
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WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving
Yogamani, S., Hughes, C., Horgan, J., Sistu, G., Chennupati, S., Uricar, M., Milz, S., Simon, M., Amende, K., Witt, C., et al. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00940 · Cited by 303 · see citing works
“However, there is a recent trend [30, 53, 51, 8] to solve tasks using a single multi-task model to enable efficient reuse of encoder features and also”
BackgroundSelf-citationSurround-View Fisheye Camera Perception for Automated Driving: Overview, Survey & Challenges
Kumar, V. R., Eising, C., Witt, C., & Yogamani, S. K. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3235057 · Cited by 121 · see citing works
Self-citationMultiNet++: Multi-Stream Feature Aggregation and Geometric Loss Strategy for Multi-Task Learning
Chennupati, S., Sistu, G., Yogamani, S. K., & Rawashdeh, S. A. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00159 · Cited by 94 · see citing works
Self-citationKumar, V. R., Klingner, M., Yogamani, S. K., Milz, S., Fingscheidt, T., & Maeder, P. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00011 · Cited by 86 · see citing works
Self-citationA hybrid deep convolutional neural network for accurate land cover classification
Wambugu, N. M., Chen, Y., Xiao, Z., Wei, M., Bello, S. A., Marcato, J., & Li, J. (2021) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2021.102515 · Cited by 68 · see citing works
Near-Field Perception for Low-Speed Vehicle Automation Using Surround-View Fisheye Cameras
Eising, C., Horgan, J., & Yogamani, S. K. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3127646 · Cited by 53 · see citing works
Self-citationKumar, V. R., Yogamani, S. K., Bach, M., Witt, C., Milz, S., & Mäder, P. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9340732 · Cited by 48 · see citing works
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An, N. S., Lan, P. N., Dao, H. V., Long, D. V., Tran, Q. T., Thủy, N. T. T., & Sang, D. V. (2022) · IEEE Access · doi:10.1109/access.2022.3168693 · Cited by 47 · see citing works
MobTCast: Leveraging Auxiliary Trajectory Forecasting for Human Mobility PredictionPreprint
Xue, H., Salim, F. D., Ren, Y., & Oliver, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.01401 · Cited by 47 · see citing works
Dynamic Task Weighting Methods for Multi-task Networks in Autonomous Driving Systems
Leang, I., Sistu, G., Bürger, F., Bursuc, A., & Yogamani, S. K. (2020) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc45102.2020.9294676 · Cited by 43 · see citing works
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Ramirez, P. Z., Tonioni, A., Salti, S., & Stefano, L. D. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00820 · Cited by 38 · see citing works
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Das, A., Krizek, P., Sistu, G., Burger, F., Madasamy, S., Uricar, M., Kumar, V. R., & Yogamani, S. (2020) · 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc45102.2020.9294677 · Cited by 25 · see citing works
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Nakamura, A. T. M., Grassi, V., & Wolf, D. F. (2021) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2021.104205 · Cited by 23 · see citing works
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Self-citationKumar, V. R., Yogamani, S. K., Milz, S., & Mäder, P. (2021) · Electronic Imaging · doi:10.2352/issn.2470-1173.2021.17.avm-181 · Cited by 14 · see citing works
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Stapleton, F., Galván, E., Sistu, G., & Yogamani, S. K. (2022) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3520304.3528984 · Cited by 8 · see citing works
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Yang, G., He, Y., Lv, Y., Chen, Y., Coatrieux, J.-L., Sun, X., Wang, Q., Wei, Y., Li, S., & Zhu, Y. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3247492 · Cited by 5 · see citing works
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Self-citationWang, Y., Chen, Y., Yuan, H., & Wu, C. (2024) · International Journal of Advances in Intelligent Informatics · doi:10.26555/ijain.v10i1.1521 · Cited by 4 · see citing works
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Self-citationEising, C., Horgan, J., & Yogamani, S. K. (2021) · arXiv (Cornell University) · Cited by 4 · see citing works
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Self-citationThayalan, S., Srıdevı, M., Santhakumar, K., Kiran, B. R., Liu, H., Gauthier, T., & Yogamani, S. K. (2022) · Journal of Electronic Imaging · doi:10.1117/1.jei.32.1.011004 · Cited by 1 · see citing works
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- Authors
- Chennupati, S., Sistu, G., Yogamani, S., & Rawashdeh, S.
- Affiliations
- University of Michigan–Dearborn (US)
- Valeo (Ireland) (IE)
- Venue
- Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
- Published
- 2019
- Pages
- 645-652
- OpenAlex
- W2909393256
- Cited by
- 42
- References
- 46
- Citations by year
- 2024: 3 · 2023: 4 · 2022: 10 · 2021: 13 · 2020: 7 · 2019: 5
- FWCI
- 2.85
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
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- cc-by-nc-nd
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- International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) (publishedVersion, cc-by-nc-nd)
- 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 › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- semantic segmentation, autonomous driving, multi-task learning, auxiliary tasks, depth estimation, adaptive loss weighting, SYNTHIA, KITTI, object recognition
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 ›
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