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Cross-Stitch Networks for Multi-task Learning
Open accessAbstract
Multi-task learning in Convolutional Networks has displayed remarkable success in the field of recognition. This success can be largely attributed to learning shared representations from multiple supervisory tasks. However, existing multi-task approaches rely on enumerating multiple network architectures specific to the tasks at hand, that do not generalize. In this paper, we propose a principled approach to learn shared representations in ConvNets using multitask learning. Specifically, we propose a new sharing unit: "cross-stitch" unit. These units combine the activations from multiple networks and can be trained end-to-end. A network with cross-stitch units can learn an optimal combination of shared and task-specific representations. Our proposed method generalizes across multiple tasks and shows dramatically improved performance over baseline methods for categories with few training examples.
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Cites
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
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
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853
Caruana, R. (1997) · Machine Learning · doi:10.1023/a:1007379606734 · Cited by 6,636
How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014) · arXiv · doi:10.48550/arxiv.1411.1792 · Cited by 6,061
Indoor segmentation and support inference from rgbd images
Silberman, N., Hoiem, D., Kohli, P., & Fergus, R. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33715-4_54 · Cited by 5,760
A unified architecture for natural language processing: deep neural networks with multitask learning
Collobert, R., & Weston, J. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390177 · Cited by 5,058
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2013) · arXiv · doi:10.48550/arxiv.1310.1531 · Cited by 4,019
Cited by
Li, Z., & Hoiem, D. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2773081 · Cited by 4,202
iCaRL: Incremental Classifier and Representation Learning
Rebuffi, S.-A., Kolesnikov, A., Sperl, G., & Lampert, C. H. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.587 · Cited by 3,988
A Survey on Multi-Task Learning
Zhang, Y., & Yang, Q. (2017) · arXiv · doi:10.48550/arxiv.1707.08114 · Cited by 2,910
Kirillov, A., He, K., Girshick, R., Rother, C., & Dollár, P. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00963 · Cited by 1,659
Panoptic Feature Pyramid Networks
Kirillov, A., Girshick, R., He, K., & Dollár, P. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00656 · Cited by 1,460
Taskonomy: Disentangling Task Transfer Learning
Zamir, A. R., Sax, A., Shen, W., Guibas, L., Malik, J., & Savarese, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00391 · Cited by 1,128
Gradient Surgery for Multi-Task Learning
Yu, T., Kumar, S., Gupta, A., Levine, S., Hausman, K., & Finn, C. (2020) · arXiv · doi:10.48550/arxiv.2001.06782 · Cited by 755
Self-citationKokkinos, I. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.579 · Cited by 657
Adversarial Multi-task Learning for Text Classification
Liu, P., Qiu, X., & Huang, X. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1001 · Cited by 601
A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks
Hashimoto, K., xiong, C., Tsuruoka, Y., & Socher, R. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1206 · Cited by 535
Xu, D., Ouyang, W., Wang, X., & Sebe, N. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00077 · Cited by 518
SpotTune: Transfer Learning Through Adaptive Fine-Tuning
Guo, Y., Shi, H., Kumar, A., Grauman, K., Rosing, T., & Feris, R. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00494 · Cited by 506
Multi-Task Learning with Deep Neural Networks: A Survey
Crawshaw, M. (2020) · arXiv · doi:10.48550/arxiv.2009.09796 · Cited by 428
Integrating Machine Learning with Human Knowledge
Deng, C., Ji, X., Rainey, C., Zhang, J., & Lu, W. (2020) · iScience · doi:10.1016/j.isci.2020.101656 · Cited by 243
Contextual Priming and Feedback for Faster R-CNN
Shrivastava, A., & Gupta, A. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_20 · Cited by 153
Self-citationAdaShare: Learning What To Share For Efficient Deep Multi-Task Learning
Sun, X., Panda, R., Feris, R., & Saenko, K. (2019) · arXiv · doi:10.48550/arxiv.1911.12423 · Cited by 169
Growing a Brain: Fine-Tuning by Increasing Model Capacity
Wang, Y.-X., Ramanan, D., & Hebert, M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.323 · Cited by 154
“Transfer learning is related to both multi-task learning [32, 4, 28, 10, 11, 45, 24, 2] and learning novel categories from few examples [49, 19, 21, 35, 51, 22, 5, 13, 50, 47, 31].”
BackgroundSelf-citationEfficiently Identifying Task Groupings for Multi-Task Learning
Fifty, C., Amid, E., Zhao, Z., Yu, T., Anil, R., & Finn, C. (2021) · arXiv · doi:10.48550/arxiv.2109.04617 · Cited by 125
Detecting 32 Pedestrian Attributes for Autonomous Vehicles
Mordan, T., Cord, M., Perez, P., & Alahi, A. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3107587 · Cited by 36
Yao, J., & Kowal, J. (2025) · Energy and AI · doi:10.1016/j.egyai.2025.100585 · Cited by 30
StuffNet: Using ‘Stuff’ to Improve Object Detection
Brahmbhatt, S., Christensen, H. I., & Hays, J. (2017) · 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2017.109 · Cited by 24
A biologically inspired architecture with switching units can learn to generalize across backgrounds
Voina, D., Shea-Brown, E., & Mihalas, S. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.09.014 · Cited by 2
Metadata
- Authors
- Misra, I., Shrivastava, A., Gupta, A., & Hebert, M.
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 3994-4003
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2339391301
- Cited by
- 1,397
- References
- 14
- FWCI
- 41.39
- Type
- conference paper
- Language
- en
- Open access
- green
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