DeepSetNet: Predicting Sets with Deep Neural Networks
Open accessAbstract
This paper addresses the task of set prediction using deep learning. This is important because the output of many computer vision tasks, including image tagging and object detection, are naturally expressed as sets of entities rather than vectors. As opposed to a vector, the size of a set is not fixed in advance, and it is invariant to the ordering of entities within it. We define a likelihood for a set distribution and learn its parameters using a deep neural network. We also derive a loss for predicting a discrete distribution corresponding to set cardinality. Set prediction is demonstrated on the problem of multi-class image classification. Moreover, we show that the proposed cardinality loss can also trivially be applied to the tasks of object counting and pedestrian detection. Our approach outperforms existing methods in all three cases on standard datasets.
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Previous attempts at direct set prediction either add other forms of prior knowledge or have not proven competitive with strong baselines on challenging benchmarks.
“previous attempts [43,16,4,39] either add other forms of prior knowledge, or have not proven to be competitive with strong baselines on challenging benchmarks.”
From End-to-End Object Detection with Transformers (2020) · cites this paper as [39]
The Conclusion reports that DeepSetNet achieved competitive or superior results in crowd counting, pedestrian detection, and multi-class image classification. The Object Counting section also says it did not use location or perspective information. Those passages run contrary to the characterization of this work as relying on such prior knowledge or failing to compete with strong baselines. (LLM, conf=0.82)
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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
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · 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
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 56,035 · see citing works
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · 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
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584 · see citing works
(2016) · Cited by 31,590 · see citing works
Fast R-CNNPreprint
Girshick, R. B. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1504.08083 · Cited by 29,416 · see citing works
Sequence to Sequence Learning with Neural Networks
Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766 · see citing works
Visualizing and understanding convolutional networks
Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839 · see citing works
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897 · see citing works
Robust Real-Time Face Detection
Viola, P., & Jones, M. J. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000013087.49260.fb · Cited by 14,097 · see citing works
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 · see citing works
Speech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893 · 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
Pedestrian Detection: An Evaluation of the State of the Art
Dollár, P., Wojek, C., Schiele, B., & Perona, P. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.155 · Cited by 3,412 · 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
Self-citationOne-shot learning of object categories
Fei-Fei, L., Fergus, R., & Perona, P. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.79 · Cited by 3,029 · see citing works
Learning Multi-domain Convolutional Neural Networks for Visual Tracking
Nam, H., & Han, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.465 · Cited by 2,806 · see citing works
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., & LeCun, Y. (2013) · arXiv · doi:10.48550/arxiv.1312.6229 · Cited by 2,680 · see citing works
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Bourdev, L., Girshick, R., Hays, J., Perona, P., Ramanan, D., Zitnick, C. L., & Dollár, P. (2014) · arXiv · doi:10.48550/arxiv.1405.0312 · Cited by 2,579 · see citing works
Single-Image Crowd Counting via Multi-Column Convolutional Neural Network
Zhang, Y., Zhou, D., Chen, S., Gao, S., & Ma, Y. (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.2016.70 · Cited by 2,356 · see citing works
Vinyals, O., Fortunato, M., & Jaitly, N. (2015) · neural information processing systems · Cited by 2,024 · see citing works
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection
Cai, Z., Fan, Q., Feris, R. S., & Vasconcelos, N. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_22 · Cited by 1,605 · see citing works
Going Deeper with Convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2014) · arXiv · doi:10.48550/arxiv.1409.4842 · Cited by 1,461 · see citing works
CNN-RNN: A Unified Framework for Multi-label Image Classification
Wang, J., Yang, Y., Mao, J., Huang, Z., Huang, C., & Xu, W. H. (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.2016.251 · Cited by 1,326 · see citing works
MOT16: A Benchmark for Multi-Object Tracking
Milan, A., Leal-Taixe, L., Reid, I., Roth, S., & Schindler, K. (2016) · arXiv · doi:10.48550/arxiv.1603.00831 · Cited by 1,309 · see citing works
Self-citationPrivacy preserving crowd monitoring: Counting people without people models or tracking
Chan, A. B., Liang, J., & Vasconcelos, N. M. (2008) · 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.2008.4587569 · Cited by 1,198 · see citing works
DenseCap: Fully Convolutional Localization Networks for Dense Captioning
Johnson, J., Karpathy, A., & Fei-Fei, L. (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.2016.494 · Cited by 1,161 · see citing works
Cross-scene crowd counting via deep convolutional neural networks
Zhang, C., Li, H., Wang, X., & Yang, X. (2015) · 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.2015.7298684 · Cited by 1,155 · see citing works
Multi-source Multi-scale Counting in Extremely Dense Crowd Images
Idrees, H., Saleemi, I., Seibert, C., & Shah, M. A. (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.2013.329 · Cited by 1,065 · see citing works
Learning To Count Objects in Images
Lempitsky, V., & Zisserman, A. (2010) · Cited by 1,063 · 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
HCP: A Flexible CNN Framework for Multi-Label Image Classification
Wei, Y., Xia, W., Lin, M., Huang, J., Ni, B., Dong, J., Zhao, Y., & Yan, S. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2491929 · Cited by 814 · see citing works
Learning Non-maximum Suppression
Hosang, J., Benenson, R., & Schiele, B. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.685 · Cited by 736 · see citing works
Order Matters: Sequence to sequence for setsPreprint
Vinyals, O., Samy Bengio, A., & Kudlur, M. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.06391 · Cited by 696 · see citing works
Towards Perspective-Free Object Counting with Deep Learning
Oñoro-Rubio, D., & López-Sastre, R. J. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46478-7_38 · Cited by 650 · see citing works
End-to-End People Detection in Crowded Scenes
Stewart, R., Andriluka, M., & Ng, A. Y. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.255 · Cited by 539 · see citing works
Pedestrian detection at 100 frames per second
Benenson, R., Mathias, M., Timofte, R., & Gool, L. V. (2012) · 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.2012.6248017 · Cited by 524 · see citing works
New features and insights for pedestrian detection
Walk, S., Majer, N., Schindler, K., & Schiele, B. (2010) · 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.2010.5540102 · Cited by 468 · see citing works
Bayesian Poisson regression for crowd counting
Chan, A. B., & Vasconcelos, N. M. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459191 · Cited by 458 · see citing works
COUNT Forest: CO-Voting Uncertain Number of Targets Using Random Forest for Crowd Density Estimation
Pham, V.-Q., Kozakaya, T., Yamaguchi, O., & Okada, R. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.372 · Cited by 376 · see citing works
Deep Convolutional Ranking for Multilabel Image AnnotationPreprint
Gong, Y., Jia, Y., Leung, T., Toshev, A., & Ioffe, S. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1312.4894 · Cited by 293 · see citing works
Deep Convolutional Ranking for Multilabel Image AnnotationPreprint
Gong, Y., Jia, Y., Leung, T., Toshev, A., & Ioffe, S. (2014) · Cited by 272 · see citing works
Arteta, C., Lempitsky, V., Noble, J. A., & Zisserman, A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10578-9_33 · Cited by 170 · see citing works
Deep Decision Network for Multi-class Image Classification
Murthy, V. N., Singh, V. K., Chen, T., Manmatha, R., & Comaniciu, D. (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.2016.246 · Cited by 78 · see citing works
Learning Multi-Domain Convolutional Neural Networks for Visual TrackingPreprint
Nam, H., & Han, B. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1510.07945 · Cited by 78 · see citing works
Individualness and Determinantal Point Processes for Pedestrian Detection
Lee, D., Cha, G., Yang, M.-H., & Oh, S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46466-4_20 · Cited by 31 · see citing works
Efficient Point Process Inference for Large-Scale Object Detection
Pham, T. T., Rezatofighi, S. H., Reid, I. D., & Chin, T.-J. (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.2016.310 · Cited by 29 · see citing works
Self-citationModel-based classification and novelty detection for point pattern data
Vo, B., Tran, N.-Q., Phung, D., & Vo, B.-T. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7900030 · Cited by 9 · see citing works
Flagged first, then by citations
1 of the 52 references couldn’t be matched to a known paper.
Research works printed but not linked: 4
- C. Arteta, V. Lempitsky, J. A. Noble, and A. Zisserman. In- teractive object counting. In ECCV, pages 504-518, 2014. 2, 7
- L. Fiaschi, U. Koethe, R. Nair, and F. A. Hamprecht. Learn- ing to count with regression forest and structured labels. In ICPR, pages 2685-2688, 2012. 2
- R. Girshick. Fast R-CNN. In ICCV 2015. 2
- Y. Wei, W. Xia, J. Huang, B. Ni, J. Dong, Y. Zhao, and S. Yan. CNN: Single-label to multi-label. CoRR, abs/1406.5726, 2014. 2 · arXiv
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Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58452-8_13 · Cited by 18,285 · see citing works
Energy flow networks: deep sets for particle jets
Komiske, P., Metodiev, E. M., & Thaler, J. (2019) · Journal of High Energy Physics · doi:10.1007/jhep01(2019)121 · Cited by 302 · see citing works
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CountNet: Estimating the Number of Concurrent Speakers Using Supervised Learning
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Ruiz, L., Gama, F., Marques, A. G., & Ribeiro, A. (2019) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2019.2955832 · Cited by 55 · see citing works
FSPool: Learning Set Representations with Featurewise Sort PoolingPreprint
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Goëau, H., Mora-Fallas, A., Champ, J., Love, N. L. R., Mazer, S. J., Mata‐Montero, E., Joly, A., & Bonnet, P. (2020) · Applications in Plant Sciences · doi:10.1002/aps3.11368 · Cited by 37 · see citing works
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Resset: A Recurrent Model for Sequence of Sets with Applications to Electronic Medical Records
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Learn to Predict Sets Using Feed-Forward Neural Networks
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Self-citationVelazquez, D. A., Gonfaus, J. M., Rodríguez, P., Roca, F. X., Ozawa, S., & Gonzàlez, J. (2021) · IEEE Access · doi:10.1109/access.2021.3101297 · Cited by 15 · see citing works
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Joint Learning of Set Cardinality and State Distribution
Rezatofighi, S. H., Milan, A., Shi, Q., Dick, A., & Reid, I. D. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11639 · Cited by 11 · see citing works
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A Weakly Supervised Learning Based Clustering Framework.Preprint
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Wang, F., Liu, J., Zhang, S., Zhang, G., Zheng, Y., Li, X., Liang, W. G., & Li, Y. (2019) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2019.09.019
Compositional Embeddings: Joint Perception and Comparison of Class Label Sets
Li, Z., & Whitehill, J. (2019)
Multi Object Detection Using Deep Neural Network
Das, S., Devaraj Verma, C., & Jvn, L. (2019) · Journal of Emerging Technologies and Innovative Research
Intelligent Vehicle for Urban Road Traffic Recognition System Using Deep Learning Algorithm
Jitu, M. O., Mostofa, M. G., Raihan, P. M. S., & Kader, A. (2025) · European Journal of Applied Science Engineering and Technology · doi:10.59324/ejaset.2025.3(3).10
Wang, T., Wang, C., Wen, Z., Deng, R., Shu, Y., Cheng, P., & Chen, J. (2026) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2026.3654868
Multi-agent reinforcement learning and object detection asstructured prediction
Carion, N. (2020) · theses.fr (ABES) · doi:10.70675/dd567c2az5c46z45f1z906fzc680ab8febb9
- Authors
- Rezatofighi, S. H., B. G., V. K., Milan, A., Abbasnejad, E., Dick, A., & Reid, I.
- Affiliations
- The University of Adelaide (AU)
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 5257-5266
- OpenAlex
- W2557260587
- Cited by
- 45
- References
- 52
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 1 · 2023: 2 · 2022: 2 · 2021: 4 · 2020: 8 · 2019: 15 · 2018: 6
- FWCI
- 2.40
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Australian Research Council (FL130100102, CE140100016)
- 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 › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- set prediction, Deep Sets, multi-class image classification, object counting, pedestrian detection, image tagging, object detection, permutation invariance
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