Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 1,067

The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.

DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents

Open access
Lee, Namhoon, Choi, Wongun, Vernaza, Paul, Choy, Christopher B., Torr, Philip H. S., Chandraker, Manmohan·2017·2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
DOI 10.1109/cvpr.2017.233
Open this paper in CoChat — free account

Abstract

We introduce a Deep Stochastic IOC RNN Encoder-decoder framework, DESIRE, for the task of future predictions of multiple interacting agents in dynamic scenes. DESIRE effectively predicts future locations of objects in multiple scenes by 1) accounting for the multi-modal nature of the future prediction (i.e., given the same context, future may vary), 2) foreseeing the potential future outcomes and make a strategic prediction based on that, and 3) reasoning not only from the past motion history, but also from the scene context as well as the interactions among the agents. DESIRE achieves these in a single end-to-end trainable neural network model, while being computationally efficient. The model first obtains a diverse set of hypothetical future prediction samples employing a conditional variational auto-encoder, which are ranked and refined by the following RNN scoring-regression module. Samples are scored by accounting for accumulated future rewards, which enables better long-term strategic decisions similar to IOC frameworks. An RNN scene context fusion module jointly captures past motion histories, the semantic scene context and interactions among multiple agents. A feedback mechanism iterates over the ranking and refinement to further boost the prediction accuracy. We evaluate our model on two publicly available datasets: KITTI and Stanford Drone Dataset. Our experiments show that the proposed model significantly improves the prediction accuracy compared to other baseline methods.

Overview

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/cvpr.2017.233

Cites

15 of 15 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Show all 15 references

Research works printed but not linked: 37

  1. 10.1017/CBO9780511811654 · DOI · Paper page
  2. shalev-shwartz, 2016, Long-term planning by short-term prediction
  3. quiñonero-candela, 2005, A unifying view of sparse approximate gaussian process regression, Journal of Machine Learning Research, 6, 1939
  4. priestley, 1981, Spectral Analysis and Time Series
  5. park, 2016, Egocentric future localization
  6. salakhutdinov, 2009, Deep boltzmann machines, AISTATS, 1, 3
  7. 10.1007/978-3-319-46484-8_33 · DOI · Paper page
  8. rezende, 2014, Stochastic backpropagation and approximate inference in deep generative models
  9. mainprice, 2016, Goal set inverse optimal control and iterative re-planning for predicting human reaching motions in shared workspaces
  10. littman, 0, Markov games as a framework for multi-agent reinforcement learning
  11. 10.1007/BF02532251 · DOI · Paper page
  12. 10.1145/1015330.1015430 · DOI · Paper page
  13. kingma, 2014, Semi-supervised learning with deep generative models, Advances in neural information processing systems, 3581
  14. 10.1007/978-3-319-10599-4_40 · DOI · Paper page
  15. kitani, 2012, Activity forecasting, European Conference on Computer Vision, 201
  16. 10.1109/WACV.2016.7477732 · doi:10.1109/wacv.2016.7477732
  17. 10.1109/ICRA.2014.6907442 · DOI
  18. ziebart, 2008, Maximum entropy inverse reinforcement learning, AAAI, 1433
  19. 10.1109/ICRA.2011.5980538 · DOI
  20. finn, 2016, Guided cost learning Deep inverse optimal control via policy optimization
  21. 10.1016/j.artint.2006.02.006 · DOI · Paper page
  22. 10.1103/PhysRevE.51.4282 · DOI · Paper page
  23. hu, 2003, Nash q-learning for general-sum stochastic games, Journal of Machine Learning Research, 4, 1039
  24. bahdanau, 2014, Neural machine translation by jointly learning to align and translate
  25. 10.1109/CVPR.2016.110 · DOI · Paper page
  26. 10.1007/978-3-319-46448-0_42 · DOI
  27. chung, 2014, Empirical evaluation of gated recurrent neural networks on sequence modeling
  28. 10.3115/v1/D14-1179 · DOI · Paper page
  29. 10.1007/978-94-011-5014-9_23 · DOI · Paper page
  30. 10.1109/CVPR.2015.7298878 · DOI · Paper page
  31. walker, 2016, An uncertain future: Forecasting from static images using variational autoencoders, European Conference on Computer Vision, 835
  32. vondrick, 2016, Anticipating visual representations from unlabeled video
  33. 10.1109/TPAMI.2007.1167 · DOI · Paper page
  34. 10.1109/CVPR.2014.416 · DOI · Paper page
  35. sutskever, 2014, Sequence to sequence learning with neural networks, Advances in neural information processing systems, 3104
  36. sohn, 2015, Learning structured output representation using deep conditional generative models, Advances in neural information processing systems, 3483
  37. thibodeau-laufer, 2014, Deep generative stochastic networks trainable by backprop

Cited by

See all 1,067

Metadata

Authors
Lee, N., Choi, W., Vernaza, P., Choy, C. B., Torr, P. H. S., & Chandraker, M.
Venue
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
IEEE
Published
2017-07
Pages
2165-2174
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr.2017.233
OpenAlex
W2607296803
Cited by
1,067
References
15
FWCI
90.42
Type
conference paper
Language
en
Open access
green

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

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents. https://app.cochat.ai/papers/10.1109/cvpr.2017.233
The paper
Lee, N., Choi, W., Vernaza, P., Choy, C. B., Torr, P. H. S., & Chandraker, M. (2017). DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2017.233

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.