Boosting Object Representation Learning via Motion and Object Continuity-Reference-Cited by-同舟云学术

Boosting Object Representation Learning via Motion and Object Continuity

Published:2023 Issue: Volume: Page:610-628
ISSN:0302-9743
Container-title:Machine Learning and Knowledge Discovery in Databases: Research Track
language:
Short-container-title:

Author:

Delfosse Quentin,Stammer Wolfgang,Rothenbächer Thomas,Vittal Dwarak,Kersting Kristian

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-43421-1_36

Reference64 articles.

1. Anand, A., Racah, E., Ozair, S., Bengio, Y., Côté, M.-A., Hjelm, R.D.: Unsupervised state representation learning in atari. In: Wallach, H.M., Larochelle, H., Beygelzimer, A., d’Alché-Buc, F., Fox, E.B., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019 (NeurIPS) (2019)

2. Anthwal, S., Ganotra, D.: An overview of optical flow-based approaches for motion segmentation. Imaging Sci. J. 67(5), 284–294 (2019)

3. Bai, S., Geng, Z., Savani, Y., Kolter, J.Z.: Deep equilibrium optical flow estimation. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)

4. Bao, Z., Tokmakov, P., Jabri, A., Wang, Y.-X., Gaidon, A., Hebert, M.: Discovering objects that can move. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)

5. Brockman, G., et al.: OpenAI gym. CoRR (2016)

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Machine Learning-Enhanced IoT Wearable Device for Monitoring and Providing Feedback on Stuttering;2024 3rd International Conference on Computational Modelling, Simulation and Optimization (ICCMSO);2024-06-14