The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.
Towards Open World Object Detection
Open accessAbstract
Humans have a natural instinct to identify unknown object instances in their environments. The intrinsic curiosity about these unknown instances aids in learning about them, when the corresponding knowledge is eventually available. This motivates us to propose a novel computer vision problem called: ‘Open World Object Detection’, where a model is tasked to: 1) identify objects that have not been introduced to it as ‘unknown’, without explicit supervision to do so, and 2) incrementally learn these identified unknown categories without forgetting previously learned classes, when the corresponding labels are progressively received. We formulate the problem, introduce a strong evaluation protocol and provide a novel solution, which we call ORE: Open World Object Detector, based on contrastive clustering and energy based unknown identification. Our experimental evaluation and ablation studies analyse the efficacy of ORE in achieving Open World objectives. As an interesting by-product, we find that identifying and characterising unknown instances helps to reduce confusion in an incremental object detection setting, where we achieve state-of-the-art performance, with no extra methodological effort. We hope that our work will attract further research into this newly identified, yet crucial research direction.1
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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
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
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Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803
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- livio, 2017, Why? What makes us curious
- wu, 2019, Detectron2
- 10.1109/TPAMI.2012.256 · DOI · Paper page
- reid, 2020, Matthewreid854/reliability v0 5 4
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Cited by
A Comprehensive Survey of Continual Learning: Theory, Method and Application
Wang, L., Zhang, X., Su, H., & Zhu, J. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3367329 · Cited by 1,113
“According to the provided X t and Y t in each D t , continual learning is further extended to zero-shot [23], few-shot [24], semi-supervised [25], open-world [26] and un-/self-supervised [27], [28] scenarios.”
MethodOvercoming Catastrophic Forgetting in Incremental Object Detection via Elastic Response Distillation
Feng, T., Wang, M., & Yuan, H. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00921 · Cited by 132
Sylph: A Hypernetwork Framework for Incremental Few-shot Object Detection
Yin, L., Perez-Rua, J. M., & Liang, K. J. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00883 · Cited by 56
Metadata
- Authors
- Joseph, K. J., Khan, S., Khan, F. S., & Balasubramanian, V. N.
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 5826-5836
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3176709420
- Cited by
- 544
- References
- 63
- FWCI
- 32.05
- Type
- conference paper
- Language
- en
- Open access
- green
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- Joseph, K. J., Khan, S., Khan, F. S., & Balasubramanian, V. N. (2021). Towards Open World Object Detection. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr46437.2021.00577
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