Prototype-based Dataset Comparison
| Authors | |
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| Publication date | 2023 |
| Book title | 2023 IEEE/CVF International Conference on Computer Vision |
| Book subtitle | ICCV 2023 : Paris, France, 2-6 October 2023 : proceedings |
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| ISBN (electronic) |
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| Event | 2023 IEEE/CVF International Conference on Computer Vision (ICCV) |
| Pages (from-to) | 1944–1954 |
| Publisher | Los Alamitos, California: IEEE Computer Society |
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| Abstract |
Dataset summarisation is a fruitful approach to dataset inspection. However, when applied to a single dataset the discovery of visual concepts is restricted to those most prominent. We argue that a comparative approach can expand upon this paradigm to enable richer forms of dataset inspection that go beyond the most prominent concepts.To enable dataset comparison we present a module that learns concept-level prototypes across datasets. We leverage self-supervised learning to discover these prototypes without supervision, and we demonstrate the benefits of our approach in two case-studies. Our findings show that dataset comparison extends dataset inspection and we hope to encourage more works in this direction. Code and usage instructions available at https://github.com/Nanne/ProtoSim
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| Document type | Conference contribution |
| Note | With supplementary material |
| Language | English |
| Published at | https://doi.org/10.1109/ICCV51070.2023.00186 |
| Published at | https://openaccess.thecvf.com/content/ICCV2023/html/van_Noord_Protoype-based_Dataset_Comparison_ICCV_2023_paper.html |
| Other links | https://github.com/Nanne/ProtoSim https://www.proceedings.com/72328.html |
| Downloads |
Prototype-based_Dataset_Comparison_2
(Accepted author manuscript)
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| Supplementary materials | |
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