ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition
Abstract
Object recognition has made great advances in the last decade, but predominately still relies on many high-quality training examples per object category. In contrast, learning new objects from only a few examples could enable many impactful applications from robotics to user personalization. Most few-shot learning research, however, has been driven by benchmark datasets that lack the high variation that these applications will face when deployed in the real-world. To close this gap, we present the ORBIT dataset and benchmark, grounded in the real-world application of teachable object recognizers for people who are blind/low-vision. The dataset contains 3,822 videos of 486 objects recorded by people who are blind/low-vision on their mobile phones. The benchmark reflects a realistic, highly challenging recognition problem, providing a rich playground to drive research in robustness to few-shot, high-variation conditions. We set the benchmark’s first state-of-the-art and show there is massive scope for further innovation, holding the potential to impact a broad range of real-world vision applications including tools for the blind/low-vision community. We release the dataset at https://doi.org/10.25383/city.14294597 and benchmark code at https://github.com/microsoft/ORBIT-Dataset.
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Prior efforts of disability-first datasets often focus on the data collection process.
“While prior efforts of disabilityfirst datasets often focus on the data collection process [36, 47, 63, 68, 88, 98], our study represents an important step in centering disabled people and their expertise throughout the AI development pipeline.”
From Disability-First AI Dataset Annotation: Co-designing Stuttered Speech Annotation Guidelines with People Who Stutter (2026) · cites this paper as [68]
In disability contexts, key datasets include ORBIT, StammerTalk, ASL Citizen, and the grassroots Disabled And Here stock image collection.
“In disability contexts, key datasets include ORBIT [80] -videos and images of objects recorded by people who are blind or low-vision; StammerTalk [74] and ASL Citizen [32] for speech and language impairments; and the grassroots Disabled And Here stock image collection celebrating disabled Black, Indigenous, People of Colour (BIPOC) [6].”
From Engaging Communities Meaningfully in Defining Disability Representation for AI Image Generation (2026) · cites this paper as [80]
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Parmar, V., Kingra, S. K., Sarwar, S. S., Li, Z., de Salvo, B., & Suri, M. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00473 · Cited by 1 · see citing works
Exploiting deep learning methods for object recognition and grasping tasks
Akkad, M. A. A., & Ali, R. (2023) · AIP conference proceedings · doi:10.1063/5.0112973
Pohovey, J., Lusardi, M., Hasan, A., Liu, S., Schreiber, A., Olatunji, S., Rogers, W. A., & Driggs-Campbell, K. (2025) · doi:10.36227/techrxiv.175459958.86411571/v1
Prototype Guided Multi-Scale Class Aggregation for Generalized Few-Shot Semantic Segmentation
Jiang, W., Qin, P., Zhang, G., Wu, K., & Wang, S. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11209413
Improving visual question answering for visually impaired people leveraging pre-trained models
Pal, R., Kar, S., Prasad, D. K., & Sekh, A. A. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-07996-x
Nurcihan, D. E. R. E., Yıldız, K., & Demir, Ö. (2026) · Journal of Naval Sciences and Engineering · doi:10.56850/jnse.1828189
Few-shot learning, a data-centric approach for adaptation
Lafargue, R. (2024) · theses.fr (ABES) · doi:10.70675/4f90d466z2cd1z4e11z9996z1615a464b603
India, G., Grayson, M., Morrison, C. P., Massiceti, D., Robinson, S., Pearson, J. S., & Jones, M. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791099
Self-citation
- Authors
- Massiceti, D., Zintgraf, L., Bronskill, J., Theodorou, L., Harris, M. T., Cutrell, E., Morrison, C., Hofmann, K., & Stumpf, S.
- Affiliations
- Microsoft (United States) (US)
- University of Oxford (GB)
- University of Cambridge (GB)
- City, University of London (GB)
- ORCID
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 10798-10808
- OpenAlex
- W3200699344
- Cited by
- 33
- References
- 55
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 5 · 2023: 10 · 2022: 7 · 2021: 2
- FWCI
- 2.77
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- Funders
- Microsoft
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- blind and visually impaired users, assistive technology, smartphone video, benchmark dataset
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