Katja Hofmann, Luisa Zintgraf, John Bronskill, Daniela Massiceti, Cecily Morrison, Lida Theodorou, Matthew Harris, Edward Cutrell, Simone Stumpf
We lifted 3 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| microsoft/ORBIT-Dataset | canonical | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| compute_count_stats | Ran | microsoft/ORBIT-Dataset/scripts/summarize_dataset.py code served (permissive licence) · get_code("2ef27523a780b554") |
| compute_num_frames_stats | Ran | microsoft/ORBIT-Dataset/scripts/summarize_dataset.py code served (permissive licence) · get_code("cc73664701e186e6") |
| get_tallies_by_user | Ran | microsoft/ORBIT-Dataset/scripts/summarize_dataset.py code served (permissive licence) · get_code("ddd3828648e997a6") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
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.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2104.03841")
get_code_for_paper("2104.03841")
have("2104.03841")
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