no code implementations • 7 Mar 2024 • Evelyn Mannix, Howard Bondell
We demonstrate that ComFe obtains higher accuracy compared to previous interpretable models across a range of fine-grained vision benchmarks, without the need to individually tune hyper-parameters for each dataset.
no code implementations • 23 Jan 2024 • Robert Turnbull, Evelyn Mannix
Results: Our submission won the recognition challenge with a mAP of 42. 2%, and was runner-up in the detection challenge with a mean average precision (mAP) of 51. 4%.
no code implementations • 28 Nov 2023 • Evelyn Mannix, Howard Bondell
This paper describes PAWS-VMK, an improved approach to prototypical semi-supervised learning in the field of computer vision, specifically designed to utilize a frozen foundation model as the neural network backbone.