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Scalable and Robust Transformer Decoders for Interpretable Image Classification with Foundation Models
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.
Detecting and recognizing characters in Greek papyri with YOLOv8, DeiT and SimCLR
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%.
Efficient Out-of-Distribution Detection with Prototypical Semi-Supervised Learning and Foundation Models
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.
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