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ComFe: Interpretable Image Classifiers With Foundation Models, Transformers and Component Features
Interpretable computer vision models are able to explain their reasoning through comparing the distances between the image patch embeddings and prototypes within a latent space.
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%.
PAWS-VMK: A Unified Approach To Semi-Supervised Learning And Out-of-Distribution Detection
This paper describes PAWS-VMK, a prototypical deep learning approach that obtains state-of-the-art results for image classification tasks in both a semi-supervised learning (SSL) and out-of-distribution (OOD) detection context.
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