17 citations · 28 across the 2 of their papers we have counts for
7 papers
Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning
Joseph Paul Cohen, Lan Dao, Paul Morrison +8
Purpose: The need to streamline patient management for COVID-19 has become more pressing than ever. Chest X-rays provide a non-invasive (potentially bedside) tool to monitor the pr…
DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning
Timo Milbich, Karsten Roth, Homanga Bharadhwaj +4
Visual Similarity plays an important role in many computer vision applications. Deep metric learning (DML) is a powerful framework for learning such similarities which not only gen…
PADS: Policy-Adapted Sampling for Visual Similarity Learning
Karsten Roth, Timo Milbich, Björn Ommer
Learning visual similarity requires to learn relations, typically between triplets of images. Albeit triplet approaches being powerful, their computational complexity mostly limits…
Revisiting Training Strategies and Generalization Performance in Deep Metric Learning
Karsten Roth, Timo Milbich, Samarth Sinha +3
Deep Metric Learning (DML) is arguably one of the most influential lines of research for learning visual similarities with many proposed approaches every year. Although the field b…
MIC: Mining Interclass Characteristics for Improved Metric Learning
Karsten Roth, Biagio Brattoli, Björn Ommer
Metric learning seeks to embed images of objects suchthat class-defined relations are captured by the embeddingspace. However, variability in images is not just due to different de…
Mask Mining for Improved Liver Lesion Segmentation
Karsten Roth, Jürgen Hesser, Tomasz Konopczyński
We propose a novel procedure to improve liver and lesion segmentation from CT scans for U-Net based models. Our method extends standard segmentation pipelines to focus on higher ta…