7 citations · 23 across the 13 of their papers we have counts for
3 papers · 1 filter
An accurate detection is not all you need to combat label noise in web-noisy datasets
Paul Albert, Jack Valmadre, Eric Arazo +3
Training a classifier on web-crawled data demands learning algorithms that are robust to annotation errors and irrelevant examples. This paper builds upon the recent empirical obse…
Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero shot Medical Image Segmentation
Sidra Aleem, Fangyijie Wang, Mayug Maniparambil +6
The Segment Anything Model (SAM) and CLIP are remarkable vision foundation models (VFMs). SAM, a prompt driven segmentation model, excels in segmentation tasks across diverse domai…
ConvLoRA and AdaBN based Domain Adaptation via Self-Training
Sidra Aleem, Julia Dietlmeier, Eric Arazo +1
Existing domain adaptation (DA) methods often involve pre-training on the source domain and fine-tuning on the target domain. For multi-target domain adaptation, having a dedicated…