Kawtar Zaher

Seemingly innocuous augmentations like blur can cripple self-supervised learning for fine-grained tasks like plant identification, but domain-aware choices unlock surprisingly strong performance.

Pre-trained Vision Transformers can power effective human-in-the-loop object retrieval, but the devil's in the details: instance selection, annotation format, and active learning strategy all significantly impact performance.