4 citations · 4 across the 11 of their papers we have counts for
6 papers · 1 filter
Bigger Text Encoders Can Hurt CLIP Zero-Shot Performance
Samir Char, Carles Domingo-Enrich, Randall Balestriero
Contrastive Language-Image Pretraining (CLIP) is a building block of many machine learning applications. Scaling laws have guided resource allocation for large-scale training, yet…
LeVLJEPA: End-to-End Vision-Language Pretraining Without Negatives
Lukas Kuhn, Giuseppe Serra, Randall Balestriero +1
Vision-language pretraining remains dominated by contrastive objectives, whereas vision-only self-supervised learning has largely adopted non-contrastive methods. At the same time,…
Into the Rabbit Hull: From Task-Relevant Concepts in DINO to Minkowski Geometry
Thomas Fel, Binxu Wang, Michael A. Lepori +8
DINOv2 is routinely deployed to recognize objects, scenes, and actions; yet the nature of what it perceives remains unknown. As a working baseline, we adopt the Linear Representati…
DIET-CP: Lightweight and Data Efficient Self Supervised Continued Pretraining
Bryan Rodas, Natalie Montesino, Jakob Ambsdorf +2
Continued pretraining offers a promising solution for adapting foundation models to a new target domain. However, in specialized domains, available datasets are often very small, l…
FastDINOv2: Frequency Based Curriculum Learning Improves Robustness and Training Speed
Jiaqi Zhang, Juntuo Wang, Zhixin Sun +2
Large-scale vision foundation models such as DINOv2 boast impressive performances by leveraging massive architectures and training datasets. But numerous scenarios require practiti…
General Methods Make Great Domain-specific Foundation Models: A Case-study on Fetal Ultrasound
Jakob Ambsdorf, Asbjørn Munk, Sebastian Llambias +6
With access to large-scale, unlabeled medical datasets, researchers are confronted with two questions: Should they attempt to pretrain a custom foundation model on this medical dat…