2 citations · 2 across the 4 of their papers we have counts for
5 papers
What Makes Synthetic Hard Negatives Work in Vision-Language Pretraining?
Nikos Giakoumoglou, Paschalis Giakoumoglou, Andreas Floros +2
Synthetic hard negatives generated in the representation space have proven effective for unimodal self-supervised learning, but transferring this idea to vision-language pretrainin…
ViTAMINS: An Empirical Study of Training Self-Supervised Vision Transformers with Synthetic Hard Negatives
Nikos Giakoumoglou, Andreas Floros, Kleanthis-Marios Papadopoulos +1
We introduce ViTAMINS, a method that integrates synthetic hard negatives into unsupervised vision transformer pretraining to improve representation quality. Our approach is thoroug…
Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives
Nikos Giakoumoglou, Andreas Floros, Kleanthis Marios Papadopoulos +1
This paper does not introduce a new method per se. Instead, we build on existing self-supervised learning approaches for vision, drawing inspiration from the adage "fake it till yo…
Unsupervised Training of Vision Transformers with Synthetic Negatives
Nikos Giakoumoglou, Andreas Floros, Kleanthis Marios Papadopoulos +1
This paper does not introduce a novel method per se. Instead, we address the neglected potential of hard negative samples in self-supervised learning. Previous works explored synth…
Comparing ImageNet Pre-training with Digital Pathology Foundation Models for Whole Slide Image-Based Survival Analysis
Kleanthis Marios Papadopoulos, Tania Stathaki
The abundance of information present in Whole Slide Images (WSIs) renders them an essential tool for survival analysis. Several Multiple Instance Learning frameworks proposed for t…