1 citations · 1 across the 5 of their papers we have counts for
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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…
Cluster Contrast for Unsupervised Visual Representation Learning
Nikos Giakoumoglou, Tania Stathaki
We introduce Cluster Contrast (CueCo), a novel approach to unsupervised visual representation learning that effectively combines the strengths of contrastive learning and clusterin…
Relational Representation Distillation
Nikos Giakoumoglou, Tania Stathaki
Knowledge distillation transfers knowledge from large teacher models to more compact student networks. The standard approach minimizes the Kullback-Leibler (KL) divergence between…
Caption-Matching: A Multimodal Approach for Cross-Domain Image Retrieval
Lucas Iijima, Nikos Giakoumoglou, Tania Stathaki
Cross-Domain Image Retrieval (CDIR) is a challenging task in computer vision, aiming to match images across different visual domains such as sketches, paintings, and photographs. E…