12 papers
Three Necessary Principles for Self-Supervised Visual Representation Learning
Nikos Giakoumoglou, Paschalis Giakoumoglou, Tania Stathaki
We argue that learning visual representations without labels requires a training signal jointly complete across three non-overlapping objectives: semantic invariance across augment…
Open-World Semantic Segmentation with Sensitivity Modeling
Anastasios Romanos Varvarigos, Nikos Giakoumoglou, Tania Stathaki
Modern vision systems must operate in "open-world" settings, where models must recognize known categories and detect unseen or anomalous content. Conventional semantic segmentation…
Caption-Matching: A Multimodal Approach for Cross-Domain Image Retrieval
Lucas Iijima, Nikolaos Giakoumoglou, Nikos Giakoumoglou +1
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…
Fake & Square: Training Self-Supervised Vision Transformers with Synthetic Data and Synthetic Hard Negatives
Nikolaos Giakoumoglou, Nikos Giakoumoglou, Andreas Floros +2
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
Nikolaos Giakoumoglou, Nikos Giakoumoglou, Andreas Floros +2
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
Nikolaos Giakoumoglou, 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…