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Paschalis Giakoumoglou

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#artificial intelligence Preprint Oct 2026

What Makes Synthetic Hard Negatives Work in Vision-Language Pretraining?

Synthetic hard negatives generated in the representation space have proven effective for unimodal self-supervised learning, but transferring this idea to vision-language pretraining is not straightforward. We analyze six representation-space synthesis strategies and identify two failure modes in their transfer to visio...

Nikos Giakoumoglou, Paschalis Giakoumoglou, Andreas Floros et al. · 0 citations

#artificial intelligence Preprint Oct 2026

Harmful Content Generation in Text-to-Image Models: Capabilities and Moderation Limitations

Text-to-image generative models can produce highly realistic imagery but also raise concerns about harmful misuse. While safety mechanisms exist, systematic evaluations of their effectiveness against realistic attacks remain limited. We present a systematic evaluation of harmful content generation across five open text...

Paschalis Giakoumoglou, Manos Schinas, Symeon Papadopoulos · 0 citations

#machine learning Preprint Aug 2026

Three Necessary Principles for Self-Supervised Visual Representation Learning

We argue that learning visual representations without labels requires a training signal jointly complete across three non-overlapping objectives: semantic invariance across augmented views, patch-level spatial prediction, and representational non-degeneracy. We formalize these as the observation, prediction, and regula...

Nikos Giakoumoglou, Paschalis Giakoumoglou, Tania Stathaki · 0 citations