2 citations · 4 across the 6 of their papers we have counts for
8 papers
Coevolving Representations in Joint Image-Feature Diffusion
Theodoros Kouzelis, Spyros Gidaris, Nikos Komodakis
Joint image-feature generative modeling has recently emerged as an effective strategy for improving diffusion training by coupling low-level VAE latents with high-level semantic fe…
Boosting Generative Image Modeling via Joint Image-Feature Synthesis
Theodoros Kouzelis, Efstathios Karypidis, Ioannis Kakogeorgiou +2
Latent diffusion models (LDMs) dominate high-quality image generation, yet integrating representation learning with generative modeling remains a challenge. We introduce a novel ge…
EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling
Theodoros Kouzelis, Ioannis Kakogeorgiou, Spyros Gidaris +1
Latent generative models have emerged as a leading approach for high-quality image synthesis. These models rely on an autoencoder to compress images into a latent space, followed b…
Enabling Local Editing in Diffusion Models by Joint and Individual Component Analysis
Theodoros Kouzelis, Manos Plitsis, Mihalis A. Nicolaou +1
Recent advances in Diffusion Models (DMs) have led to significant progress in visual synthesis and editing tasks, establishing them as a strong competitor to Generative Adversarial…
Weakly-supervised Automated Audio Captioning via text only training
Theodoros Kouzelis, Vassilis Katsouros
In recent years, datasets of paired audio and captions have enabled remarkable success in automatically generating descriptions for audio clips, namely Automated Audio Captioning (…
Investigating Personalization Methods in Text to Music Generation
Manos Plitsis, Theodoros Kouzelis, Georgios Paraskevopoulos +2
In this work, we investigate the personalization of text-to-music diffusion models in a few-shot setting. Motivated by recent advances in the computer vision domain, we are the fir…
Weakly-supervised forced alignment of disfluent speech using phoneme-level modeling
Theodoros Kouzelis, Georgios Paraskevopoulos, Athanasios Katsamanis +1
The study of speech disorders can benefit greatly from time-aligned data. However, audio-text mismatches in disfluent speech cause rapid performance degradation for modern speech a…
Sample-Efficient Unsupervised Domain Adaptation of Speech Recognition Systems A case study for Modern Greek
Georgios Paraskevopoulos, Theodoros Kouzelis, Georgios Rouvalis +3
Modern speech recognition systems exhibits rapid performance degradation under domain shift. This issue is especially prevalent in data-scarce settings, such as low-resource langua…