activity
20242026
most citedAdvancing Semantic Future Prediction through Multimodal Visual Sequence Transformers

1 citations · 1 across the 2 of their papers we have counts for

collaborators

6 papers

cs.CV2026

Benchmarking Composed Image Retrieval for Applied Earth Observation

Bill Psomas, Dionysis Christopoulos, Thanasis Petropoulos +6

Remote sensing composed image retrieval (RSCIR) enables search in large satellite image archives using composed queries that combine a reference image with a textual modifier. Alth…

cs.CV2025

DINO-Foresight: Looking into the Future with DINO

Efstathios Karypidis, Ioannis Kakogeorgiou, Spyros Gidaris +1

Predicting future dynamics is crucial for applications like autonomous driving and robotics, where understanding the environment is key. Existing pixel-level methods are computatio…

cs.CV20251 cited

Advancing Semantic Future Prediction through Multimodal Visual Sequence Transformers

Efstathios Karypidis, Ioannis Kakogeorgiou, Spyros Gidaris +1

Semantic future prediction is important for autonomous systems navigating dynamic environments. This paper introduces FUTURIST, a method for multimodal future semantic prediction t…

cs.LG2025

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…

cs.CV2024

Composed Image Retrieval for Remote Sensing

Bill Psomas, Ioannis Kakogeorgiou, Nikos Efthymiadis +4

This work introduces composed image retrieval to remote sensing. It allows to query a large image archive by image examples alternated by a textual description, enriching the descr…

cs.CV2024

SPOT: Self-Training with Patch-Order Permutation for Object-Centric Learning with Autoregressive Transformers

Ioannis Kakogeorgiou, Spyros Gidaris, Konstantinos Karantzalos +1

Unsupervised object-centric learning aims to decompose scenes into interpretable object entities, termed slots. Slot-based auto-encoders stand out as a prominent method for this ta…