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cs.CV2026
i-WiViG: Interpretable Window Vision GNN
Ivica Obadic, Dmitry Kangin, Adrian Höhl +3
Vision graph neural networks have emerged as a popular approach for modeling the global and spatial context for image recognition. However, a significant drawback of these methods…
cs.CV2024
IMAFD: An Interpretable Multi-stage Approach to Flood Detection from time series Multispectral Data
Ziyang Zhang, Plamen Angelov, Dmitry Kangin +1
In this paper, we address two critical challenges in the domain of flood detection: the computational expense of large-scale time series change detection and the lack of interpreta…
cs.CV2024
Unsupervised Domain Adaptation within Deep Foundation Latent Spaces
Dmitry Kangin, Plamen Angelov
The vision transformer-based foundation models, such as ViT or Dino-V2, are aimed at solving problems with little or no finetuning of features. Using a setting of prototypical netw…