3 accepted papers
Class incremental semantic segmentation (CISS) aims to segment new classes during continual steps while preventing the forgetting of old knowledge. Existing methods alleviate catastrophic forgetting by replaying distributions of previously learned classes using stored prototypes or features. However…
Class incremental semantic segmentation (CISS) enables a model to continually segment new classes from non-stationary data while preserving previously learned knowledge. Recent top-performing approaches are prototype-based methods that assign a prototype to each learned class to reproduce previous k…
Scene text image super-resolution (STISR) focuses on enhancing the clarity and readability of low-resolution text images. Existing methods often rely on text probability distribution priors derived from text recognizers to guide the super-resolution process. While effective in capturing general stru…