Computer Vision and Image Understanding (CVIU), 2026, 2026

Sign language translation systems traditionally rely on intermediate gloss representations to bridge the gap between visual input and written language output. However, manual gloss annotation is costly, language-dependent, and often lossy, prompting growing interest in gloss-free alternatives. This paper introduces , a novel two-stage framework for gloss-free sign language translation and gloss sequence generation. first translates continuous sign language videos into written language sentences using a lightweight decoder built atop SlowFast-based spatiotemporal features and a frozen mBART model. Then, in the second stage, it generates gloss sequences from these sentences using a Large Language Model (LLaMa3.1-8B-Instruct) that has been fine-tuned with weak supervision. Our experiments on PHOENIX-2014-T and Wav2Gloss Fieldwork demonstrate strong translation performance and state-of-the-art multilingual gloss generation, even in zero-shot settings. The proposed framework reduces annotation bottlenecks while maintaining flexibility and interpretability, paving the way for scalable and inclusive sign language technologies. The code and fine-tuning scripts are available at https://github.com/colonnaemanuele/Handscribe.

European Conference on Artificial Intelligence (ECAI), 2025, 2025

We present Label Anything, an innovative neural network architecture designed for few-shot semantic segmentation (FSS) that demonstrates remarkable generalizability across multiple classes with minimal examples required per class.

Eight Workshop on Natural Language for Artificial Intelligence (NL4AI 2024) @AIxIA 2024, 2024

This paper introduces an early exploration of Text-to-LIS, a new model designed to generate contextually accurate Italian Sign Language (LIS) gestures for digital humans.