Sign Embeddings

Sign embeddings represent visual signs as numerical vectors, aiming to capture the semantic meaning and visual characteristics of sign language gestures for improved machine understanding. Current research focuses on developing robust and bias-mitigated models, often employing deep learning architectures like convolutional neural networks and transformers, to address challenges such as limited datasets, cross-lingual transfer, and the need for signer-independent recognition. This work is crucial for advancing sign language processing technologies, improving accessibility for deaf and hard-of-hearing communities, and enabling applications like sign language translation and recognition systems.