Learnt Contrastive Concept Embeddings for Sign Recognition
Ryan Wong Necati Cihan Camgoz Richard Bowden
In natural language processing (NLP) of spoken languages, word embeddings
have been shown to be a useful method to encode the meaning of words. Sign
languages are visual languages, which require sign embeddings to capture the
visual and linguistic semantics of sign. Unlike many common approaches to Sign
Recognition, we focus on explicitly creating sign embeddings that bridge the
gap between sign language and spoken language. We propose a learning framework
to derive LCC (Learnt Contrastive Concept) embeddings for sign language, a
weakly supervised contrastive approach to learning sign embeddings. We train a
vocabulary of embeddings that are based on the linguistic labels for sign
video. Additionally, we develop a conceptual similarity loss which is able to
utilise word embeddings from NLP methods to create sign embeddings that have
better sign language to spoken language correspondence. These learnt
representations allow the model to automatically localise the sign in time. Our
approach achieves state-of-the-art keypoint-based sign recognition performance
on the WLASL and BOBSL datasets.