Reproduce “C${^2}$RL: Content and Context Representation Learning for Gloss-free Sign Language Translation and Retrieval” on OpenResearch
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About this paper
Zhigang Chen, Benjia Zhou, Yiqing Huang, Jun Wan, Yibo Hu, Hailin Shi, Yanyan Liang, Zhen Lei and 1 more
C²RL proposes a gloss-free pretraining paradigm for sign language representation learning, outperforming prior methods in sign language translation and retrieval. This system achieved a 26.75 BLEU-4 score on PHOENIX-2014T, a 21.61 BLEU-4 score on CSL-Daily, and notable gains in retrieval accuracy, narrowing the performance gap with gloss-based approaches by up to 67.8% on BLEU-4.
Key insights
- Jointly learning both implicit content (fine-grained visual-text alignment) and explicit context (overall semantic understanding for sentence generation) is crucial for robust gloss-free sign language representation.
- The combined optimization of contrastive learning (ICL) and sequence-to-sequence language modeling (ECL) effectively addresses the challenges of non-monotonic video-text alignment in sign language.
- The learned sign language representations are highly robust and adaptable, demonstrating versatility across different datasets and compatibility with various language model backbones.
Summary by alphaXiv.
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