Transformer heads and motion gates offer no gains for CorrNet CSLR

A new empirical study investigates enhancements to the CorrNet model for continuous sign language recognition (CSLR). Researchers found that replacing the BiLSTM temporal head with a Transformer encoder did not improve performance and had similar computational costs. Additionally, a proposed MotionGate module, designed to inject motion cues, consistently defaulted to an identity mapping, indicating redundancy with CorrNet's existing correlation-based encoding. AI

IMPACT Suggests that architectural extensions for CSLR should be carefully tested rather than assumed to be beneficial.

RANK_REASON Academic paper detailing empirical study of model enhancements. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• BiLSTM
• MotionGate
• Transformer

• paper
• other

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