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insight - Sign Language Recognition
Spatial-Temporal Part-aware Network for Isolated Sign Language Recognition
A new Spatial-Temporal Part-aware Network (StepNet) that effectively captures the fine-grained spatial and temporal cues in sign language videos without using any keypoint-level annotations.
TCNet: Continuous Sign Language Recognition from Trajectories and Correlated Regions
Efficiently capturing spatial interactions over time in sign language recognition using TCNet.
Continuous Sign Language Recognition Based on Motor Attention Mechanism and Frame-level Self-distillation
Proposing a novel motor attention mechanism and applying self-distillation to improve continuous sign language recognition.
Continuous Sign Language Recognition with Motor Attention Mechanism and Self-Distillation
The author proposes a novel motor attention mechanism to capture dynamic changes in sign language expressions, enhancing recognition accuracy. Additionally, the self-distillation method is applied to improve feature expression without increasing computational resources.
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