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Found 8 papers, 8 papers with code
CorrNet+: Sign Language Recognition and Translation via Spatial-Temporal Correlation
In specific, CorrNet+ employs a correlation module and an identification module to build human body trajectories.
Improving Continuous Sign Language Recognition with Adapted Image Models
Besides, fully fine-tuning the model easily forgets the generic essential knowledge acquired in the pretraining stage and overfits the downstream data.
Dynamic Spatial-Temporal Aggregation for Skeleton-Aware Sign Language Recognition
Current methods utilize spatial graph modules and temporal modules to capture spatial and temporal features, respectively.
COMMA: Co-Articulated Multi-Modal Learning
First, the prompts of the vision and language branches in these methods are usually separated or uni-directionally correlated.
AdaBrowse: Adaptive Video Browser for Efficient Continuous Sign Language Recognition
Then these features are fed into a policy network to intelligently select a subsequence to process.
Ranked #7 on
Sign Language Recognition
on CSL-Daily
Continuous Sign Language Recognition with Correlation Network
Visualizations demonstrate the effects of CorrNet on emphasizing human body trajectories across adjacent frames.
Self-Emphasizing Network for Continuous Sign Language Recognition
To relieve this problem, we propose a self-emphasizing network (SEN) to emphasize informative spatial regions in a self-motivated way, with few extra computations and without additional expensive supervision.
Ranked #8 on
Sign Language Recognition
on CSL-Daily
Temporal Lift Pooling for Continuous Sign Language Recognition
In this paper, we derive temporal lift pooling (TLP) from the Lifting Scheme in signal processing to intelligently downsample features of different temporal hierarchies.
