STARK: Spatio-Temporal Attention for Representation of Keypoints for Continuous Sign Language Recognition
STARK: Spatio-Temporal Attention for Representation of Keypoints for Continuous Sign Language Recognition explores A unified spatio-temporal attention network for efficient continuous sign language recognition.. Commercial viability score: 4/10 in Sign Language Recognition.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $10K - $14K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Prior WorkHLA: Hadamard Linear AttentionViability: 4.0
- Higher ViabilityLow-latency Event-based Object Detection with Spatially-Sparse Linear AttentionViability: 7.0
- Higher ViabilitySignSparK: Efficient Multilingual Sign Language Production via Sparse Keyframe LearningViability: 8.0
- Higher ViabilityGeometry-Aware Metric Learning for Cross-Lingual Few-Shot Sign Language Recognition on Static Hand KeypointsViability: 7.0
- Higher ViabilityDynamic LIBRAS Gesture Recognition via CNN over Spatiotemporal Matrix Representation