Hand Sign Language Recognition System
Investigating how to effectively combine simulated and real data for robust perception under domain shift.
Highlights
- Exploring the use of Gated Multimodal Units (GMU) to fuse features from simulated and real data, aiming to improve robustness under domain shift.
- Preliminary results show that multimodal fusion can enhance performance on target domain tasks by leveraging complementary information from both domains.
- Future work includes systematic evaluation of fusion strategies and their impact on generalization across diverse sim-to-real scenarios.
Demo
Next: embed a Hugging Face Space here (or custom inference UI).