Leverage synthetic data with unlabeled real data

Demonstrate how labeled synthetic data can be effectively combined with unlabeled real data—via unsupervised domain adaptation—to train accurate egocentric hand–object interaction detectors.

Background

Unsupervised domain adaptation (UDA) is critical when real data cannot be labeled. The paper explores teacher–student adaptation and adversarial alignment to transfer knowledge from synthetic to real domains.

Establishing effective synth-to-real UDA protocols would enable broader application of HOI detection without incurring annotation costs.

References

As a result, several key open questions still need to be addressed: 1) How large is the gap between synthetic and real data? 2) What are its main causes? 3) How can it be minimized? 4) Can synthetic data fully replace real-world data? 5) Is it possible to leverage synthetic data when real-world data is unlabeled?

— Leveraging Synthetic Data for Enhancing Egocentric Hand-Object Interaction Detection  (2603.29733 - Leonardi et al., 31 Mar 2026) in Section 1 (Introduction)