VLPose: Bridging the Domain Gap in Pose Estimation With Language-Vision Tuning.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Deep Learning

Background:

• Deep learning has advanced Human Pose Estimation (HPE) in natural settings.
• HPE models struggle with artificial scenarios (e.g., art) due to a domain gap, hindering VR/AR applications.
• Retraining large models on diverse data is computationally expensive.

Purpose of the Study:

• To bridge the domain gap between natural and artificial scenarios for HPE.
• To develop efficient tuning strategies for adapting HPE models.
• To enhance the generalization and robustness of HPE models using language-vision synergy.

Main Methods:

• Introduced VLPose, a novel framework leveraging language models.
• Enhanced traditional pose estimation models by integrating language and vision.
• Employed efficient tuning strategies to adapt models across domains.

Main Results:

• Achieved improvements of 2.26% on the HumanArt dataset.
• Demonstrated a 3.74% improvement on the MSCOCO dataset.
• Outperformed state-of-the-art tuning strategies in cross-domain adaptation.

Conclusions:

• VLPose effectively bridges the domain gap in human pose estimation.
• The framework enhances HPE model adaptability and performance in diverse scenarios.
• Efficient tuning strategies combined with language-vision synergy offer a scalable solution for HPE.