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Related Experiment Video

Updated: Apr 8, 2026

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

DynaPURLS: Dynamic Refinement of Part-Aware Representations for Skeleton-Based Zero-Shot Action Recognition.

Jingmin Zhu, Anqi Zhu, James Bailey

IEEE Transactions on Pattern Analysis and Machine Intelligence

|April 6, 2026

Summary

This summary is machine-generated.

This study introduces DynaPURLS, a novel framework for zero-shot skeleton-based action recognition (ZS-SAR). DynaPURLS enhances generalization by dynamically aligning fine-grained visual and semantic features, outperforming existing methods.

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Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Current zero-shot skeleton-based action recognition (ZS-SAR) methods struggle with domain shift due to coarse alignment.
This limitation hinders effective transfer of fine-grained visual knowledge between seen and unseen classes.

Purpose of the Study:

To introduce DynaPURLS, a unified framework for robust, multi-scale visual-semantic correspondences.
To dynamically refine these correspondences at inference for improved generalization in ZS-SAR.

Main Methods:

Leveraging large language models for hierarchical textual descriptions of actions.
Employing an adaptive partitioning module for fine-grained visual representations.
Utilizing a dynamic refinement module with a confidence-aware memory bank for robust alignment.

Main Results:

DynaPURLS establishes robust, multi-scale visual-semantic correspondences.
The framework dynamically refines these correspondences at inference time.
Significant performance improvements and new state-of-the-art records on benchmark datasets (NTU RGB+D 60/120, PKU-MMD).

Conclusions:

DynaPURLS effectively bridges the domain gap in ZS-SAR.
The proposed dynamic refinement and semantic grouping enhance generalization capabilities.
The framework offers a significant advancement in skeleton-based action recognition research.