most cited3D Skeleton-based Few-shot Action Recognition with JEANIE is not so Naïve

7 citations · 11 across the 3 of their papers we have counts for

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cs.CV2025

Feature Hallucination for Self-supervised Action Recognition

Lei Wang, Piotr Koniusz

Understanding human actions in videos requires more than raw pixel analysis; it relies on high-level semantic reasoning and effective integration of multimodal features. We propose…

cs.CV2025

AP-CAP: Advancing High-Quality Data Synthesis for Animal Pose Estimation via a Controllable Image Generation Pipeline

Lei Wang, Yujie Zhong, Xiaopeng Sun +5

The task of 2D animal pose estimation plays a crucial role in advancing deep learning applications in animal behavior analysis and ecological research. Despite notable progress in…

cs.CV20248 cited

Do Language Models Understand Time?

Xi Ding, Lei Wang

Large language models (LLMs) have revolutionized video-based computer vision applications, including action recognition, anomaly detection, and video summarization. Videos inherent…

cs.CV2024

When Spatial meets Temporal in Action Recognition

Huilin Chen, Lei Wang, Yifan Chen +2

Video action recognition has made significant strides, but challenges remain in effectively using both spatial and temporal information. While existing methods often focus on eithe…

cs.CV20231 cited

3Mformer: Multi-order Multi-mode Transformer for Skeletal Action Recognition

Lei Wang, Piotr Koniusz

Many skeletal action recognition models use GCNs to represent the human body by 3D body joints connected body parts. GCNs aggregate one- or few-hop graph neighbourhoods, and ignore…

cs.CV20217 cited

3D Skeleton-based Few-shot Action Recognition with JEANIE is not so Naïve

Lei Wang, Jun Liu, Piotr Koniusz

In this paper, we propose a Few-shot Learning pipeline for 3D skeleton-based action recognition by Joint tEmporal and cAmera viewpoiNt alIgnmEnt (JEANIE). To factor out misalignmen…