Spatio-Temporal Joint Density Driven Learning for Skeleton-Based Action Recognition

• URL: http://arxiv.org/abs/2505.23012v1
• Date: Thu, 29 May 2025 02:40:47 GMT
• Title: Spatio-Temporal Joint Density Driven Learning for Skeleton-Based Action Recognition
• Authors: Shanaka Ramesh Gunasekara, Wanqing Li, Philip Ogunbona, Jack Yang,
• Abstract summary: This paper introduces a novel measurement, referred to as spatial-temporal joint density (STJD), to quantify such interaction.<n> Tracking the evolution of this density throughout an action can effectively identify a subset of discriminative moving and/or static joints.<n>A new contrastive learning strategy named STJD-CL is proposed to align the representation of a skeleton sequence with that of its prime joints.
• Score: 4.891381363264954
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Traditional approaches in unsupervised or self supervised learning for skeleton-based action classification have concentrated predominantly on the dynamic aspects of skeletal sequences. Yet, the intricate interaction between the moving and static elements of the skeleton presents a rarely tapped discriminative potential for action classification. This paper introduces a novel measurement, referred to as spatial-temporal joint density (STJD), to quantify such interaction. Tracking the evolution of this density throughout an action can effectively identify a subset of discriminative moving and/or static joints termed "prime joints" to steer self-supervised learning. A new contrastive learning strategy named STJD-CL is proposed to align the representation of a skeleton sequence with that of its prime joints while simultaneously contrasting the representations of prime and nonprime joints. In addition, a method called STJD-MP is developed by integrating it with a reconstruction-based framework for more effective learning. Experimental evaluations on the NTU RGB+D 60, NTU RGB+D 120, and PKUMMD datasets in various downstream tasks demonstrate that the proposed STJD-CL and STJD-MP improved performance, particularly by 3.5 and 3.6 percentage points over the state-of-the-art contrastive methods on the NTU RGB+D 120 dataset using X-sub and X-set evaluations, respectively.

Related papers

• Joint Temporal Pooling for Improving Skeleton-based Action Recognition [4.891381363264954]
  In skeleton-based human action recognition, temporal pooling is a critical step for capturing relationship of joint dynamics. This paper presents a novel Adaptive Joint Motion Temporal Pooling (MAP) method for improving skeleton-based action recognition. The efficacy of JMAP has been validated through experiments on the popular NTU RGBD+ 120 and PKU-MMD datasets.
  arXiv  Detail & Related papers  (2024-08-18T04:40:16Z)
• Skeleton2vec: A Self-supervised Learning Framework with Contextualized Target Representations for Skeleton Sequence [56.092059713922744]
  We show that using high-level contextualized features as prediction targets can achieve superior performance. Specifically, we propose Skeleton2vec, a simple and efficient self-supervised 3D action representation learning framework. Our proposed Skeleton2vec outperforms previous methods and achieves state-of-the-art results.
  arXiv  Detail & Related papers  (2024-01-01T12:08:35Z)
• SCD-Net: Spatiotemporal Clues Disentanglement Network for Self-supervised Skeleton-based Action Recognition [39.99711066167837]
  This paper introduces a contrastive learning framework, namely Stemporal Clues Disentanglement Network (SCD-Net) Specifically, we integrate the sequences with a feature extractor to derive explicit clues from spatial and temporal domains respectively. We conduct evaluations on the NTU-+D (60&120) PKU-MMDI (&I) datasets, covering various downstream tasks such as action recognition, action retrieval, transfer learning.
  arXiv  Detail & Related papers  (2023-09-11T21:32:13Z)
• Self-supervised Action Representation Learning from Partial Spatio-Temporal Skeleton Sequences [29.376328807860993]
  We propose a Partial Spatio-Temporal Learning (PSTL) framework to exploit the local relationship between different skeleton joints and video frames. Our method achieves state-of-the-art performance on NTURGB+D 60, NTURGBMM+D 120 and PKU-D under various downstream tasks.
  arXiv  Detail & Related papers  (2023-02-17T17:35:05Z)
• Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action Recognition [88.34182299496074]
  Action labels are only available on a source dataset, but unavailable on a target dataset in the training stage. We utilize a self-supervision scheme to reduce the domain shift between two skeleton-based action datasets. By segmenting and permuting temporal segments or human body parts, we design two self-supervised learning classification tasks.
  arXiv  Detail & Related papers  (2022-07-17T07:05:39Z)
• SpatioTemporal Focus for Skeleton-based Action Recognition [66.8571926307011]
  Graph convolutional networks (GCNs) are widely adopted in skeleton-based action recognition. We argue that the performance of recent proposed skeleton-based action recognition methods is limited by the following factors. Inspired by the recent attention mechanism, we propose a multi-grain contextual focus module, termed MCF, to capture the action associated relation information.
  arXiv  Detail & Related papers  (2022-03-31T02:45:24Z)
• Joint-bone Fusion Graph Convolutional Network for Semi-supervised Skeleton Action Recognition [65.78703941973183]
  We propose a novel correlation-driven joint-bone fusion graph convolutional network (CD-JBF-GCN) as an encoder and use a pose prediction head as a decoder. Specifically, the CD-JBF-GC can explore the motion transmission between the joint stream and the bone stream. The pose prediction based auto-encoder in the self-supervised training stage allows the network to learn motion representation from unlabeled data.
  arXiv  Detail & Related papers  (2022-02-08T16:03:15Z)
• Contrast-reconstruction Representation Learning for Self-supervised Skeleton-based Action Recognition [18.667198945509114]
  We propose a novel Contrast-Reconstruction Representation Learning network (CRRL) It simultaneously captures postures and motion dynamics for unsupervised skeleton-based action recognition. Experimental results on several benchmarks, i.e., NTU RGB+D 60, NTU RGB+D 120, CMU mocap, and NW-UCLA, demonstrate the promise of the proposed CRRL method.
  arXiv  Detail & Related papers  (2021-11-22T08:45:34Z)
• 3D Human Action Representation Learning via Cross-View Consistency Pursuit [52.19199260960558]
  We propose a Cross-view Contrastive Learning framework for unsupervised 3D skeleton-based action Representation (CrosSCLR) CrosSCLR consists of both single-view contrastive learning (SkeletonCLR) and cross-view consistent knowledge mining (CVC-KM) modules, integrated in a collaborative learning manner.
  arXiv  Detail & Related papers  (2021-04-29T16:29:41Z)
• JOLO-GCN: Mining Joint-Centered Light-Weight Information for Skeleton-Based Action Recognition [47.47099206295254]
  We propose a novel framework for employing human pose skeleton and joint-centered light-weight information jointly in a two-stream graph convolutional network. Compared to the pure skeleton-based baseline, this hybrid scheme effectively boosts performance, while keeping the computational and memory overheads low.
  arXiv  Detail & Related papers  (2020-11-16T08:39:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.