Spatio-Temporal Joint Density Driven Learning for Skeleton-Based Action Recognition Article Swipe
Shanaka Ramesh Gunasekara
,
Wanqing Li
,
Philip Ogunbona
,
Jie Yang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/tbiom.2025.3566212
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/tbiom.2025.3566212
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tbiom.2025.3566212
- OA Status
- green
- References
- 44
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4410027873Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tbiom.2025.3566212Digital Object Identifier
- Title
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Spatio-Temporal Joint Density Driven Learning for Skeleton-Based Action RecognitionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-05-02Full publication date if available
- Authors
-
Shanaka Ramesh Gunasekara, Wanqing Li, Philip Ogunbona, Jie YangList of authors in order
- Landing page
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https://doi.org/10.1109/tbiom.2025.3566212Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2505.23012Direct OA link when available
- Concepts
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Skeleton (computer programming), Joint (building), Action recognition, Artificial intelligence, Computer science, Action (physics), Pattern recognition (psychology), Human skeleton, Engineering, Physics, Class (philosophy), Architectural engineering, Quantum mechanics, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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44Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.in | 2, 153 |
| abstract_inverted_index.is | 95, 127 |
| abstract_inverted_index.it | 131 |
| abstract_inverted_index.of | 19, 32, 64, 75, 101, 107, 116 |
| abstract_inverted_index.on | 15, 142, 178 |
| abstract_inverted_index.or | 4 |
| abstract_inverted_index.to | 51, 57, 84, 97 |
| abstract_inverted_index.120 | 182 |
| abstract_inverted_index.3.5 | 168 |
| abstract_inverted_index.3.6 | 170 |
| abstract_inverted_index.60, | 146 |
| abstract_inverted_index.NTU | 144, 147, 180 |
| abstract_inverted_index.and | 29, 118, 150, 162, 169, 186 |
| abstract_inverted_index.can | 70 |
| abstract_inverted_index.for | 8, 41, 136 |
| abstract_inverted_index.its | 108 |
| abstract_inverted_index.new | 89 |
| abstract_inverted_index.the | 16, 23, 27, 33, 62, 99, 114, 143, 159, 174, 179 |
| abstract_inverted_index.120, | 149 |
| abstract_inverted_index.This | 44 |
| abstract_inverted_index.Yet, | 22 |
| abstract_inverted_index.have | 12 |
| abstract_inverted_index.more | 137 |
| abstract_inverted_index.over | 173 |
| abstract_inverted_index.self | 5 |
| abstract_inverted_index.such | 59 |
| abstract_inverted_index.that | 106, 158 |
| abstract_inverted_index.this | 65 |
| abstract_inverted_index.with | 105, 132 |
| abstract_inverted_index.RGB+D | 145, 148, 181 |
| abstract_inverted_index.X-set | 187 |
| abstract_inverted_index.X-sub | 185 |
| abstract_inverted_index.align | 98 |
| abstract_inverted_index.joint | 54 |
| abstract_inverted_index.named | 93 |
| abstract_inverted_index.novel | 48 |
| abstract_inverted_index.paper | 45 |
| abstract_inverted_index.prime | 109, 117 |
| abstract_inverted_index.steer | 85 |
| abstract_inverted_index.tasks | 156 |
| abstract_inverted_index.using | 184 |
| abstract_inverted_index.while | 111 |
| abstract_inverted_index."prime | 82 |
| abstract_inverted_index.PKUMMD | 151 |
| abstract_inverted_index.action | 10, 42, 69 |
| abstract_inverted_index.and/or | 78 |
| abstract_inverted_index.called | 125 |
| abstract_inverted_index.joints | 80, 110 |
| abstract_inverted_index.method | 124 |
| abstract_inverted_index.moving | 28, 77 |
| abstract_inverted_index.points | 172 |
| abstract_inverted_index.rarely | 37 |
| abstract_inverted_index.static | 30, 79 |
| abstract_inverted_index.subset | 74 |
| abstract_inverted_index.tapped | 38 |
| abstract_inverted_index.termed | 81 |
| abstract_inverted_index.(STJD), | 56 |
| abstract_inverted_index.STJD-CL | 94, 161 |
| abstract_inverted_index.STJD-MP | 126, 163 |
| abstract_inverted_index.aspects | 18 |
| abstract_inverted_index.between | 26 |
| abstract_inverted_index.dataset | 183 |
| abstract_inverted_index.density | 55, 66 |
| abstract_inverted_index.dynamic | 17 |
| abstract_inverted_index.joints" | 83 |
| abstract_inverted_index.joints. | 120 |
| abstract_inverted_index.methods | 177 |
| abstract_inverted_index.various | 154 |
| abstract_inverted_index.Tracking | 61 |
| abstract_inverted_index.datasets | 152 |
| abstract_inverted_index.elements | 31 |
| abstract_inverted_index.identify | 72 |
| abstract_inverted_index.improved | 164 |
| abstract_inverted_index.learning | 7, 91 |
| abstract_inverted_index.nonprime | 119 |
| abstract_inverted_index.presents | 35 |
| abstract_inverted_index.proposed | 96, 160 |
| abstract_inverted_index.quantify | 58 |
| abstract_inverted_index.referred | 50 |
| abstract_inverted_index.sequence | 104 |
| abstract_inverted_index.skeletal | 20 |
| abstract_inverted_index.skeleton | 34, 103 |
| abstract_inverted_index.strategy | 92 |
| abstract_inverted_index.addition, | 122 |
| abstract_inverted_index.developed | 128 |
| abstract_inverted_index.effective | 138 |
| abstract_inverted_index.evolution | 63 |
| abstract_inverted_index.framework | 135 |
| abstract_inverted_index.intricate | 24 |
| abstract_inverted_index.learning. | 87, 139 |
| abstract_inverted_index.potential | 40 |
| abstract_inverted_index.approaches | 1 |
| abstract_inverted_index.downstream | 155 |
| abstract_inverted_index.introduces | 46 |
| abstract_inverted_index.percentage | 171 |
| abstract_inverted_index.sequences. | 21 |
| abstract_inverted_index.supervised | 6 |
| abstract_inverted_index.throughout | 67 |
| abstract_inverted_index.Traditional | 0 |
| abstract_inverted_index.contrasting | 113 |
| abstract_inverted_index.contrastive | 90, 176 |
| abstract_inverted_index.demonstrate | 157 |
| abstract_inverted_index.effectively | 71 |
| abstract_inverted_index.evaluations | 141 |
| abstract_inverted_index.integrating | 130 |
| abstract_inverted_index.interaction | 25 |
| abstract_inverted_index.Experimental | 140 |
| abstract_inverted_index.concentrated | 13 |
| abstract_inverted_index.evaluations, | 188 |
| abstract_inverted_index.interaction. | 60 |
| abstract_inverted_index.measurement, | 49 |
| abstract_inverted_index.particularly | 166 |
| abstract_inverted_index.performance, | 165 |
| abstract_inverted_index.unsupervised | 3 |
| abstract_inverted_index.predominantly | 14 |
| abstract_inverted_index.respectively. | 189 |
| abstract_inverted_index.classification | 11 |
| abstract_inverted_index.discriminative | 39, 76 |
| abstract_inverted_index.representation | 100 |
| abstract_inverted_index.simultaneously | 112 |
| abstract_inverted_index.skeleton-based | 9 |
| abstract_inverted_index.classification. | 43 |
| abstract_inverted_index.representations | 115 |
| abstract_inverted_index.self-supervised | 86 |
| abstract_inverted_index.spatial-temporal | 53 |
| abstract_inverted_index.state-of-the-art | 175 |
| abstract_inverted_index.reconstruction-based | 134 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.14289349 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |