Cross-View Correspondence Modeling for Joint Representation Learning Between Egocentric and Exocentric Videos Article Swipe
Zhehao Zhu
,
Yoichi Sato
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3593474
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3593474
Joint analysis of human action videos from egocentric and exocentric views enables a more comprehensive understanding of human behavior. While previous works leverage paired videos to align clip-level features across views, they often ignore the complex spatial and temporal misalignments inherent in such data. In this work, we propose a Cross-View Transformer that explicitly models fine-grained spatiotemporal correspondence between egocentric and exocentric videos. Our model incorporates self-attention to enhance intra-view context and cross-view attention to align features across space and time. To train the model, we introduce a hybrid loss function combining a triplet loss and a domain classification loss, further reinforced by a sample screening mechanism that emphasizes informative training pairs. We evaluate our method on multiple egocentric action recognition benchmarks, including Charades-Ego and EPIC-Kitchens. Experimental results demonstrate that our method consistently outperforms existing approaches, achieving state-of-the-art performance on several egocentric video understanding tasks.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2025.3593474
- OA Status
- gold
- References
- 50
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412721953
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4412721953Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2025.3593474Digital Object Identifier
- Title
-
Cross-View Correspondence Modeling for Joint Representation Learning Between Egocentric and Exocentric VideosWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-01Full publication date if available
- Authors
-
Zhehao Zhu, Yoichi SatoList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2025.3593474Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1109/access.2025.3593474Direct OA link when available
- Concepts
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Endocentric and exocentric, Computer science, Representation (politics), Artificial intelligence, Joint (building), Human–computer interaction, Computer vision, Natural language processing, Engineering, Political science, Politics, Law, Architectural engineering, Noun phrase, NounTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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50Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.evaluate | 113 |
| abstract_inverted_index.existing | 134 |
| abstract_inverted_index.features | 28, 76 |
| abstract_inverted_index.function | 90 |
| abstract_inverted_index.inherent | 40 |
| abstract_inverted_index.leverage | 22 |
| abstract_inverted_index.multiple | 117 |
| abstract_inverted_index.previous | 20 |
| abstract_inverted_index.temporal | 38 |
| abstract_inverted_index.training | 110 |
| abstract_inverted_index.achieving | 136 |
| abstract_inverted_index.attention | 73 |
| abstract_inverted_index.behavior. | 18 |
| abstract_inverted_index.combining | 91 |
| abstract_inverted_index.including | 122 |
| abstract_inverted_index.introduce | 86 |
| abstract_inverted_index.mechanism | 106 |
| abstract_inverted_index.screening | 105 |
| abstract_inverted_index.Cross-View | 50 |
| abstract_inverted_index.clip-level | 27 |
| abstract_inverted_index.cross-view | 72 |
| abstract_inverted_index.egocentric | 7, 59, 118, 141 |
| abstract_inverted_index.emphasizes | 108 |
| abstract_inverted_index.exocentric | 9, 61 |
| abstract_inverted_index.explicitly | 53 |
| abstract_inverted_index.intra-view | 69 |
| abstract_inverted_index.reinforced | 101 |
| abstract_inverted_index.Transformer | 51 |
| abstract_inverted_index.approaches, | 135 |
| abstract_inverted_index.benchmarks, | 121 |
| abstract_inverted_index.demonstrate | 128 |
| abstract_inverted_index.informative | 109 |
| abstract_inverted_index.outperforms | 133 |
| abstract_inverted_index.performance | 138 |
| abstract_inverted_index.recognition | 120 |
| abstract_inverted_index.Charades-Ego | 123 |
| abstract_inverted_index.Experimental | 126 |
| abstract_inverted_index.consistently | 132 |
| abstract_inverted_index.fine-grained | 55 |
| abstract_inverted_index.incorporates | 65 |
| abstract_inverted_index.comprehensive | 14 |
| abstract_inverted_index.misalignments | 39 |
| abstract_inverted_index.understanding | 15, 143 |
| abstract_inverted_index.EPIC-Kitchens. | 125 |
| abstract_inverted_index.classification | 98 |
| abstract_inverted_index.correspondence | 57 |
| abstract_inverted_index.self-attention | 66 |
| abstract_inverted_index.spatiotemporal | 56 |
| abstract_inverted_index.state-of-the-art | 137 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.6000000238418579 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| citation_normalized_percentile.value | 0.31026541 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |