ACP++: Action Co-occurrence Priors for Human-Object Interaction Detection
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Dong Jin | - |
dc.contributor.author | Sun, Xiao | - |
dc.contributor.author | Choi, Jinsoo | - |
dc.contributor.author | Lin, Stephen | - |
dc.contributor.author | Kweon, In So | - |
dc.date.accessioned | 2023-08-16T08:04:01Z | - |
dc.date.available | 2023-08-16T08:04:01Z | - |
dc.date.created | 2023-07-21 | - |
dc.date.issued | 2021-09 | - |
dc.identifier.issn | 1057-7149 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/189248 | - |
dc.description.abstract | A common problem in the task of human-object interaction (HOI) detection is that numerous HOI classes have only a small number of labeled examples, resulting in training sets with a long-tailed distribution. The lack of positive labels can lead to low classification accuracy for these classes. Towards addressing this issue, we observe that there exist natural correlations and anti-correlations among human-object interactions. In this paper, we model the correlations as action co-occurrence matrices and present techniques to learn these priors and leverage them for more effective training, especially on rare classes. The efficacy of our approach is demonstrated experimentally, where the performance of our approach consistently improves over the state-of-the-art methods on both of the two leading HOI detection benchmark datasets, HICO-Det and V-COCO. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | ACP++: Action Co-occurrence Priors for Human-Object Interaction Detection | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Dong Jin | - |
dc.identifier.doi | 10.1109/TIP.2021.3113563 | - |
dc.identifier.scopusid | 2-s2.0-85115758158 | - |
dc.identifier.wosid | 000716696700003 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON IMAGE PROCESSING, v.30, pp.9150 - 9163 | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON IMAGE PROCESSING | - |
dc.citation.title | IEEE TRANSACTIONS ON IMAGE PROCESSING | - |
dc.citation.volume | 30 | - |
dc.citation.startPage | 9150 | - |
dc.citation.endPage | 9163 | - |
dc.type.rims | ART | - |
dc.type.docType | 정기학술지(Article(Perspective Article포함)) | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordAuthor | Visualization | - |
dc.subject.keywordAuthor | Training | - |
dc.subject.keywordAuthor | Task analysis | - |
dc.subject.keywordAuthor | Bicycles | - |
dc.subject.keywordAuthor | Semantics | - |
dc.subject.keywordAuthor | Context modeling | - |
dc.subject.keywordAuthor | Benchmark testing | - |
dc.subject.keywordAuthor | Human-object interaction | - |
dc.subject.keywordAuthor | Visual relationship | - |
dc.subject.keywordAuthor | Co-occurrence | - |
dc.subject.keywordAuthor | Label hierarchy | - |
dc.subject.keywordAuthor | Knowledge distillation | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/9547056 | - |
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