{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:58:45Z","timestamp":1753887525792,"version":"3.41.2"},"reference-count":35,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,11,18]],"date-time":"2021-11-18T00:00:00Z","timestamp":1637193600000},"content-version":"vor","delay-in-days":321,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61632006","61772049","61876012","61771058"],"award-info":[{"award-number":["61632006","61772049","61876012","61771058"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Hand gesture recognition is a challenging topic in the field of computer vision. Multimodal hand gesture recognition based on RGB\u2010D is with higher accuracy than that of only RGB or depth. It is not difficult to conclude that the gain originates from the complementary information existing in the two modalities. However, in reality, multimodal data are not always easy to acquire simultaneously, while unimodal RGB or depth hand gesture data are more general. Therefore, one hand gesture system is expected, in which only unimordal RGB or Depth data is supported for testing, while multimodal RGB\u2010D data is available for training so as to attain the complementary information. Fortunately, a kind of method via multimodal training and unimodal testing has been proposed. However, unimodal feature representation and cross\u2010modality transfer still need to be further improved. To this end, this paper proposes a new 3D\u2010Ghost and Spatial Attention Inflated 3D ConvNet (3DGSAI) to extract high\u2010quality features for each modality. The baseline of 3DGSAI network is Inflated 3D ConvNet (I3D), and two main improvements are proposed. One is 3D\u2010Ghost module, and the other is the spatial attention mechanism. The 3D\u2010Ghost module can extract richer features for hand gesture representation, and the spatial attention mechanism makes the network pay more attention to hand region. This paper also proposes an adaptive parameter for positive knowledge transfer, which ensures that the transfer always occurs from the strong modality network to the weak one. Extensive experiments on SKIG, VIVA, and NVGesture datasets demonstrate that our method is competitive with the state of the art. Especially, the performance of our method reaches 97.87% on the SKIG dataset using only RGB, which is the current best result.<\/jats:p>","DOI":"10.1155\/2021\/5044916","type":"journal-article","created":{"date-parts":[[2021,11,18]],"date-time":"2021-11-18T20:50:08Z","timestamp":1637268608000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Attentive 3D\u2010Ghost Module for Dynamic Hand Gesture Recognition with Positive Knowledge Transfer"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5583-8260","authenticated-orcid":false,"given":"Jinghua","family":"Li","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2402-220X","authenticated-orcid":false,"given":"Runze","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7722-7172","authenticated-orcid":false,"given":"Dehui","family":"Kong","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3045-624X","authenticated-orcid":false,"given":"Shaofan","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4977-0183","authenticated-orcid":false,"given":"Lichun","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8125-4648","authenticated-orcid":false,"given":"Baocai","family":"Yin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2991-5522","authenticated-orcid":false,"given":"Ronghua","family":"Gao","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,11,18]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-012-9356-9"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00779-015-0844-1"},{"key":"e_1_2_9_3_2","doi-asserted-by":"crossref","unstructured":"MiaoQ. 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