{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T14:57:59Z","timestamp":1726066679342},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030398774"},{"type":"electronic","value":"9783030398781"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-39878-1_17","type":"book-chapter","created":{"date-parts":[[2020,2,3]],"date-time":"2020-02-03T07:02:33Z","timestamp":1580713353000},"page":"179-187","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Latent-Space Data Augmentation for\u00a0Visually-Grounded Language Understanding"],"prefix":"10.1007","author":[{"given":"Aly","family":"Magassouba","sequence":"first","affiliation":[]},{"given":"Komei","family":"Sugiura","sequence":"additional","affiliation":[]},{"given":"Hisashi","family":"Kawai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,2,4]]},"reference":[{"key":"17_CR1","unstructured":"Magassouba, A., Sugiura, K., Kawai, H.: A multimodal target-source classifier model for object fetching from natural language instructions. In: Proceedings of the National Congress of the Japanese Society for Artificial Intelligence, pp. 2D3E403\u20132D3E403 (2019). (in Japanese)"},{"issue":"4","key":"17_CR2","first-page":"3884","volume":"4","author":"A Magassouba","year":"2019","unstructured":"Magassouba, A., Sugiura, K., Trinh Quoc, A., Kawai, H.: Understanding natural language instructions for fetching daily objects using GAN-based multimodal target-source classification. IEEE RA-L 4(4), 3884\u20133891 (2019)","journal-title":"IEEE RA-L"},{"key":"17_CR3","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1016\/j.artint.2015.08.002","volume":"229","author":"L Iocchi","year":"2015","unstructured":"Iocchi, L., Holz, D., Ruiz-del Solar, J., Sugiura, K., Van Der Zant, T.: RoboCup@ Home: analysis and results of evolving competitions for domestic and service robots. Artif. Intell. 229, 258\u2013281 (2015)","journal-title":"Artif. Intell."},{"key":"17_CR4","doi-asserted-by":"crossref","unstructured":"Yu, L., Tan, H., Bansal, M., Berg, T.L.: A joint speaker listener-reinforcer model for referring expressions. In: CVPR, vol. 2 (2017)","DOI":"10.1109\/CVPR.2017.375"},{"issue":"4","key":"17_CR5","first-page":"3113","volume":"3","author":"A Magassouba","year":"2018","unstructured":"Magassouba, A., Sugiura, K., Kawai, H.: A multimodal classifier generative adversarial network for carry and place tasks from ambiguous language instructions. IEEE RA-L 3(4), 3113\u20133120 (2018)","journal-title":"IEEE RA-L"},{"key":"17_CR6","unstructured":"Magassouba, A.,\u00a0Sugiura, K.,\u00a0Kawai, H.: Multimodal attention branch network for perspective-free sentence generation. In: Conference on Robot Learning (CoRL) (2019)"},{"key":"17_CR7","doi-asserted-by":"crossref","unstructured":"Cohen, V.,\u00a0Burchfiel, B., Nguyen, T., Gopalan, N., Tellex, S., Konidaris, G.: Grounding language attributes to objects using bayesian eigenobjects. In: Proceedings IEEE IROS 2019 (2019)","DOI":"10.1109\/IROS40897.2019.8968603"},{"key":"17_CR8","doi-asserted-by":"crossref","unstructured":"Nagaraja,V.K., Morariu, V.I., Davis, L.S.: Modeling context between objects for referring expression understanding. In: ECCV, pp. 792\u2013807 (2016)","DOI":"10.1007\/978-3-319-46493-0_48"},{"key":"17_CR9","doi-asserted-by":"crossref","unstructured":"Hatori, J., et al.: Interactively picking real-world objects with unconstrained spoken language instructions. In: IEEE ICRA, pp. 3774\u20133781 (2018)","DOI":"10.1109\/ICRA.2018.8460699"},{"key":"17_CR10","doi-asserted-by":"crossref","unstructured":"Shridhar, M.,\u00a0Hsu, D.: Interactive visual grounding of referring expressions for human-robot interaction. In: RSS (2018)","DOI":"10.15607\/RSS.2018.XIV.028"},{"key":"17_CR11","unstructured":"Springenberg, J.T.: Unsupervised and semi-supervised learning with categorical generative adversarial networks. In: Proceedings ICLR 2015 (2015)"},{"key":"17_CR12","doi-asserted-by":"crossref","unstructured":"Sugiura, K., Kawai, H.: Grounded language understanding for manipulation instructions using GAN-based classification. In: IEEE ASRU (2017)","DOI":"10.1109\/ASRU.2017.8268980"},{"key":"17_CR13","unstructured":"Odena, A., Olah, C., Shlens, J.: Conditional Image Synthesis with Auxiliary Classifier GANs. In: ICML, pp. 2642\u20132651 (2017)"},{"key":"17_CR14","doi-asserted-by":"crossref","unstructured":"Anderson, P.,\u00a0Wu, Q.,\u00a0Teney, D.,\u00a0Bruce, J.,\u00a0Johnson, M., S\u00fcnderhauf, N., Reid, I., Gould, S.,\u00a0van\u00a0den Hengel, A.: Vision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments. In: ECCV, pp. 3674\u20133683 (2018)","DOI":"10.1109\/CVPR.2018.00387"},{"key":"17_CR15","doi-asserted-by":"crossref","unstructured":"Bousmalis, K., et al.: Using simulation and domain adaptation to improve efficiency of deep robotic grasping. In: Proceedings of IEEE ICRA, pp. 4243\u20134250 (2018)","DOI":"10.1109\/ICRA.2018.8460875"},{"key":"17_CR16","doi-asserted-by":"crossref","unstructured":"Tan, J.,\u00a0Zhang, T.,\u00a0Coumans, E.,\u00a0Iscen, A.,\u00a0Bai, Y., Hafner, D.,\u00a0Bohez, S.,\u00a0Vanhoucke, V.: Sim-to-real: learning agile locomotion for quadruped robots. In: RSS (2018)","DOI":"10.15607\/RSS.2018.XIV.010"},{"key":"17_CR17","unstructured":"Simonyan, K.,\u00a0Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings ICLR 2015 (2014)"},{"key":"17_CR18","unstructured":"Devlin, J., Chang, M.-W.,\u00a0Lee, K.,\u00a0Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: ACL, pp. 4171\u20134186 (2019)"},{"key":"17_CR19","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, C.: Improved training of wasserstein GANs. In: NIPS, pp. 5769\u20135779 (2017)"},{"key":"17_CR20","unstructured":"Miyato, T., Kataoka, T.,\u00a0Koyama, M., Yoshida, Y.: Spectral normalization for generative adversarial networks. In: ICLR (2018)"}],"container-title":["Advances in Intelligent Systems and Computing","Advances in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-39878-1_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,2,3]],"date-time":"2020-02-03T07:09:52Z","timestamp":1580713792000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-39878-1_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030398774","9783030398781"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-39878-1_17","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"type":"print","value":"2194-5357"},{"type":"electronic","value":"2194-5365"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"4 February 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"JSAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Annual Conference of the Japanese Society for Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Niigata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 June 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 June 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"33","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"jsai2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.ai-gakkai.or.jp\/jsai2019\/en","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}