{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T16:07:59Z","timestamp":1743091679549,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":38,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819985456"},{"type":"electronic","value":"9789819985463"}],"license":[{"start":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T00:00:00Z","timestamp":1703548800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T00:00:00Z","timestamp":1703548800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-99-8546-3_12","type":"book-chapter","created":{"date-parts":[[2023,12,25]],"date-time":"2023-12-25T19:02:17Z","timestamp":1703530937000},"page":"145-157","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Interactive Learning for\u00a0Interpretable Visual Recognition via\u00a0Semantic-Aware Self-Teaching Framework"],"prefix":"10.1007","author":[{"given":"Hao","family":"Jiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haowei","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junhao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wentao","family":"Wan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keze","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,26]]},"reference":[{"key":"12_CR1","unstructured":"Alvarez-Melis, D. et al.: Towards robust interpretability with self-explaining neural networks. In: NIPS (2018)"},{"key":"12_CR2","unstructured":"Beckh, K., et al.: Explainable machine learning with prior knowledge: an overview. arXiv (2021)"},{"key":"12_CR3","doi-asserted-by":"crossref","unstructured":"Bychkov, D., et al.: Deep learning based tissue analysis predicts outcome in colorectal cancer. Sci. Rep. (2018)","DOI":"10.1038\/s41598-018-21758-3"},{"key":"12_CR4","unstructured":"Chen, C., et al.: This looks like that: deep learning for interpretable image recognition. Neural Inf. Process. Syst. (2019)"},{"key":"12_CR5","doi-asserted-by":"crossref","unstructured":"Chen, Z., et al.: Concept whitening for interpretable image recognition. Nat. Mach. Intell. (2020)","DOI":"10.1038\/s42256-020-00265-z"},{"key":"12_CR6","doi-asserted-by":"crossref","unstructured":"Deng, J., et al.: Imagenet: a large-scale hierarchical image database. In: CVPR (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"12_CR7","doi-asserted-by":"crossref","unstructured":"Donnelly, J., et al.: Deformable protopnet: an interpretable image classifier using deformable prototypes. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.01002"},{"key":"12_CR8","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. In: ICLR (2021)"},{"key":"12_CR9","doi-asserted-by":"crossref","unstructured":"He, J., et al.: Transfg: a transformer architecture for fine-grained recognition. In: AAAI (2022)","DOI":"10.1609\/aaai.v36i1.19967"},{"key":"12_CR10","doi-asserted-by":"crossref","unstructured":"He, K., et al.: Deep residual learning for image recognition. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"12_CR11","unstructured":"Huang, Y., Chen, J.: Teacher-critical training strategies for image captioning. In: CVPR (2020)"},{"key":"12_CR12","doi-asserted-by":"crossref","unstructured":"Huang, Z., Li, Y.: Interpretable and accurate fine-grained recognition via region grouping. arXiv (2020)","DOI":"10.1109\/CVPR42600.2020.00869"},{"key":"12_CR13","doi-asserted-by":"crossref","unstructured":"Ji, R., et al.: Attention convolutional binary neural tree for fine-grained visual categorization. In: CVPR (2019)","DOI":"10.1109\/CVPR42600.2020.01048"},{"key":"12_CR14","doi-asserted-by":"crossref","unstructured":"Johns, E., et al.: Becoming the expert - interactive multi-class machine teaching. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298877"},{"key":"12_CR15","unstructured":"Khosla, A., et al.: Novel dataset for fine-grained image categorization: stanford dogs. In: Proceedings of the CVPR Workshop on Fine-Grained Visual Categorization (FGVC) (2011)"},{"key":"12_CR16","unstructured":"Kim, S., et al.: Vit-net: interpretable vision transformers with neural tree decoder. In: ICML (2023)"},{"key":"12_CR17","doi-asserted-by":"crossref","unstructured":"Krause, J., et al.: 3d object representations for fine-grained categorization. In: ICCV (2013)","DOI":"10.1109\/ICCVW.2013.77"},{"key":"12_CR18","doi-asserted-by":"crossref","unstructured":"Linardatos, P., et al.: Explainable AI: a review of machine learning interpretability methods. Entropy (2020)","DOI":"10.3390\/e23010018"},{"key":"12_CR19","doi-asserted-by":"crossref","unstructured":"Liu, R., et al.: Teacher-student training for robust tacotron-based TTS. In: ICASSP (2020)","DOI":"10.1109\/ICASSP40776.2020.9054681"},{"key":"12_CR20","unstructured":"Liu, W., et al.: Iterative machine teaching. Mach. Learn. (2017)"},{"key":"12_CR21","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"12_CR22","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. Learning (2017)"},{"key":"12_CR23","unstructured":"Meek, C., et al.: Analysis of a design pattern for teaching with features and labels. arXiv (2016)"},{"key":"12_CR24","doi-asserted-by":"crossref","unstructured":"Mei, S., Zhu, X.: Using machine teaching to identify optimal training-set attacks on machine learners. In: AAAI (2015)","DOI":"10.1609\/aaai.v29i1.9569"},{"key":"12_CR25","doi-asserted-by":"crossref","unstructured":"Nauta, et al.: Neural prototype trees for interpretable fine-grained image recognition. arXiv (2020)","DOI":"10.1109\/CVPR46437.2021.01469"},{"key":"12_CR26","doi-asserted-by":"crossref","unstructured":"Nauta, M., et al.: Neural prototype trees for interpretable fine-grained image recognition. In: CVPR (2020)","DOI":"10.1109\/CVPR46437.2021.01469"},{"key":"12_CR27","doi-asserted-by":"crossref","unstructured":"Rymarczyk, D., et al.: Interpretable image classification with differentiable prototypes assignment. In: ECCV (2022)","DOI":"10.1007\/978-3-031-19775-8_21"},{"key":"12_CR28","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., et al.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: ICCV (2016)","DOI":"10.1109\/ICCV.2017.74"},{"key":"12_CR29","unstructured":"Wah, C., et al.: The caltech-ucsd birds-200-2011 dataset (2011)"},{"key":"12_CR30","doi-asserted-by":"crossref","unstructured":"Wang, J., et al.: Interpretable image recognition by constructing transparent embedding space. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00093"},{"key":"12_CR31","unstructured":"Xue, M., et al.: Protopformer: concentrating on prototypical parts in vision transformers for interpretable image recognition. arXiv (2022)"},{"key":"12_CR32","doi-asserted-by":"crossref","unstructured":"Zech, J.R., et al.: Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study. PLoS Med. (2018)","DOI":"10.1371\/journal.pmed.1002683"},{"key":"12_CR33","doi-asserted-by":"crossref","unstructured":"Zeng, X., Sun, H.: Interactive image recognition of space target objects. IOP Conf. Ser. (2017)","DOI":"10.1088\/1757-899X\/272\/1\/012008"},{"key":"12_CR34","unstructured":"Zhang, C., et al.: One-shot machine teaching: cost very few examples to converge faster. arXiv (2022)"},{"key":"12_CR35","doi-asserted-by":"crossref","unstructured":"Zhang, Q., et al.: Interpretable convolutional neural networks. In: CVPR (2017)","DOI":"10.1109\/CVPR.2018.00920"},{"key":"12_CR36","doi-asserted-by":"crossref","unstructured":"Zhang, X., et al.: Explainable machine learning in image classification models: an uncertainty quantification perspective. Knowl. Based Syst. (2022)","DOI":"10.1016\/j.knosys.2022.108418"},{"key":"12_CR37","doi-asserted-by":"crossref","unstructured":"Zhu, X., et al.: Machine teaching: an inverse problem to machine learning and an approach toward optimal education. In: AAAI (2015)","DOI":"10.1609\/aaai.v29i1.9761"},{"key":"12_CR38","unstructured":"Zhu, X., et al.: An overview of machine teaching. arXiv (2018)"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8546-3_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,25]],"date-time":"2023-12-25T19:13:58Z","timestamp":1703531638000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8546-3_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,26]]},"ISBN":["9789819985456","9789819985463"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8546-3_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,12,26]]},"assertion":[{"value":"26 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xiamen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/prcv2023.xmu.edu.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1420","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"532","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"37% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,78","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,69","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}