{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T19:50:32Z","timestamp":1774295432870,"version":"3.50.1"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031262838","type":"print"},{"value":"9783031262845","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-26284-5_29","type":"book-chapter","created":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T08:02:59Z","timestamp":1677052979000},"page":"475-490","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Visual Explanation Generation Based on\u00a0Lambda Attention Branch Networks"],"prefix":"10.1007","author":[{"given":"Tsumugi","family":"Iida","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takumi","family":"Komatsu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kanta","family":"Kaneda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tsubasa","family":"Hirakawa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takayoshi","family":"Yamashita","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hironobu","family":"Fujiyoshi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Komei","family":"Sugiura","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,23]]},"reference":[{"key":"29_CR1","doi-asserted-by":"crossref","unstructured":"Abnar, S., Zuidema, W.: Quantifying attention flow in transformers. arXiv preprint arXiv:2005.00928 (2020)","DOI":"10.18653\/v1\/2020.acl-main.385"},{"key":"29_CR2","unstructured":"Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., Kim, B.: Sanity checks for saliency maps. In: NeurIPS, vol. 31 (2018)"},{"key":"29_CR3","unstructured":"Bello, I.: LambdaNetworks: modeling long-range interactions without attention. In: ICLR (2021)"},{"key":"29_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/978-3-319-44781-0_8","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2016","author":"A Binder","year":"2016","unstructured":"Binder, A., Montavon, G., Lapuschkin, S., M\u00fcller, K.-R., Samek, W.: Layer-wise relevance propagation for neural networks with local renormalization layers. In: Villa, A.E.P., Masulli, P., Pons Rivero, A.J. (eds.) ICANN 2016. LNCS, vol. 9887, pp. 63\u201371. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-44781-0_8"},{"key":"29_CR5","doi-asserted-by":"crossref","unstructured":"Chefer, H., Gur, S., Wolf, L.: Transformer interpretability beyond attention visualization. In: CVPR, pp. 782\u2013791 (2021)","DOI":"10.1109\/CVPR46437.2021.00084"},{"key":"29_CR6","unstructured":"Das, A., Rad, P.: Opportunities and challenges in explainable artificial intelligence (XAI): a survey. arXiv preprint arXiv:2006.11371 (2020)"},{"key":"29_CR7","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR, pp. 248\u2013255 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"29_CR8","unstructured":"Dosovitskiy, A., Beyer, L., et al.: An image is worth $$16\\times 16$$ words: transformers for image recognition at scale. In: ICLR (2021)"},{"key":"29_CR9","doi-asserted-by":"crossref","unstructured":"Fel, T., Vigouroux, D., Cad\u00e8ne, R., Serre, T.: How good is your explanation? Algorithmic stability measures to assess the quality of explanations for deep neural networks. In: WACV, pp. 720\u2013730 (2022)","DOI":"10.1109\/WACV51458.2022.00163"},{"key":"29_CR10","doi-asserted-by":"crossref","unstructured":"Fukui, H., Hirakawa, T., et al.: Attention branch network: learning of attention mechanism for visual explanation. In: CVPR, pp. 10705\u201310714 (2019)","DOI":"10.1109\/CVPR.2019.01096"},{"key":"29_CR11","unstructured":"Hooker, S., Erhan, D., Kindermans, P.J., Kim, B.: A benchmark for interpretability methods in deep neural networks. In: NeurIPS, vol. 32 (2019)"},{"key":"29_CR12","unstructured":"Ismail, A.A., Corrada Bravo, H., Feizi, S.: Improving deep learning interpretability by saliency guided training. In: NeurIPS (2021)"},{"key":"29_CR13","unstructured":"Jain, S., Wallace, B.: Attention is not explanation. In: NAACL, pp. 3543\u20133556 (2019)"},{"key":"29_CR14","unstructured":"Khan, S., Naseer, M., Hayat, M., Zamir, S.W., et al.: Transformers in vision: a survey. arXiv preprint arXiv:2101.01169 (2021)"},{"key":"29_CR15","doi-asserted-by":"crossref","unstructured":"Li, H., Ellis, J., Zhang, L., Chang, S.F.: PatternNet: visual pattern mining with deep neural network. In: ICMR, pp. 291\u2013299 (2018)","DOI":"10.1145\/3206025.3206039"},{"key":"29_CR16","unstructured":"Lundberg, S., Lee, S.I.: A unified approach to interpreting model predictions. In: NeurIPS, pp. 4765\u20134774 (2017)"},{"issue":"4","key":"29_CR17","first-page":"3113","volume":"3","author":"A Magassouba","year":"2018","unstructured":"Magassouba, A., Sugiura, K., et al.: A multimodal classifier generative adversarial network for carry and place tasks from ambiguous language instructions. RA-L 3(4), 3113\u20133120 (2018)","journal-title":"RA-L"},{"issue":"12","key":"29_CR18","doi-asserted-by":"publisher","first-page":"787","DOI":"10.1080\/01691864.2021.1913446","volume":"35","author":"A Magassouba","year":"2021","unstructured":"Magassouba, A., Sugiura, K., et al.: Predicting and attending to damaging collisions for placing everyday objects in photo-realistic simulations. Adv. Robot. 35(12), 787\u2013799 (2021)","journal-title":"Adv. Robot."},{"key":"29_CR19","doi-asserted-by":"crossref","unstructured":"Mitsuhara, M., Fukui, H., Sakashita, Y., et al.: Embedding human knowledge into deep neural network via attention map. In: VISAPP (2021)","DOI":"10.5220\/0010335806260636"},{"key":"29_CR20","doi-asserted-by":"crossref","unstructured":"Nishizuka, N., Sugiura, K., et al.: Deep flare net (DeFN) model for solar flare prediction. Astrophys. J. 858(2), 113 (8 pp) (2018)","DOI":"10.3847\/1538-4357\/aab9a7"},{"issue":"4","key":"29_CR21","first-page":"5945","volume":"5","author":"T Ogura","year":"2020","unstructured":"Ogura, T., Magassouba, A., Sugiura, K., et al.: Alleviating the burden of labeling: sentence generation by attention branch encoder-decoder network. RA-L 5(4), 5945\u20135952 (2020)","journal-title":"RA-L"},{"key":"29_CR22","unstructured":"Pan, B., Panda, R., Jiang, Y., et al.: IA-RED$$^2$$: interpretability-aware redundancy reduction for vision transformers. In: NeurIPS (2021)"},{"issue":"1\u20132","key":"29_CR23","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s11207-011-9841-3","volume":"275","author":"W Pesnell","year":"2012","unstructured":"Pesnell, W., Thompson, B., Chamberlin, P.: The solar dynamics observatory (SDO). Sol. Phys. 275(1\u20132), 3\u201315 (2012). https:\/\/doi.org\/10.1007\/s11207-011-9841-3","journal-title":"Sol. Phys."},{"key":"29_CR24","unstructured":"Petsiuk, V., Das, A., Saenko, K.: RISE: randomized input sampling for explanation of black-box models. In: BMVC, p. 151 (13 pp) (2018)"},{"key":"29_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101561","volume":"59","author":"P Porwal","year":"2020","unstructured":"Porwal, P., et al.: IDRiD: diabetic retinopathy - segmentation and grading challenge. Med. Image Anal. 59, 101561 (2020)","journal-title":"Med. Image Anal."},{"key":"29_CR26","doi-asserted-by":"crossref","unstructured":"Ribeiro, M., Singh, S., et al.: \u201cWhy should i trust you?\u201d: explaining the predictions of any classifier. In: KDD, pp. 1135\u20131144 (2016)","DOI":"10.1145\/2939672.2939778"},{"key":"29_CR27","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1007\/s11207-011-9834-2","volume":"275","author":"P Scherrer","year":"2012","unstructured":"Scherrer, P., Schou, J., Bush, R., et al.: The helioseismic and magnetic imager (HMI) investigation for the solar dynamics observatory (SDO). Sol. Phys. 275, 207\u2013227 (2012). https:\/\/doi.org\/10.1007\/s11207-011-9834-2","journal-title":"Sol. Phys."},{"key":"29_CR28","doi-asserted-by":"crossref","unstructured":"Selvaraju, R., et al.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: ICCV, pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"29_CR29","unstructured":"Smilkov, D., Thorat, N., Kim, B., Vi\u00e9gas, F.B., Wattenberg, M.: SmoothGrad: removing noise by adding noise. arXiv preprint arXiv:1706.03825 (2017)"},{"key":"29_CR30","unstructured":"Srinivas, S., Fleuret, F.: Full-gradient representation for neural network visualization. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"29_CR31","unstructured":"Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. In: ICML, vol. 70, pp. 3319\u20133328 (2017)"},{"key":"29_CR32","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., et al.: Attention is all you need. In: NeurIPS, pp. 6000\u20136010 (2017)"},{"key":"29_CR33","doi-asserted-by":"crossref","unstructured":"Vig, J.: A multiscale visualization of attention in the transformer model. In: ACL, pp. 37\u201342 (2019)","DOI":"10.18653\/v1\/P19-3007"},{"key":"29_CR34","doi-asserted-by":"crossref","unstructured":"Wang, H., Wang, Z., Du, M., et al.: Score-CAM: score-weighted visual explanations for convolutional neural networks. In: CVPR, pp. 24\u201325 (2020)","DOI":"10.1109\/CVPRW50498.2020.00020"},{"issue":"6","key":"29_CR35","doi-asserted-by":"publisher","first-page":"290","DOI":"10.4239\/wjd.v4.i6.290","volume":"4","author":"L Wu","year":"2013","unstructured":"Wu, L., et al.: Classification of diabetic retinopathy and diabetic macular edema. World J. Diabetes 4(6), 290\u2013294 (2013)","journal-title":"World J. Diabetes"},{"key":"29_CR36","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Chen, Y., Li, H., Zhang, Q.: IA-CNN: a generalised interpretable convolutional neural network with attention mechanism. In: IJCNN, pp. 1\u20138 (2021)","DOI":"10.1109\/IJCNN52387.2021.9533727"},{"key":"29_CR37","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., et al.: Learning deep features for discriminative localization. In: CVPR, pp. 2921\u20132929 (2016)","DOI":"10.1109\/CVPR.2016.319"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-26284-5_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T08:17:38Z","timestamp":1677053858000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-26284-5_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031262838","9783031262845"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-26284-5_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"23 February 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Macao","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"accv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.accv2022.org","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":"CMT Microsoft","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"836","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":"277","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":"33% - 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":"3.3","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":"2.6","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"For the ACCV 2022 workshops 25 papers have been accepted from 40 submissions","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}