{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T06:43:17Z","timestamp":1774680197487,"version":"3.50.1"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031959172","type":"print"},{"value":"9783031959189","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-95918-9_23","type":"book-chapter","created":{"date-parts":[[2025,6,21]],"date-time":"2025-06-21T13:31:07Z","timestamp":1750512667000},"page":"325-338","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The Weighting Game: Evaluating Quality of\u00a0Explainability Methods"],"prefix":"10.1007","author":[{"given":"Lassi","family":"Raatikainen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Esa","family":"Rahtu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,16]]},"reference":[{"key":"23_CR1","unstructured":"Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., Kim, B.: Sanity checks for saliency maps. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS 2018, pp. 9525\u20139536. Curran Associates Inc., Red Hook (2018)"},{"key":"23_CR2","doi-asserted-by":"crossref","unstructured":"Bach, S., Binder, A., Montavon, G., Klauschen, F., M\u00fcller, K.R., Samek, W.: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS One 10 (2015)","DOI":"10.1371\/journal.pone.0130140"},{"key":"23_CR3","doi-asserted-by":"crossref","unstructured":"Chattopadhay, A., Sarkar, A., Howlader, P., Balasubramanian, V.N.: Grad-CAM++: generalized gradient-based visual explanations for deep convolutional networks. In: WACV (2018)","DOI":"10.1109\/WACV.2018.00097"},{"key":"23_CR4","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 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"23_CR5","doi-asserted-by":"crossref","unstructured":"Desai, S., Ramaswamy, H.G.: Ablation-CAM: visual explanations for deep convolutional network via gradient-free localization. In: WACV (2020)","DOI":"10.1109\/WACV45572.2020.9093360"},{"key":"23_CR6","unstructured":"Dosovitskiy, A., et al.: An image is worth 16$$\\times $$16 words: transformers for image recognition at scale. In: ICLR (2021)"},{"issue":"7","key":"23_CR7","doi-asserted-by":"publisher","first-page":"620","DOI":"10.1038\/s42256-021-00343-w","volume":"3","author":"G Erion","year":"2021","unstructured":"Erion, G., Janizek, J.D., Sturmfels, P., Lundberg, S.M., Lee, S.I.: Improving performance of deep learning models with axiomatic attribution priors and expected gradients. Nat. Mach. Intell. 3(7), 620\u2013631 (2021)","journal-title":"Nat. Mach. Intell."},{"key":"23_CR8","unstructured":"Everingham, M., Van\u00a0Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL visual object classes challenge 2007 (VOC2007) results. http:\/\/www.pascal-network.org\/challenges\/VOC\/voc2007\/workshop\/index.html"},{"key":"23_CR9","doi-asserted-by":"crossref","unstructured":"Fu, R., Hu, Q., Dong, X., Guo, Y., Gao, Y., Li, B.: Axiom-based grad-CAM: towards accurate visualization and explanation of CNNs. In: BMVC (2020)","DOI":"10.5244\/C.34.146"},{"key":"23_CR10","unstructured":"Gildenblat, J. (ed.): Pytorch library for CAM methods (2021). https:\/\/github.com\/jacobgil\/pytorch-grad-cam"},{"key":"23_CR11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"23_CR12","doi-asserted-by":"publisher","first-page":"5875","DOI":"10.1109\/TIP.2021.3089943","volume":"30","author":"PT Jiang","year":"2021","unstructured":"Jiang, P.T., Zhang, C.B., Hou, Q., Cheng, M.M., Wei, Y.: LayerCAM: exploring hierarchical class activation maps for localization. IEEE Trans. Image Process. 30, 5875\u20135888 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"23_CR13","doi-asserted-by":"crossref","unstructured":"Kapishnikov, A., Bolukbasi, T., Vi\u00e9gas, F., Terry, M.: XRAI: better attributions through regions. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00505"},{"key":"23_CR14","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., et al.: Microsoft COCO: common objects in context. In: ECCV (2014)","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"23_CR15","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Video swin transformer. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00320"},{"key":"23_CR16","doi-asserted-by":"crossref","unstructured":"Niklaus, S., Mai, L., Yang, J., Liu, F.: 3D ken burns effect from a single image. ACM Trans. Graph. 38(6), 184:1\u2013184:15 (2019)","DOI":"10.1145\/3355089.3356528"},{"key":"23_CR17","unstructured":"Omeiza, D., Speakman, S., Cintas, C., Weldemariam, K.: Smooth grad-CAM++: an enhanced inference level visualization technique for deep convolutional neural network models. ArXiv abs\/1908.01224 (2019)"},{"key":"23_CR18","unstructured":"Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: NeurIPS (2019)"},{"key":"23_CR19","unstructured":"Petsiuk, V., Das, A., Saenko, K.: RISE: randomized input sampling for explanation of black-box models. In: BMVC (2018)"},{"key":"23_CR20","unstructured":"Raghu, M., Unterthiner, T., Kornblith, S., Zhang, C., Dosovitskiy, A.: Do vision transformers see like convolutional neural networks? In: NeurIPS (2021)"},{"key":"23_CR21","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"23_CR22","unstructured":"Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps. CoRR abs\/1312.6034 (2014)"},{"key":"23_CR23","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)"},{"key":"23_CR24","unstructured":"Smilkov, D., Thorat, N., Kim, B., Vi\u00e9gas, F., Wattenberg, M.: SmoothGrad: removing noise by adding noise. ArXiv (2017)"},{"key":"23_CR25","unstructured":"Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.A.: Striving for simplicity: the all convolutional net. CoRR abs\/1412.6806 (2015)"},{"key":"23_CR26","unstructured":"Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. In: ICML (2017)"},{"key":"23_CR27","doi-asserted-by":"crossref","unstructured":"Volokitin, A., Gygli, M., Boix, X.: Predicting when saliency maps are accurate and eye fixations consistent. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.65"},{"key":"23_CR28","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: ECCV (2014)","DOI":"10.1007\/978-3-319-10590-1_53"},{"issue":"10","key":"23_CR29","doi-asserted-by":"publisher","first-page":"1084","DOI":"10.1007\/s11263-017-1059-x","volume":"126","author":"J Zhang","year":"2017","unstructured":"Zhang, J., Bargal, S.A., Lin, Z., Brandt, J., Shen, X., Sclaroff, S.: Top-down neural attention by excitation backprop. Int. J. Comput. Vision 126(10), 1084\u20131102 (2017)","journal-title":"Int. J. Comput. Vision"},{"key":"23_CR30","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.319"}],"container-title":["Lecture Notes in Computer Science","Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-95918-9_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T05:17:44Z","timestamp":1774675064000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-95918-9_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031959172","9783031959189"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-95918-9_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"16 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SCIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Scandinavian Conference on Image Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Reykjavik","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Iceland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 June 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"scia2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/scia2025.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}