{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T02:29:19Z","timestamp":1743820159293,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031732010"},{"type":"electronic","value":"9783031732027"}],"license":[{"start":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T00:00:00Z","timestamp":1732147200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T00:00:00Z","timestamp":1732147200000},"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\/978-3-031-73202-7_24","type":"book-chapter","created":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T14:17:22Z","timestamp":1732112242000},"page":"414-429","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["ViG-Bias: Visually Grounded Bias Discovery and\u00a0Mitigation"],"prefix":"10.1007","author":[{"given":"Badr-Eddine","family":"Marani","sequence":"first","affiliation":[]},{"given":"Mohamed","family":"Hanini","sequence":"additional","affiliation":[]},{"given":"Nihitha","family":"Malayarukil","sequence":"additional","affiliation":[]},{"given":"Stergios","family":"Christodoulidis","sequence":"additional","affiliation":[]},{"given":"Maria","family":"Vakalopoulou","sequence":"additional","affiliation":[]},{"given":"Enzo","family":"Ferrante","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,21]]},"reference":[{"key":"24_CR1","doi-asserted-by":"publisher","first-page":"502","DOI":"10.1016\/j.cag.2021.09.002","volume":"102","author":"G Alicioglu","year":"2022","unstructured":"Alicioglu, G., Sun, B.: A survey of visual analytics for Explainable Artificial Intelligence methods. Comput. Graph. 102, 502\u2013520 (2022). https:\/\/doi.org\/10.1016\/j.cag.2021.09.002","journal-title":"Comput. Graph."},{"key":"24_CR2","unstructured":"Barocas, S., Hardt, M., Narayanan, A.: Fairness and Machine Learning: Limitations and Opportunities. MIT Press, Cambridge (2023). https:\/\/fairmlbook.org\/pdf\/fairmlbook.pdf"},{"key":"24_CR3","unstructured":"Buolamwini, J., Gebru, T.: Gender shades: intersectional accuracy disparities in commercial gender classification. In: Friedler, S.A., Wilson, C. (eds.) Proceedings of the 1st Conference on Fairness, Accountability and Transparency. Proceedings of Machine Learning Research, vol.\u00a081, pp. 77\u201391. PMLR (2018). https:\/\/proceedings.mlr.press\/v81\/buolamwini18a.html"},{"key":"24_CR4","doi-asserted-by":"publisher","unstructured":"Chattopadhay, A., Sarkar, A., Howlader, P., Balasubramanian, V.N.: Grad-CAM++: generalized gradient-based visual explanations for deep convolutional networks. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE (2018). https:\/\/doi.org\/10.1109\/WACV.2018.00097","DOI":"10.1109\/WACV.2018.00097"},{"key":"24_CR5","doi-asserted-by":"crossref","unstructured":"Du, Y., Liu, Z., Li, J., Zhao, W.X.: A survey of vision-language pre-trained models (2022). https:\/\/arxiv.org\/abs\/2202.10936","DOI":"10.24963\/ijcai.2022\/762"},{"key":"24_CR6","unstructured":"Eyuboglu, S., et al.: Domino: discovering systematic errors with cross-modal embeddings (2022). https:\/\/arxiv.org\/abs\/2203.14960"},{"issue":"11","key":"24_CR7","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1038\/s42256-020-00257-z","volume":"2","author":"R Geirhos","year":"2020","unstructured":"Geirhos, R., et al.: Shortcut learning in deep neural networks. Nat. Mach. Intell. 2(11), 665\u2013673 (2020). https:\/\/doi.org\/10.1038\/s42256-020-00257-z","journal-title":"Nat. Mach. Intell."},{"key":"24_CR8","unstructured":"Joshi, S., Yang, Y., Xue, Y., Yang, W., Mirzasoleiman, B.: Towards mitigating spurious correlations in the wild: a benchmark and a more realistic dataset (2023). https:\/\/arxiv.org\/abs\/2306.11957"},{"key":"24_CR9","unstructured":"Kim, Y., Mo, S., Kim, M., Lee, K., Lee, J., Shin, J.: Bias-to-text: debiasing unknown visual biases through language interpretation (2023). https:\/\/arxiv.org\/abs\/2301.11104"},{"key":"24_CR10","doi-asserted-by":"crossref","unstructured":"Kirillov, A., et al.: Segment anything (2023). https:\/\/arxiv.org\/abs\/2304.02643","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"24_CR11","unstructured":"Krishnakumar, A., Prabhu, V., Sudhakar, S., Hoffman, J.: UDIS: unsupervised discovery of bias in deep visual recognition models (2021). https:\/\/arxiv.org\/abs\/2110.15499"},{"key":"24_CR12","unstructured":"Liu, E.Z., et al.: Just train twice: improving group robustness without training group information (2021). https:\/\/arxiv.org\/abs\/2107.09044"},{"key":"24_CR13","unstructured":"Liu, S., et al.: Grounding DINO: marrying DINO with grounded pre-training for open-set object detection (2023). https:\/\/arxiv.org\/abs\/2303.05499"},{"key":"24_CR14","doi-asserted-by":"crossref","unstructured":"Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild (2015). https:\/\/arxiv.org\/abs\/1411.7766","DOI":"10.1109\/ICCV.2015.425"},{"key":"24_CR15","unstructured":"Mokady, R., Hertz, A., Bermano, A.H.: ClipCap: CLIP prefix for image captioning (2021). https:\/\/arxiv.org\/abs\/2111.09734"},{"key":"24_CR16","unstructured":"Radford, A., et al.: Learning transferable visual models from natural language supervision (2021). https:\/\/arxiv.org\/abs\/2103.00020"},{"key":"24_CR17","unstructured":"Sagawa, S., Koh, P.W., Hashimoto, T.B., Liang, P.: Distributionally robust neural networks for group shifts: on the importance of regularization for worst-case generalization (2020). https:\/\/arxiv.org\/abs\/1911.08731"},{"issue":"2","key":"24_CR18","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","volume":"128","author":"RR Selvaraju","year":"2019","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. Int. J. Comput. Vision 128(2), 336\u2013359 (2019). https:\/\/doi.org\/10.1007\/s11263-019-01228-7","journal-title":"Int. J. Comput. Vision"},{"key":"24_CR19","unstructured":"Singla, S., Feizi, S.: Salient ImageNet: how to discover spurious features in deep learning? (2022). https:\/\/arxiv.org\/abs\/2110.04301"},{"key":"24_CR20","unstructured":"Srinivas, S., Fleuret, F.: Full-gradient representation for neural network visualization (2019). https:\/\/arxiv.org\/abs\/1905.00780"},{"key":"24_CR21","doi-asserted-by":"crossref","unstructured":"Sun, S., Koch, L.M., Baumgartner, C.F.: Right for the wrong reason: can interpretable ML techniques detect spurious correlations? (2023). https:\/\/arxiv.org\/abs\/2307.12344","DOI":"10.1007\/978-3-031-43895-0_40"},{"key":"24_CR22","doi-asserted-by":"crossref","unstructured":"Wang, H., et al.: Score-CAM: score-weighted visual explanations for convolutional neural networks (2020). https:\/\/arxiv.org\/abs\/1910.01279","DOI":"10.1109\/CVPRW50498.2020.00020"},{"key":"24_CR23","doi-asserted-by":"crossref","unstructured":"Yenamandra, S., Ramesh, P., Prabhu, V., Hoffman, J.: FACTS: first amplify correlations and then slice to discover bias (2023). https:\/\/arxiv.org\/abs\/2309.17430","DOI":"10.1109\/ICCV51070.2023.00442"},{"key":"24_CR24","unstructured":"Zhang, M., R\u00e9, C.: Contrastive adapters for foundation model group robustness (2022). https:\/\/arxiv.org\/abs\/2207.07180"},{"key":"24_CR25","unstructured":"Zhang, X., He, Y., Xu, R., Yu, H., Shen, Z., Cui, P.: NICO++: towards better benchmarking for domain generalization (2022). https:\/\/arxiv.org\/abs\/2204.08040"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73202-7_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T15:10:51Z","timestamp":1732115451000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73202-7_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,21]]},"ISBN":["9783031732010","9783031732027"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73202-7_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,21]]},"assertion":[{"value":"21 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}