{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T10:22:52Z","timestamp":1777890172360,"version":"3.51.4"},"reference-count":29,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T00:00:00Z","timestamp":1760832000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T00:00:00Z","timestamp":1760832000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000001","name":"US National Science Foundation","doi-asserted-by":"publisher","award":["2217071,2213700,2106913,2008208"],"award-info":[{"award-number":["2217071,2213700,2106913,2008208"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,19]]},"DOI":"10.1109\/iccv51701.2025.00065","type":"proceedings-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T19:45:49Z","timestamp":1777491949000},"page":"614-623","source":"Crossref","is-referenced-by-count":0,"title":["GCAV: A Global Concept Activation Vector Framework for Cross-Layer Consistency in Interpretability"],"prefix":"10.1109","author":[{"given":"Zhenghao","family":"He","sequence":"first","affiliation":[{"name":"University of Virginia,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sanchit","family":"Sinha","sequence":"additional","affiliation":[{"name":"University of Virginia,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangzhi","family":"Xiong","sequence":"additional","affiliation":[{"name":"University of Virginia,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aidong","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Virginia,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2021.07.015"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.354"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.50"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW59228.2023.00069"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-020-00265-z"},{"key":"ref6","article-title":"Visual-tcav: Concept-based attribution and saliency maps for post-hoc explainability in image classification","author":"De Santis","year":"2024","journal-title":"arXiv preprint"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref8","first-page":"2881","article-title":"What neural networks memorize and why: Discovering the long tail via influence estimation","volume":"33","author":"Feldman","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref9","article-title":"Towards automatic concept-based explanations","author":"Ghorbani","year":"2019","journal-title":"NeurIPS"},{"key":"ref10","article-title":"Explaining classifiers with causal concept effect (cace)","author":"Goyal","year":"2019","journal-title":"arXiv preprint"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.52202\/075280-2783"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref13","first-page":"2668","article-title":"Interpretability be-yond feature attribution: Quantitative testing with concept activation vectors (tcav)","volume-title":"In International conference on machine learning","author":"Kim"},{"key":"ref14","article-title":"Understanding black-box predictions via influence functions","author":"Wei Koh","year":"2017","journal-title":"In ICML. PMLR"},{"key":"ref15","first-page":"5338","article-title":"Concept bottleneck models","author":"Wei Koh","year":"2020","journal-title":"In ICML"},{"key":"ref16","author":"Krizhevsky","year":"2009","journal-title":"Learning multiple layers of features from tiny images"},{"key":"ref17","first-page":"30","article-title":"A unified approach to interpreting model predictions","author":"Lundberg","year":"2017","journal-title":"NeurIPS"},{"key":"ref18","article-title":"Explaining explainability: Understanding concept activation vectors","author":"Nicolson","year":"2024","journal-title":"arXiv preprint"},{"key":"ref19","article-title":"Representation learning with contrastive predictive coding","author":"van den Oord","year":"2018","journal-title":"arXiv preprint"},{"key":"ref20","first-page":"19920","article-title":"Estimating training data influence by tracing gradient descent","volume":"33","author":"Pruthi","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-3020"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref24","first-page":"3319","article-title":"Axiomatic attribution for deep networks","author":"Sundararajan","year":"2017","journal-title":"In ICML"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.4324\/9781410605337-29"},{"key":"ref26","article-title":"Attention is all you need","author":"Vaswani","year":"2017","journal-title":"In NeurIPS"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1403112111"},{"key":"ref28","first-page":"31","article-title":"Representer point selection for explaining deep neural networks","author":"Yeh","year":"2018","journal-title":"Advances in neural information processing systems"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10590-1_53"}],"event":{"name":"2025 IEEE\/CVF International Conference on Computer Vision (ICCV)","location":"Honolulu, HI, USA","start":{"date-parts":[[2025,10,19]]},"end":{"date-parts":[[2025,10,25]]}},"container-title":["2025 IEEE\/CVF International Conference on Computer Vision (ICCV)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11443115\/11443287\/11445206.pdf?arnumber=11445206","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T05:31:39Z","timestamp":1777613499000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11445206\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,19]]},"references-count":29,"URL":"https:\/\/doi.org\/10.1109\/iccv51701.2025.00065","relation":{},"subject":[],"published":{"date-parts":[[2025,10,19]]}}}