{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T15:57:47Z","timestamp":1781539067825,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":52,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T00:00:00Z","timestamp":1781481600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100004225","name":"Petrobras","doi-asserted-by":"publisher","award":["2023\/00095-3"],"award-info":[{"award-number":["2023\/00095-3"]}],"id":[{"id":"10.13039\/501100004225","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["313193\/2023-1"],"award-info":[{"award-number":["313193\/2023-1"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"name":"S\u00e3o Paulo Research Foundation \u2014 FAPESP","award":["2025\/07171-2"],"award-info":[{"award-number":["2025\/07171-2"]}]},{"name":"S\u00e3o Paulo Research Foundation \u2014 FAPESP","award":["2024\/04890-5"],"award-info":[{"award-number":["2024\/04890-5"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,6,16]]},"DOI":"10.1145\/3805622.3810617","type":"proceedings-article","created":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T14:42:57Z","timestamp":1781534577000},"page":"1092-1101","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Context-Aware Interpretable Representations for Retrieval and Graph Convolutional Network Classification"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2167-0463","authenticated-orcid":false,"given":"Thiago C\u00e9sar","family":"Castilho Almeida","sequence":"first","affiliation":[{"name":"UNESP - Universidade Estadual Paulista, Rio Claro, S\u00e3o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3715-8991","authenticated-orcid":false,"given":"Gustavo","family":"Rosseto Let\u00edcio","sequence":"additional","affiliation":[{"name":"UNESP - Universidade Estadual Paulista, Rio Claro, S\u00e3o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0153-7910","authenticated-orcid":false,"given":"Vinicius Atsushi","family":"Sato Kawai","sequence":"additional","affiliation":[{"name":"UNESP - Universidade Estadual Paulista, Rio Claro, S\u00e3o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2867-4838","authenticated-orcid":false,"given":"Daniel Carlos","family":"Guimar\u00e3es Pedronette","sequence":"additional","affiliation":[{"name":"UNESP - Universidade Estadual Paulista, Rio Claro, S\u00e3o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,6,15]]},"reference":[{"key":"e_1_3_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN64981.2025.11229362"},{"key":"e_1_3_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.5220\/0008985901420152"},{"key":"e_1_3_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-88720-8_40"},{"key":"e_1_3_3_2_5_2","doi-asserted-by":"publisher","unstructured":"Plamen Angelov Dmitry Kangin and Ziyang Zhang. 2025. IDEAL: Interpretable-by-Design ALgorithms for learning from foundation feature spaces. Neurocomputing 626 (2025) 129464. 10.1016\/j.neucom.2025.129464","DOI":"10.1016\/j.neucom.2025.129464"},{"key":"e_1_3_3_2_6_2","doi-asserted-by":"publisher","unstructured":"Yoshua Bengio Aaron Courville and Pascal Vincent. 2013. Representation Learning: A Review and New Perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence 35 8 (2013) 1798\u20131828. 10.1109\/TPAMI.2013.50","DOI":"10.1109\/TPAMI.2013.50"},{"key":"e_1_3_3_2_7_2","volume-title":"Advances in Neural Information Processing Systems","author":"Chen Chaofan","year":"2019","unstructured":"Chaofan Chen, Oscar Li, Daniel Tao, Alina Barnett, Cynthia Rudin, and Jonathan\u00a0K Su. 2019. This Looks Like That: Deep Learning for Interpretable Image Recognition. In Advances in Neural Information Processing Systems , Vol.\u00a032. Curran Associates, Inc.https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2019\/file\/adf7ee2dcf142b0e11888e72b43fcb75-Paper.pdf"},{"key":"e_1_3_3_2_8_2","doi-asserted-by":"publisher","unstructured":"Fenxiao Chen Yun-Cheng Wang Bin Wang and C.-C.\u00a0Jay Kuo. 2020. Graph representation learning: a survey. APSIPA Transactions on Signal and Information Processing 9 1 (2020). 10.1017\/atsip.2020.13","DOI":"10.1017\/atsip.2020.13"},{"key":"e_1_3_3_2_9_2","volume-title":"International Conference on Learning Representations","author":"Dosovitskiy Alexey","year":"2021","unstructured":"Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. 2021. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In International Conference on Learning Representations."},{"key":"e_1_3_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00726"},{"key":"e_1_3_3_2_11_2","doi-asserted-by":"publisher","unstructured":"Filipe Alves\u00a0de Fernando Daniel Carlos\u00a0Guimar\u00e3es Pedronette Gustavo Jos\u00e9\u00a0de Sousa Lucas\u00a0Pascotti Valem and Ivan\u00a0Rizzo Guilherme. 2022. RaDE+: A semantic rank-based graph embedding algorithm. International Journal of Information Management Data Insights 2 1 (2022) 100078. 10.1016\/j.jjimei.2022.100078","DOI":"10.1016\/j.jjimei.2022.100078"},{"key":"e_1_3_3_2_12_2","unstructured":"Srishti Gautam Ahcene Boubekki Marina\u00a0MC H\u00f6hne and Michael Kampffmeyer. 2024. Prototypical Self-Explainable Models Without Re-training. Transactions on Machine Learning Research (2024)."},{"key":"e_1_3_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_3_2_15_2","volume-title":"International Conference on Learning Representations","author":"Higgins Irina","year":"2017","unstructured":"Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner. 2017. beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=Sy2fzU9gl"},{"key":"e_1_3_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539321"},{"key":"e_1_3_3_2_17_2","doi-asserted-by":"publisher","unstructured":"Xuan Huang Lei Wu and Yinsong Ye. 2019. A Review on Dimensionality Reduction Techniques. International Journal of Pattern Recognition and Artificial Intelligence 33 10 (2019) 1950017. 10.1142\/S0218001419500174","DOI":"10.1142\/S0218001419500174"},{"key":"e_1_3_3_2_18_2","volume-title":"International Conference on Learning Representations","author":"Kipf Thomas\u00a0N.","year":"2017","unstructured":"Thomas\u00a0N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=SJU4ayYgl"},{"key":"e_1_3_3_2_19_2","series-title":"Proceedings of Machine Learning Research","first-page":"5338","volume-title":"Proceedings of the 37th International Conference on Machine Learning","volume":"119","author":"Koh Pang\u00a0Wei","year":"2020","unstructured":"Pang\u00a0Wei Koh, Thao Nguyen, Yew\u00a0Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, and Percy Liang. 2020. Concept Bottleneck Models. In Proceedings of the 37th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol.\u00a0119). PMLR, 5338\u20135348."},{"key":"e_1_3_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR56361.2022.9956591"},{"key":"e_1_3_3_2_21_2","doi-asserted-by":"publisher","unstructured":"Guang-Hai Liu and Jing-Yu Yang. 2013. Content-based image retrieval using color difference histogram. Pattern Recognition 46 1 (2013) 188\u2013198. 10.1016\/j.patcog.2012.06.001","DOI":"10.1016\/j.patcog.2012.06.001"},{"key":"e_1_3_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"e_1_3_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"e_1_3_3_2_24_2","volume-title":"International Conference on Machine Learning","author":"Locatello Francesco","year":"2019","unstructured":"Francesco Locatello, Stefan Bauer, Mario Lu\u010di\u0107, Gunnar R\u00e4tsch, Sylvain Gelly, Bernhard Sch\u00f6lkopf, and Olivier\u00a0Frederic Bachem. 2019. Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. In International Conference on Machine Learning. http:\/\/proceedings.mlr.press\/v97\/locatello19a.html Best Paper Award."},{"key":"e_1_3_3_2_25_2","series-title":"Proceedings of Machine Learning Research","first-page":"4212","volume-title":"Proceedings of the 36th International Conference on Machine Learning","volume":"97","author":"Ma Jianxin","year":"2019","unstructured":"Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, and Wenwu Zhu. 2019. Disentangled Graph Convolutional Networks. In Proceedings of the 36th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol.\u00a097). PMLR, 4212\u20134221. https:\/\/proceedings.mlr.press\/v97\/ma19a.html"},{"key":"e_1_3_3_2_26_2","doi-asserted-by":"publisher","unstructured":"Emanuele Marconato Andrea Passerini and Stefano Teso. 2023. Interpretability Is in the Mind of the Beholder: A Causal Framework for Human-Interpretable Representation Learning. Entropy 25 12 (2023). 10.3390\/e25121574","DOI":"10.3390\/e25121574"},{"key":"e_1_3_3_2_27_2","doi-asserted-by":"publisher","unstructured":"Leland McInnes John Healy Nathaniel Saul and Lukas Gro\u00dfberger. 2018. UMAP: Uniform Manifold Approximation and Projection. Journal of Open Source Software 3 29 (2018) 861. 10.21105\/joss.00861","DOI":"10.21105\/joss.00861"},{"key":"e_1_3_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2006.42"},{"key":"e_1_3_3_2_29_2","volume-title":"The Eleventh International Conference on Learning Representations","author":"Oikarinen Tuomas","year":"2023","unstructured":"Tuomas Oikarinen, Subhro Das, Lam\u00a0M. Nguyen, and Tsui-Wei Weng. 2023. Label-free Concept Bottleneck Models. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6248092"},{"key":"e_1_3_3_2_31_2","doi-asserted-by":"publisher","unstructured":"Daniel Carlos\u00a0Guimar\u00e3es Pedronette Lucas\u00a0Valem Pascotti and Longin\u00a0Jan Latecki. 2021. Efficient Rank-Based Diffusion Process with Assured Convergence. Journal of Imaging 7 3 (2021). 10.3390\/jimaging7030049","DOI":"10.3390\/jimaging7030049"},{"key":"e_1_3_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/SIBGRAPI.2015.28"},{"key":"e_1_3_3_2_33_2","doi-asserted-by":"publisher","unstructured":"Daniel Carlos\u00a0Guimar\u00e3es Pedronette Lucas\u00a0Pascotti Valem Jurandy Almeida and Ricardo da S.\u00a0Torres. 2019. Multimedia Retrieval Through Unsupervised Hypergraph-Based Manifold Ranking. IEEE Transactions on Image Processing 28 12 (2019) 5824\u20135838. 10.1109\/TIP.2019.2920526","DOI":"10.1109\/TIP.2019.2920526"},{"key":"e_1_3_3_2_34_2","doi-asserted-by":"publisher","unstructured":"Daniel Carlos\u00a0Guimar\u00e3es Pedronette Lucas\u00a0Pascotti Valem and Ricardo da S.\u00a0Torres. 2021. A BFS-Tree of ranking references for unsupervised manifold learning. Pattern Recognition 111 (2021) 107666. 10.1016\/j.patcog.2020.107666","DOI":"10.1016\/j.patcog.2020.107666"},{"key":"e_1_3_3_2_35_2","doi-asserted-by":"publisher","unstructured":"V.H. Pereira-Ferrero T.G. Lewis L.P. Valem L.G.P. Ferrero D.C.G. Pedronette and L.J. Latecki. 2024. Unsupervised affinity learning based on manifold analysis for image retrieval: A survey. Computer Science Review 53 (2024) 100657. 10.1016\/j.cosrev.2024.100657","DOI":"10.1016\/j.cosrev.2024.100657"},{"key":"e_1_3_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_3_2_37_2","doi-asserted-by":"publisher","unstructured":"Simone Piaggesi Megha Khosla Andr\u00e9 Panisson and Avishek Anand. 2024. DINE: Dimensional Interpretability of Node Embeddings. IEEE Transactions on Knowledge and Data Engineering 36 12 (2024) 7986\u20137997. 10.1109\/TKDE.2024.3425460","DOI":"10.1109\/TKDE.2024.3425460"},{"key":"e_1_3_3_2_38_2","unstructured":"Simone Piaggesi Andr\u00e9 Panisson and Megha Khosla. 2025. Disentangled and Self-Explainable Node Representation Learning. Transactions on Machine Learning Research (2025). https:\/\/openreview.net\/forum?id=s51TQ8Eg1e"},{"key":"e_1_3_3_2_39_2","doi-asserted-by":"crossref","unstructured":"Cynthia Rudin. 2019. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence 1 5 (May 2019) 206\u2013215.","DOI":"10.1038\/s42256-019-0048-x"},{"key":"e_1_3_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICTAI50040.2020.00154"},{"key":"e_1_3_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"e_1_3_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP46576.2022.9898060"},{"key":"e_1_3_3_2_43_2","doi-asserted-by":"publisher","unstructured":"Lucas\u00a0Pascotti Valem Daniel Carlos\u00a0Guimar\u00e3es Pedronette and Longin\u00a0Jan Latecki. 2023. Graph Convolutional Networks based on manifold learning for semi-supervised image classification. Computer Vision and Image Understanding 227 (2023) 103618. 10.1016\/j.cviu.2022.103618","DOI":"10.1016\/j.cviu.2022.103618"},{"key":"e_1_3_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/3078971.3079017"},{"key":"e_1_3_3_2_45_2","volume-title":"Advances in Neural Information Processing Systems","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan\u00a0N Gomez, \u0141\u00a0ukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In Advances in Neural Information Processing Systems , Vol.\u00a030. Curran Associates, Inc."},{"key":"e_1_3_3_2_46_2","volume-title":"International Conference on Learning Representations","author":"Veli\u010dkovi\u0107 Petar","year":"2019","unstructured":"Petar Veli\u010dkovi\u0107, William Fedus, William\u00a0L. Hamilton, Pietro Li\u00f2, Yoshua Bengio, and R\u00a0Devon Hjelm. 2019. Deep Graph Infomax. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=rklz9iAcKQ"},{"key":"e_1_3_3_2_47_2","volume-title":"The Caltech-UCSD Birds-200-2011 Dataset","author":"Wah Catherine","year":"2011","unstructured":"Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie. 2011. The Caltech-UCSD Birds-200-2011 Dataset. Technical Report."},{"key":"e_1_3_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00093"},{"key":"e_1_3_3_2_49_2","doi-asserted-by":"publisher","unstructured":"Xin Wang Hong Chen Si\u2019ao Tang Zihao Wu and Wenwu Zhu. 2024. Disentangled Representation Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 46 12 (2024) 9677\u20139696. 10.1109\/TPAMI.2024.3420937","DOI":"10.1109\/TPAMI.2024.3420937"},{"key":"e_1_3_3_2_50_2","series-title":"Proceedings of Machine Learning Research","first-page":"6861","volume-title":"Proceedings of the 36th International Conference on Machine Learning","volume":"97","author":"Wu Felix","year":"2019","unstructured":"Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019. Simplifying Graph Convolutional Networks. In Proceedings of the 36th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol.\u00a097). PMLR, 6861\u20136871. https:\/\/proceedings.mlr.press\/v97\/wu19e.html"},{"key":"e_1_3_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01839"},{"key":"e_1_3_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00974"},{"key":"e_1_3_3_2_53_2","doi-asserted-by":"publisher","unstructured":"Liang Zheng Yi Yang and Qi Tian. 2018. SIFT Meets CNN: A Decade Survey of Instance Retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 40 5 (2018) 1224\u20131244. 10.1109\/TPAMI.2017.2709749","DOI":"10.1109\/TPAMI.2017.2709749"}],"event":{"name":"ICMR '26: International Conference on Multimedia Retrieval","location":"Amsterdam The Netherlands","acronym":"ICMR '26","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 2026 International Conference on Multimedia Retrieval"],"original-title":[],"deposited":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T15:46:02Z","timestamp":1781538362000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3805622.3810617"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,15]]},"references-count":52,"alternative-id":["10.1145\/3805622.3810617","10.1145\/3805622"],"URL":"https:\/\/doi.org\/10.1145\/3805622.3810617","relation":{},"subject":[],"published":{"date-parts":[[2026,6,15]]},"assertion":[{"value":"2026-06-15","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}