{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T17:11:28Z","timestamp":1783962688107,"version":"3.55.0"},"reference-count":57,"publisher":"Association for Computing Machinery (ACM)","issue":"12","license":[{"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.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"crossref","award":["2023YFC3305600"],"award-info":[{"award-number":["2023YFC3305600"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100002338","name":"Ministry of Education of China","doi-asserted-by":"crossref","award":["8091B022149, 8091B02072404"],"award-info":[{"award-number":["8091B022149, 8091B02072404"]}],"id":[{"id":"10.13039\/501100002338","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62132016, 62171343, and 62071361"],"award-info":[{"award-number":["62132016, 62171343, and 62071361"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["ZDRC2102"],"award-info":[{"award-number":["ZDRC2102"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2024,12,31]]},"abstract":"<jats:p>Transformers have been recognized as powerful tools for various cross-modal tasks due to their superior ability to perform representation learning through self-attention. Existing transformer-based cross-modal models can be categorized into single-stream and dual-stream ones. By performing fine-grained interaction with self-attention on the cross-modal concatenated features, the former can simultaneously learn intra- and inter-modal correlations. However, this simple concatenation treats the inputs of different modalities equally; as a result, the heterogeneous differences between modalities are ignored, leading to a modality gap. The latter process the inputs of different modalities separately, then perform cross-modal interaction on the subsequently fused networks, resulting in a failure to integrate the fine-grained correlations of both intra- and inter-modality in a uniform module. To this end, we propose an effective heterogeneous graph transformer for dual-stream cross-modal representation learning, named CrossFormer, which constructs a heterogeneous graph as a bridge to achieve fine-grained intra- and inter-modal interaction on a dual-stream network. Specifically, we first represent multi-modal data with a heterogeneous graph, then develop a dual-positional encoding strategy that enables the heterogeneous graph to obtain the relative positional information. Finally, a dual-stream self-attention is performed on the heterogeneous graph, bridging the gap between modalities and effectively capturing fine-grained intra- and inter-modal interactions simultaneously. Extensive experiments on various cross-modal tasks demonstrate the superiority of our method.<\/jats:p>","DOI":"10.1145\/3688801","type":"journal-article","created":{"date-parts":[[2024,9,20]],"date-time":"2024-09-20T13:43:58Z","timestamp":1726839838000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":20,"title":["CrossFormer: Cross-Modal Representation Learning via Heterogeneous Graph Transformer"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-0504-4506","authenticated-orcid":false,"given":"Xiao","family":"Liang","sequence":"first","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xi\u2019an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0855-1646","authenticated-orcid":false,"given":"Erkun","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xi\u2019an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2620-3247","authenticated-orcid":false,"given":"Cheng","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xi\u2019an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7916-3683","authenticated-orcid":false,"given":"Yanhua","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xidian University, Xi\u2019an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,11,21]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00636"},{"key":"e_1_3_1_3_2","first-page":"15789","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Chen Jiacheng","year":"2021","unstructured":"Jiacheng Chen, Hexiang Hu, Hao Wu, Yuning Jiang, and Changhu Wang. 2021. Learning the best pooling strategy for visual semantic embedding. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 15789\u201315798."},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102133"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58577-8_7"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","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. 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv:2010.11929. Retrieved from 10.48550\/arXiv.2010.11929","DOI":"10.48550\/arXiv.2010.11929"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.670"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","unstructured":"Zhicheng Huang Zhaoyang Zeng Bei Liu Dongmei Fu and Jianlong Fu. 2020. Pixel-bert: Aligning image pixels with text by deep multi-modal transformers. arXiv:2004.00849. Retrieved from 10.48550\/arXiv.2004.00849","DOI":"10.48550\/arXiv.2004.00849"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/1460096.1460104"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","unstructured":"Yuqi Huo Manli Zhang Guangzhen Liu Haoyu Lu Yizhao Gao Guoxing Yang Jingyuan Wen Heng Zhang Baogui Xu Weihao Zheng Zongzheng Xi Yueqian Yang Anwen Hu Jinming Zhao Ruichen Li Yida Zhao Liang Zhang Yuqing Song Xin Hong Wanqing Cui Danyang Hou Yingyan Li Junyi Li Peiyu Liu Zheng Gong Chuhao Jin Yuchong Sun Shizhe Chen Zhiwu Lu Zhicheng Dou Qin Jin Yanyan Lan Wayne Xin Zhao Ruihua Song and Ji-Rong Wen. 2021. WenLan: Bridging vision and language by large-scale multi-modal pre-training. arXiv:2103.06561. Retrieved from 10.48550\/arXiv.2103.06561","DOI":"10.48550\/arXiv.2103.06561"},{"key":"e_1_3_1_12_2","first-page":"4904","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Jia Chao","year":"2021","unstructured":"Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. 2021. Scaling up visual and vision-language representation learning with noisy text supervision. In Proceedings of the International Conference on Machine Learning. PMLR, 4904\u20134916."},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6767"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.348"},{"key":"e_1_3_1_15_2","first-page":"5583","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Kim Wonjae","year":"2021","unstructured":"Wonjae Kim, Bokyung Son, and Ildoo Kim. 2021. ViLT: Vision-and-language transformer without convolution or region supervision. In Proceedings of the International Conference on Machine Learning. PMLR, 5583\u20135594."},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","unstructured":"Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv:1412.6980. Retrieved from 10.48550\/arXiv.1412.6980","DOI":"10.48550\/arXiv.1412.6980"},{"key":"e_1_3_1_17_2","first-page":"1097","volume-title":"In Proceedings of the Advances in Neural Information Processing Systems","volume":"25","author":"Krizhevsky Alex","year":"2012","unstructured":"Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 25, 1097\u20131105."},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6795"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","unstructured":"Liunian Harold Li Mark Yatskar Da Yin Cho-Jui Hsieh and Kai-Wei Chang. 2019. Visualbert: A simple and performant baseline for vision and language. arXiv:1908.03557. Retrieved from 10.48550\/arXiv.1908.03557","DOI":"10.48550\/arXiv.1908.03557"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58577-8_8"},{"key":"e_1_3_1_21_2","first-page":"17612","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","volume":"35","author":"Liang Victor Weixin","year":"2022","unstructured":"Victor Weixin Liang, Yuhui Zhang, Yongchan Kwon, Serena Yeung, and James Y. Zou. 2022. Mind the gap: Understanding the modality gap in multi-modal contrastive representation learning. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 35, 17612\u201317625."},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3107235"},{"key":"e_1_3_1_24_2","first-page":"3419","volume-title":"Proceedings of the Neural Information Processing Systems (NeurIPS \u201914)","author":"Liu Wei","year":"2014","unstructured":"Wei Liu, Cun Mu, Sanjiv Kumar, and Shih-Fu Chang. 2014. Discrete graph hashing. In Proceedings of the Neural Information Processing Systems (NeurIPS \u201914), 3419\u20133427."},{"key":"e_1_3_1_25_2","first-page":"15692","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Lu Haoyu","year":"2022","unstructured":"Haoyu Lu, Nanyi Fei, Yuqi Huo, Yizhao Gao, Zhiwu Lu, and Ji-Rong Wen. 2022. COTS: Collaborative two-stream vision-language pre-training model for cross-modal retrieval. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 15692\u201315701."},{"key":"e_1_3_1_26_2","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","volume":"32","author":"Lu Jiasen","year":"2019","unstructured":"Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. 2019. Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 32."},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00754"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2020.3044473"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/3343031.3350999"},{"key":"e_1_3_1_30_2","first-page":"5s","article-title":"TEVL: Trilinear Encoder for Video-language Representation Learning","volume":"19","author":"Man Xin","year":"2023","unstructured":"Xin Man, Jie Shao, Feiyu Chen, Mingxing Zhang, and Heng Tao Shen. 2023. TEVL: Trilinear Encoder for Video-language Representation Learning. ACM Transactions on Multimedia Computing, Communications and Applications 19, 5s (2023), 1\u201320.","journal-title":"ACM Transactions on Multimedia Computing, Communications and Applications"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2017.2729400"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","unstructured":"Sharan Narang Hyung Won Chung Yi Tay William Fedus Thibault Fevry Michael Matena Karishma Malkan Noah Fiedel Noam Shazeer Zhenzhong Lan Yanqi Zhou Wei Li Nan Ding Jake Marcus Adam Roberts and Colin Raffel. 2021. Do transformer modifications transfer across implementations and applications? arXiv:2102.11972. Retrieved from 10.48550\/arXiv.2102.11972","DOI":"10.48550\/arXiv.2102.11972"},{"key":"e_1_3_1_33_2","first-page":"11","article-title":"Deep multimodal learning for affective analysis and retrieval","volume":"17","author":"Pang Lei","year":"2015","unstructured":"Lei Pang, Shiai Zhu, and Chong-Wah Ngo. 2015. Deep multimodal learning for affective analysis and retrieval. IEEE Transactions on Multimedia 17, 11 (2015), 2008\u20132020.","journal-title":"IEEE Transactions on Multimedia"},{"key":"e_1_3_1_34_2","first-page":"8748","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Radford Alec","year":"2021","unstructured":"Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021. Learning transferable visual models from natural language supervision. In Proceedings of the International Conference on Machine Learning. PMLR, 8748\u20138763."},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/2500492"},{"key":"e_1_3_1_36_2","doi-asserted-by":"crossref","unstructured":"Hao Tan and Mohit Bansal. 2019. LXMERT: Learning cross-modality encoder representations from transformers. arXiv:1908.07490.","DOI":"10.18653\/v1\/D19-1514"},{"key":"e_1_3_1_37_2","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","volume":"30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 30."},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2587640"},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.541"},{"key":"e_1_3_1_40_2","first-page":"23318","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Wang Peng","year":"2022","unstructured":"Peng Wang, An Yang, Rui Men, Junyang Lin, Shuai Bai, Zhikang Li, Jianxin Ma, Chang Zhou, Jingren Zhou, and Hongxia Yang. 2022. OFA: Unifying architectures, tasks, and modalities through a simple sequence-to-sequence learning framework. In Proceedings of the International Conference on Machine Learning. PMLR, 23318\u201323340."},{"key":"e_1_3_1_41_2","doi-asserted-by":"crossref","first-page":"1s","DOI":"10.1145\/3408317","article-title":"Survey on deep multi-modal data analytics: Collaboration, rivalry, and fusion","volume":"17","author":"Wang Yang","year":"2021","unstructured":"Yang Wang. 2021. Survey on deep multi-modal data analytics: Collaboration, rivalry, and fusion. ACM Transactions on Multimedia Computing, Communications and Applications 17, 1s (2021), 1\u201325.","journal-title":"ACM Transactions on Multimedia Computing, Communications and Applications"},{"key":"e_1_3_1_42_2","first-page":"2208","volume-title":"Proceedings of the IEEE International Conference on Computer Vision (ICCV \u201921)","author":"Wen Keyu","year":"2021","unstructured":"Keyu Wen, Jin Xia, Yuanyuan Huang, Linyang Li, Jiayan Xu, and Jie Shao. 2021. COOKIE: Contrastive cross-modal knowledge sharing pre-training for vision-language representation. In Proceedings of the IEEE International Conference on Computer Vision (ICCV \u201921), 2208\u20132217."},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","unstructured":"Yonghui Wu Mike Schuster Zhifeng Chen Quoc V. Le Mohammad Norouzi Wolfgang Macherey Maxim Krikun Yuan Cao Qin Gao Klaus Macherey Jeff Klingner Apurva Shah Melvin Johnson Xiaobing Liu \u0141ukasz Kaiser Stephan Gouws Yoshikiyo Kato Taku Kudo Hideto Kazawa Keith Stevens George Kurian Nishant Patil Wei Wang Cliff Young Jason Smith Jason Riesa Alex Rudnick Oriol Vinyals Greg Corrado Macduff Hughes and Jeffrey Dean. 2016. Google\u2019s neural machine translation system: Bridging the gap between human and machine translation. arXiv:1609.08144. Retrieved from 10.48550\/arXiv.1609.08144","DOI":"10.48550\/arXiv.1609.08144"},{"key":"e_1_3_1_44_2","first-page":"2088","volume-title":"Proceedings of the ACM International Conference on Multimedia","author":"Wu Yiling","year":"2019","unstructured":"Yiling Wu, Shuhui Wang, Guoli Song, and Qingming Huang. 2019. Learning fragment self-attention embeddings for image-text matching. In Proceedings of the ACM International Conference on Multimedia, 2088\u20132096."},{"issue":"2","key":"e_1_3_1_45_2","first-page":"1","article-title":"Hierarchical transformer with spatio-temporal context aggregation for next point-of-interest recommendation","volume":"42","author":"Xie Jiayi","year":"2023","unstructured":"Jiayi Xie and Zhenzhong Chen. 2023. Hierarchical transformer with spatio-temporal context aggregation for next point-of-interest recommendation. ACM Transactions on Information Systems 42, 2 (2023), 1\u201330.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_1_46_2","first-page":"10524","volume-title":"Proceedings of the International Conference on Machine Learning (ICML \u201920)","author":"Xiong Ruibin","year":"2020","unstructured":"Ruibin Xiong, Yunchang Yang, Di He, Kai Zheng, Shuxin Zheng, Chen Xing, Huishuai Zhang, Yanyan Lan, Liwei Wang, and Tieyan Liu. 2020. On layer normalization in the transformer architecture. In Proceedings of the International Conference on Machine Learning (ICML \u201920). PMLR, 10524\u201310533."},{"key":"e_1_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298966"},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00306"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01094"},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_42"},{"key":"e_1_3_1_51_2","first-page":"28877","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","volume":"34","author":"Ying Chengxuan","year":"2021","unstructured":"Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu. 2021. Do transformers really perform badly for graph representation? In Proceedings of the Advances in Neural Information Processing Systems, Vol. 34, 28877\u201328888."},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i4.16431"},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2022.3141603"},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00553"},{"key":"e_1_3_1_55_2","first-page":"3s","article-title":"Affective interaction: Attentive representation learning for multi-modal sentiment classification","volume":"18","author":"Zhang Yazhou","year":"2022","unstructured":"Yazhou Zhang, Prayag Tiwari, Lu Rong, Rui Chen, Nojoom A. AlNajem, and M. Shamim Hossain. 2022. Affective interaction: Attentive representation learning for multi-modal sentiment classification. ACM Transactions on Multimedia Computing, Communications and Applications 18, 3s (2022), 1\u201323.","journal-title":"ACM Transactions on Multimedia Computing, Communications and Applications"},{"key":"e_1_3_1_56_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.2974065"},{"issue":"1","key":"e_1_3_1_57_2","first-page":"239","article-title":"Multi-modal hashing for efficient multimedia retrieval: A survey","volume":"36","author":"Zhu Lei","year":"2023","unstructured":"Lei Zhu, Chaoqun Zheng, Weili Guan, Jingjing Li, Yang Yang, and Heng Tao Shen. 2023. Multi-modal hashing for efficient multimedia retrieval: A survey. IEEE Transactions on Knowledge and Data Engineering 36, 1 (2023), 239\u2013260.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_58_2","first-page":"3375","article-title":"Multimodal sentiment analysis with image-text interaction network","volume":"25","author":"Zhu Tong","year":"2022","unstructured":"Tong Zhu, Leida Li, Jufeng Yang, Sicheng Zhao, Hantao Liu, and Jiansheng Qian. 2022. Multimodal sentiment analysis with image-text interaction network. IEEE Transactions on Multimedia 25 (2022), 3375\u20133385.","journal-title":"IEEE Transactions on Multimedia"}],"container-title":["ACM Transactions on Multimedia Computing, Communications, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3688801","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3688801","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:04:09Z","timestamp":1750291449000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3688801"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,21]]},"references-count":57,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2024,12,31]]}},"alternative-id":["10.1145\/3688801"],"URL":"https:\/\/doi.org\/10.1145\/3688801","relation":{},"ISSN":["1551-6857","1551-6865"],"issn-type":[{"value":"1551-6857","type":"print"},{"value":"1551-6865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,21]]},"assertion":[{"value":"2023-08-22","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-07-25","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-11-21","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}