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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2023,10,31]]},"abstract":"<jats:p>\n            The prevailing framework for matching multimodal inputs is based on a two-stage process: (1) detecting proposals with an object detector and (2) matching text queries with proposals. Existing two-stage solutions mostly focus on the matching step. In this article, we argue that these methods overlook an obvious\n            <jats:italic>mismatch<\/jats:italic>\n            between the roles of proposals in the two stages: they generate proposals solely based on the detection confidence (i.e., query-agnostic), hoping that the proposals contain all instances mentioned in the text query (i.e., query-aware). Due to this mismatch, chances are that proposals relevant to the text query are suppressed during the filtering process, which in turn bounds the matching performance. To this end, we propose VL-NMS, which is the first method to yield query-aware proposals at the first stage. VL-NMS regards all mentioned instances as critical objects and introduces a lightweight module to predict a score for aligning each proposal with a critical object. These scores can guide the NMS operation to filter out proposals irrelevant to the text query, increasing the recall of critical objects, and resulting in a significantly improved matching performance. Since VL-NMS is agnostic to the matching step, it can be easily integrated into any state-of-the-art two-stage matching method. We validate the effectiveness of VL-NMS on three multimodal matching tasks, namely referring expression grounding, phrase grounding, and image-text matching. Extensive ablation studies on several baselines and benchmarks consistently demonstrate the superiority of VL-NMS.\n          <\/jats:p>","DOI":"10.1145\/3579095","type":"journal-article","created":{"date-parts":[[2023,1,4]],"date-time":"2023-01-04T12:59:05Z","timestamp":1672837145000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["VL-NMS: Breaking Proposal Bottlenecks in Two-stage Visual-language Matching"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9034-4506","authenticated-orcid":false,"given":"Chenchi","family":"Zhang","sequence":"first","affiliation":[{"name":"Zhejiang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3188-1811","authenticated-orcid":false,"given":"Wenbo","family":"Ma","sequence":"additional","affiliation":[{"name":"Zhejiang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6142-9914","authenticated-orcid":false,"given":"Jun","family":"Xiao","sequence":"additional","affiliation":[{"name":"Zhejiang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7374-8739","authenticated-orcid":false,"given":"Hanwang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7842-7616","authenticated-orcid":false,"given":"Jian","family":"Shao","sequence":"additional","affiliation":[{"name":"Zhejiang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9017-2508","authenticated-orcid":false,"given":"Yueting","family":"Zhuang","sequence":"additional","affiliation":[{"name":"Zhejiang University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6148-9709","authenticated-orcid":false,"given":"Long","family":"Chen","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,6,7]]},"reference":[{"key":"e_1_3_2_2_2","volume-title":"arXiv preprint arXiv:2005.01655","author":"Akula Arjun R.","year":"2020","unstructured":"Arjun R. 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In Advances in Neural Information Processing Systems, Vol. 30."},{"key":"e_1_3_2_66_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICME52920.2022.9859830"},{"key":"e_1_3_2_67_2","first-page":"394","article-title":"Learning two-branch neural networks for image-text matching tasks","author":"Wang Liwei","year":"2018","unstructured":"Liwei Wang, Yin Li, Jing Huang, and Svetlana Lazebnik. 2018. Learning two-branch neural networks for image-text matching tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2018), 394\u2013407.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_2_68_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00206"},{"key":"e_1_3_2_69_2","article-title":"Google\u2019s neural machine translation system: Bridging the gap between human and machine translation","author":"Wu Yonghui","year":"2016","unstructured":"Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et\u00a0al. 2016. Google\u2019s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144 (2016).","journal-title":"arXiv preprint arXiv:1609.08144"},{"key":"e_1_3_2_70_2","doi-asserted-by":"publisher","DOI":"10.1145\/3240508.3240521"},{"key":"e_1_3_2_71_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.327"},{"key":"e_1_3_2_72_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i4.16406"},{"key":"e_1_3_2_73_2","first-page":"1","article-title":"Cross-modal hybrid feature fusion for image-sentence matching","author":"Xu Xing","year":"2021","unstructured":"Xing Xu, Yifan Wang, Yixuan He, Yang Yang, Alan Hanjalic, and Heng Tao Shen. 2021. Cross-modal hybrid feature fusion for image-sentence matching. 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In arXiv preprint arXiv:1912.01674."},{"key":"e_1_3_2_75_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00928"},{"key":"e_1_3_2_76_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00474"},{"key":"e_1_3_2_77_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00997"},{"key":"e_1_3_2_78_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58568-6_23"},{"key":"e_1_3_2_79_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00478"},{"key":"e_1_3_2_80_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01506"},{"key":"e_1_3_2_81_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2020.2971171"},{"key":"e_1_3_2_82_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01075"},{"key":"e_1_3_2_83_2","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00166"},{"key":"e_1_3_2_84_2","first-page":"1","article-title":"Multi-source multi-level attention networks for visual question answering","author":"Yu Dongfei","year":"2019","unstructured":"Dongfei Yu, Jianlong Fu, Xinmei Tian, and Tao Mei. 2019. Multi-source multi-level attention networks for visual question answering. ACM Transactions on Multimedia Computing, Communications, and Applications (2019), 1\u201320.","journal-title":"ACM Transactions on Multimedia Computing, Communications, and Applications"},{"key":"e_1_3_2_85_2","first-page":"4467","article-title":"Multimodal transformer with multi-view visual representation for image captioning","author":"Yu Jun","year":"2019","unstructured":"Jun Yu, Jing Li, Zhou Yu, and Qingming Huang. 2019. Multimodal transformer with multi-view visual representation for image captioning. 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In arXiv preprint arXiv:1805.03508."},{"key":"e_1_3_2_91_2","first-page":"2761","article-title":"Knowledge-representation-enhanced multimodal transformer for scene text visual question answering","author":"Yu Zhou","year":"2022","unstructured":"Zhou Yu, Zhu Junjie Yu, Jun, and Zhenzhong Kuang. 2022. Knowledge-representation-enhanced multimodal transformer for scene text visual question answering. Journal of Image and Graphics (2022), 2761\u20132774.","journal-title":"Journal of Image and Graphics"},{"key":"e_1_3_2_92_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00437"},{"key":"e_1_3_2_93_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2019.2931352"},{"key":"e_1_3_2_94_2","volume-title":"arXiv preprint arXiv:2203.16265","author":"Zhu Chaoyang","year":"2022","unstructured":"Chaoyang Zhu, Yiyi Zhou, Yunhang Shen, Gen Luo, Xingjia Pan, Mingbao Lin, Chao Chen, Liujuan Cao, Xiaoshuai Sun, and Rongrong Ji. 2022. SeqTR: A simple yet universal network for visual grounding. 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