{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T06:40:13Z","timestamp":1735627213633,"version":"3.32.0"},"reference-count":20,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2024,8]]},"abstract":"<jats:p>We demonstrate MaskSearch, a system designed to accelerate queries over databases of image masks generated by machine learning models. MaskSearch formalizes and accelerates a new category of queries for retrieving images and their corresponding masks based on mask properties, which support various applications, from identifying spurious correlations learned by models to exploring discrepancies between model saliency and human attention. This demonstration makes the following contributions: (1) the introduction of MaskSearch's graphical user interface (GUI), which enables interactive exploration of image databases through mask properties, (2) hands-on opportunities for users to explore MaskSearch's capabilities and constraints within machine learning workflows, and (3) an opportunity for conference attendees to understand how MaskSearch accelerates queries over image masks.<\/jats:p>","DOI":"10.14778\/3685800.3685859","type":"journal-article","created":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T17:25:21Z","timestamp":1731086721000},"page":"4297-4300","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Demonstration of MaskSearch: Efficiently Querying Image Masks for Machine Learning Workflows"],"prefix":"10.14778","volume":"17","author":[{"given":"Lindsey Linxi","family":"Wei","sequence":"first","affiliation":[{"name":"University of Washington"}]},{"given":"Chung Yik Edward","family":"Yeung","sequence":"additional","affiliation":[{"name":"University of Washington"}]},{"given":"Hongjian","family":"Yu","sequence":"additional","affiliation":[{"name":"University of Washington"}]},{"given":"Jingchuan","family":"Zhou","sequence":"additional","affiliation":[{"name":"University of Washington"}]},{"given":"Dong","family":"He","sequence":"additional","affiliation":[{"name":"University of Washington"}]},{"given":"Magdalena","family":"Balazinska","sequence":"additional","affiliation":[{"name":"University of Washington"}]}],"member":"320","published-online":{"date-parts":[[2024,11,8]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"https:\/\/datafromsky.com\/traffic-monitoring\/. Last accessed","author":"Monitoring Traffic","year":"2024","unstructured":"2024. Traffic Monitoring. https:\/\/datafromsky.com\/traffic-monitoring\/. Last accessed: Jul. 18, 2024."},{"key":"e_1_2_1_2_1","volume-title":"Human attention in visual question answering: Do humans and deep networks look at the same regions? Computer Vision and Image Understanding","author":"Das","year":"2017","unstructured":"Das el al. 2017. Human attention in visual question answering: Do humans and deep networks look at the same regions? Computer Vision and Image Understanding (2017)."},{"key":"e_1_2_1_3_1","first-page":"1","article-title":"Finding a Needle in Haystack: Facebook's Photo Storage","volume":"10","author":"Beaver","year":"2010","unstructured":"Beaver et al. 2010. Finding a Needle in Haystack: Facebook's Photo Storage. In OSDI, Vol. 10. 1--8.","journal-title":"OSDI"},{"key":"e_1_2_1_4_1","unstructured":"Beery et al. 2020. The iWildCam 2020 Competition Dataset. arXiv preprint arXiv:2004.10340 (2020)."},{"key":"e_1_2_1_5_1","volume-title":"Imagenet: A large-scale hierarchical image database. In CVPR. 248--255.","author":"Deng","year":"2009","unstructured":"Deng et al. 2009. Imagenet: A large-scale hierarchical image database. In CVPR. 248--255."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-021-00338-7"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/2.410146"},{"key":"e_1_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Hong et al. 2020. Human factors in model interpretability: Industry practices challenges and needs. PACM HCI 4 CSCW1 (2020) 1--26.","DOI":"10.1145\/3392878"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.14778\/3485450.3485460"},{"key":"e_1_2_1_10_1","unstructured":"He et al. 2023. MaskSearch: Querying Image Masks at Scale. arXiv preprint arXiv:2305.02375 (2023)."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE48307.2020.00201"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.14778\/3025111.3025117"},{"key":"e_1_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Redmon et al. 2016. You only look once: Unified real-time object detection. In CVPR. 779--788.","DOI":"10.1109\/CVPR.2016.91"},{"key":"e_1_2_1_14_1","unstructured":"Rong et al. 2021. Human attention in fine-grained classification. arXiv preprint arXiv:2111.01628 (2021)."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.14778\/3476311.3476381"},{"key":"e_1_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Selvaraju et al. 2017. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. In ICCV. 618--626.","DOI":"10.1109\/ICCV.2017.74"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3299869.3300073"},{"key":"e_1_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Teso et al. 2023. Leveraging explanations in interactive machine learning: An overview. Frontiers in Artificial Intelligence 6 (2023).","DOI":"10.3389\/frai.2023.1066049"},{"key":"e_1_2_1_19_1","volume-title":"Technical Report CNSTR-2011-001. California Institute of Technology.","author":"Wah","year":"2011","unstructured":"Wah et al. 2011. Caltech-UCSD Birds-200-2011 Dataset. Technical Report CNSTR-2011-001. California Institute of Technology."},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1002\/int.22458"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3685800.3685859","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T05:28:37Z","timestamp":1735622917000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3685800.3685859"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8]]},"references-count":20,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["10.14778\/3685800.3685859"],"URL":"https:\/\/doi.org\/10.14778\/3685800.3685859","relation":{},"ISSN":["2150-8097"],"issn-type":[{"type":"print","value":"2150-8097"}],"subject":[],"published":{"date-parts":[[2024,8]]},"assertion":[{"value":"2024-11-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}