{"title":"Multimedia Retrieval in Mixed Reality: Leveraging Live Queries for Immersive Experiences","authors":"Rahel Arnold, H. Schuldt","doi":"10.1109/AIxVR59861.2024.00048","DOIUrl":"https://doi.org/10.1109/AIxVR59861.2024.00048","url":null,"abstract":"Recent advancements in Mixed Reality (MR) technology and the exponential growth of multimedia data production have led to the emergence of innovative approaches for efficient content retrieval. This paper introduces Mixed Reality Multimedia Retrieval ((MR)2), a groundbreaking concept at the convergence of MR and multimedia retrieval. At its core, (MR)2 leverages MR’s transformative capabilities with an innovative live query option, allowing users to initiate queries intuitively through real-world object interactions. By autonomously generating queries based on object recognition in the user’s field of view, (MR)2 facilitates the retrieval of similar multimedia content from a connected database. The technical backbone of the (MR)2 framework includes object detection (YOLOv8), semantic similarity search (CLIP), and data management (Cottontail DB). Our research redefines user interactions with multimedia databases, seamlessly bridging the physical and digital domains. A successful iOS prototype application demonstrates promising results, paving the way for immersive and context-aware multimedia retrieval in the MR era.","PeriodicalId":518749,"journal":{"name":"2024 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR)","volume":"199 2","pages":"289-293"},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Optimizing Robotic Automatic Suturing Through VR-Enhanced Data Generation for Reinforcement Learning Algorithms","authors":"Nieto N., Sánchez J.A., Aguirre M.G., Félix F., Muñoz L.A.","doi":"10.1109/AIxVR59861.2024.00064","DOIUrl":"https://doi.org/10.1109/AIxVR59861.2024.00064","url":null,"abstract":"This paper explores the integration of Virtual Reality(VR) to a Surgical Robotic Simulation to enhance the quality of data used for training a ground-truth algorithm for surgical procedures performed by the DaVinci robot inside a simulated environment. As it is to be demonstrated by this paper, VR and Reinforcement Learning (RL) techniques can significantly improve the realism and effectiveness of the training data compared to traditional methods. It also investigates and deepens the study of incorporating Cognitive Vision theories to guide the learning process, following the premise that the full-immersion visual and haptic feedback models will result in better quality training data for the surgical robot to perform an autonomous surgery algorithm, leading to more accurate and adaptable minimally-invasive robotic surgery (MIRS) systems. This research was inspired and shaped by our active participation in the 2023-2024 AccelNet Surgical Robotics Challenge.","PeriodicalId":518749,"journal":{"name":"2024 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR)","volume":"226 3","pages":"375-383"},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}