Titlebook: Information and Communication Technology for Competitive Strategies (ICTCS 2020); Intelligent Strategi M. Shamim Kaiser,Juanying Xie,Vijay

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Chinmay Khamkar,Manav Shah,Samip Kalyani,Kiran Bhowmickdeo temporal localization models rely on specific datasets for training, with high data collection costs, but exhibit poor generalization capability under the across-dataset and out-of-distribution (OOD) settings. In this paper, we propose a .raining-.ree .ideo .emporal .rounding (TFVTG) approach th

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Reema Roychaudhary,Rekha Shahapurkar. Dense visual SLAM, leveraging RGB-D camera systems, offers advantages but faces challenges in achieving real-time performance, robustness, and scalability for large-scale scenes. Recent approaches utilizing neural implicit scene representations show promise but suffer from high computational costs

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Shradha Itkare,Arati Manjaramkaradversarial augmentation in the supervised learning generalization, naively incorporating it into self-supervised MDE models potentially causes over-regularization, suffering from severe performance degradation. In this paper, we conduct qualitative analysis and illuminate the main causes: (i) inher

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H. V. Chaitra,Ramachandra,Chandani Sah,Saahithi Pradhan,Soundarya Kuralla,Vanitha Sreeusion model that dynamically adapts to scene graphs. Existing methods struggle to handle scene graphs due to varying numbers of nodes, multiple edge combinations, and manipulator-induced node-edge operations. EchoScene overcomes this by associating each node with a denoising process and enables coll