Theses and Dissertations from UMD

Permanent URI for this communityhttp://hdl.handle.net/1903/2

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM

More information is available at Theses and Dissertations at University of Maryland Libraries.

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    Advance Video Modeling Techniques for Video Generation and Enhancement Tasks
    (2024) Shrivastava, Gaurav; Shrivastava, Abhinav; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    This thesis investigates advanced techniques that are useful in video modeling for generation and enhancement tasks. In the first part of the thesis, we explore generative modeling that exploits the external corpus for learning priors. The task here is of video prediction, i.e., to extrapolate future sequences given a few context frames. In a followup work we also demonstrate how can we reduce the inference time further and make the video prediction model more efficient. Additionally, we demonstrate that we are not only able to extrapolate one future sequence from a given context frame but multiple sequences given context frames. In the second part, we explore the methods that exploit internal statistics of videos to perform various restoration and enhancement tasks. Here, we show how robustly they perform the restoration tasks like denoising, super-resolution, frame interpolation, and object removal tasks. Furthermore, in a follow-up work, we utilize the inherent compositionality of videos and internal statistics to perform a wider variety of enhancement tasks such as relighting, dehazing, and foreground/background manipulations. Lastly, we provide insight into our future work on how data-free enhancement techniques could be improved. Additionally, we provide further insights on how multisteps video prediction techniques can be improved.
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    Exploring The Role Of Generative Artificial Intelligence In Cultural Relevant Storytelling For Native Language Learning Among Children
    (2024) Nanduri, Dinesh Kumar; Marsh, Diana E; Information Studies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    In an era marked by the rapid disappearance of languages, UNESCO warns that nearly half of the world's linguistic heritage might soon become dormant. Despite its current health, Telugu has seen a decline in usage, reduced focus in India's educational systems, and overshadowing by dominant global languages. This thesis explores Generative Artificial Intelligence (GenAI) to counter this trend, focusing on its application in native language learning for children, key carriers of their ancestral tongues. Through scoping reviews and participatory design sessions with young Telugu-speaking learners and their guardians, the study investigates GenAI's role in enhancing language learning tailored to individual and cultural contexts. It highlights storytelling as a potent mechanism for language acquisition, facilitated by GenAI's ability to personalize learning experiences and bridge generational gaps. The research also addresses ethical considerations vital for designing GenAI tools, promoting inclusivity, bias mitigation, and cultural integrity protection. It showcases a future where technology helps prevent linguistic dormancy and empowers children to celebrate human language and cultural diversity.