Browsing by Author "Patil, V."

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    Cognitive Chromatic Image Synthesis Using UNET and GAN
    (Institute of Electrical and Electronics Engineers Inc., 2024) Choubey, D.; Patil, V.; Anand Kumar, M.
    In the last decade, there has been a lot of interest for image colorization over a wide range of applications, especially in the restoration of old or damaged images. Because there are a lot of options when it comes to assigning color information, this problem is ill-posed by nature and is quite difficult to solve. Researchers have handled this issue in a variety of imaginative ways. More recent developments in automated colorization are focused on images that are repetitive in nature or images that require extensive editing. For instance, in such settings, semantic maps can be used as additional input to offer better control over the generalization of the colorization task with the help of conditional Deep Convolutional Generative Adversarial Networks (DCGANs).Our solution combines the techniques to allow computers to produce vivid visuals in this way. Monochrome or black and white images most of the times differ from the colored images in terms of visual detail and image content and colorizing them by hand is a tedious and often an artistic task. © 2024 IEEE.
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    Detecting Document Versions and Their Ordering in a Collection
    (Springer Science and Business Media Deutschland GmbH, 2021) Modani, N.; Maurya, A.; Verma, G.; Nair, I.; Patil, V.; Kanfade, A.
    Given the iterative and collaborative nature of authoring and the need to adapt the documents for different audience, people end up with a large number of versions of their documents. These additional versions of documents increase the required cognitive effort for various tasks for humans (such as finding the latest version of a document, or organizing documents), and may degrade the performance of machine tasks such as clustering or recommendation of documents. To the best of our knowledge, the task of identifying and ordering the versions of documents from a collection of documents has not been addressed in prior literature. We propose a three-stage approach for the task of identifying versions and ordering them correctly in this paper. We also create a novel dataset for this purpose from Wikipedia, which we are releasing to the research community (https://github.com/natwar-modani/versions ). We show that our proposed approach significantly outperforms state-of-the-art approach adapted for this task from the closest previously known task of Near Duplicate Detection, which justifies defining this problem as a novel challenge. © 2021, Springer Nature Switzerland AG.