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Dwip Dalal, Gautam Vashishtha, Prajwal Singh, Shanmuganathan Raman ICIP, 2023 project page/ arXiv/ video We formulate the problem as an image-to-image (I2I) translation task and propose a conditional Denoising Diffusion Probabilistic Model (DDPM) based framework using classifier-free guidance. We incorporate a deep CNN-based autoencoder in our proposed framework to enhance the quality of the latent representation of the input LDR image used for conditioning. |
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Prajwal Singh, Pankaj Pandey, Krishna Miyapuram, Shanmuganathan Raman ICASSP, 2023 project page/ arXiv/ video We use a contrastive learning method in the proposed framework to extract features from EEG signals and synthesize the images from extracted features using conditional GAN. We modify the loss function to train the GAN, which enables it to synthesize 128x128 images using a small number of images. |
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Aalok Gangopadhyay, Prajwal Singh, Shanmuganathan Raman NCC, 2022 project page/ arXiv/ video To minimize this intervention, we propose APEX-Net, a deep learning based framework with novel loss functions for solving the plot extraction problem. We introduce APEX-1M, a new large scale dataset which contains both the plot images and the raw data. |
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Kaustubh Sadekar, Ashish Tiwari, Prajwal Singh, Shanmuganathan Raman ICPR, 2022 project page/ arXiv/ video We propose a large scale dataset of 1 Million challenging images for the task of hand written document image binarisation (HDIB) with accurate segmentation groundtruth. |
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Indradeep Mastan, Shanmuganathan Raman, Prajwal Singh WACV VAQ Workshop, 2022 project page/ arXiv/ video We perform image enhancement using a deep internal learning framework. Our Deep Internal Learning for Image Enhancement framework (DILIE) enhances content features and style features and preserves semantics in the enhanced image. |
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Prajwal Singh, Kaustubh Sadekar, Shanmuganathan Raman, arXiv, 2021 project page/ arXiv/ video This work proposes a tree-structured autoencoder framework to generate robust embeddings of point clouds by utilizing hierarchical information using graph convolution. We perform multiple experiments to assess the quality of embeddings generated by the proposed encoder architecture and visualize the t-SNE map to highlight its ability to distinguish between different object classes. |
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