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Found 6 papers, 3 papers with code
Learning Robust Deep Visual Representations from EEG Brain Recordings
Reconstruction of visual images from brain Electroencephalography (EEG) signals has garnered a lot of interest due to its applications in brain-computer interfacing.
Single Image LDR to HDR Conversion using Conditional Diffusion
Digital imaging aims to replicate realistic scenes, but Low Dynamic Range (LDR) cameras cannot represent the wide dynamic range of real scenes, resulting in under-/overexposed images.
A Graph Neural Network Approach for Temporal Mesh Blending and Correspondence
We have proposed a self-supervised deep learning framework for solving the mesh blending problem in scenarios where the meshes are not in correspondence.
TreeGCN-ED: Encoding Point Cloud using a Tree-Structured Graph Network
Point cloud is one of the widely used techniques for representing and storing 3D geometric data.
LS-HDIB: A Large Scale Handwritten Document Image Binarization Dataset
This work proposes LS-HDIB - a large-scale handwritten document image binarization dataset containing over a million document images that span numerous real-world scenarios.
APEX-Net: Automatic Plot Extractor Network
To minimize this intervention, we propose APEX-Net, a deep learning based framework with novel loss functions for solving the plot extraction problem.
