8 accepted papers
Estimation of cognitive workload from EEG signals is a key challenge in advancing neuroergonomic systems and brain-computer interfaces (BCIs). A hybrid approach is presented, combining EEGNet and Graph Attention Networks (GATs) to effectively capture the intricate spatial and temporal dynamics withi…
Image segmentation is a crucial element of image processing that divides an image into distinct regions based on pixel intensity, facilitating detailed analysis and interpretation of various image components. Conventional segmentation techniques often struggle with challenges such as local minima en…
"Severe blurring of scene text images, resulting in the loss of critical strokes and textual information, has a profound impact on text readability and recognizability. Therefore, scene text image super-resolution, aiming to enhance text resolution and legibility in low-resolution images, is a cruci…
With the rapid development of brain-computer interfaces, the number of applications based on this technology is increasing rapidly. This work proposes a Stacked BLSTM-LSTM, EEG-Transformer, and their ensemble network to predict real-life motor activities of individuals using EEG (ElectroEncephalo-Gr…
Facial micro-expressions are sudden involuntary minute muscle movements which reveal true emotions that people try to conceal. Spotting a micro-expression and recognizing it is a major challenge owing to its short duration and intensity. Many works pursued traditional and deep learning based approac…
Handwritten Word Recognition and Spotting is a challenging field dealing with handwritten text possessing irregular and complex shapes. The design of deep neural network models makes it necessary to extend training datasets in order to introduce variations and increase the number of samples; word-re…
Video generation is an active field of research. With the rise in the amount of available data and economically available processing power in the form of GPUs, deep Learning has been a go-to solution for many real life problems and similarly it is often attempted to solve the problem of video genera…
Thumbnails are widely used all over the world as a preview for digital images. In this work we propose a deep neural framework to generate thumbnails of any size and aspect ratio, even for unseen values during training, with high accuracy and precision. We use Global Context Aggregation (GCA) and a…