This research paper addresses the challenge of recognizing faces masked due to the post-pandemic scenario. The researchers created a new dataset by selecting images from the Labelled Faces in the…
Continue readingThe article introduces ATGNN (Audio Tagging Graph Neural Network), a deep learning model for audio tagging. It addresses the limitations of CNNs and Transformers by treating the spectrogram as a…
Continue readingThe paper presents a method to improve object detection in infrared (IR) images by leveraging cues from the RGB modality. The lack of sufficient labeled training data in the IR…
Continue readingThe research paper by Zhong, Bhattad, Wang, and Forsyth from the University of Illinois Urbana-Champaign investigates the lack of equivariance in state-of-the-art depth and normal predictors. Despite strong performances, these…
Continue readingThe paper presents SA2-Net, a scale-aware attention network for microscopic image segmentation. Microscopic image segmentation, assigning semantic labels to each pixel in a microscopic image, is a challenging task. Existing…
Continue readingThe paper presents a new approach to Arabic Sentiment Analysis (ASA) using Bi-Directional Long Short-Term Memory (BiLSTM) and Convolutional Neural Networks (CNN)-BiLSTM models. The proposed framework introduces a noise layer…
Continue readingThis article from the University of Tehran proposes an intelligent method for detecting novel faults in centrifugal pumps using deep Convolutional Neural Networks (CNNs) and unsupervised methods. The study addresses…
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