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Fredom: Fairness domain adaptation approach to semantic scene understanding
Abstract Although Domain Adaptation in Semantic Scene Segmentation has shown
impressive improvement in recent years, the fairness concerns in the domain adaptation …
impressive improvement in recent years, the fairness concerns in the domain adaptation …
Public health advocacy dataset: A dataset of tobacco usage videos from social media
The Public Health Advocacy Dataset (PHAD) is a comprehensive collection of 5,730 videos
related to tobacco products sourced from social media platforms like TikTok and YouTube …
related to tobacco products sourced from social media platforms like TikTok and YouTube …
Learning from Oversampling: A Systematic Exploitation of oversampling to address Data Scarcity issues in Deep Learning based Magnetic Resonance Image …
Data acquisitions in Magnetic Resonance Imaging (MRI) are inherently slow due to
sequential acquisition protocol. Image reconstruction from under-sampled data is posed as …
sequential acquisition protocol. Image reconstruction from under-sampled data is posed as …
Flaash: Flow-attention adaptive semantic hierarchical fusion for multi-modal tobacco content analysis
The proliferation of tobacco-related content on social media platforms poses significant
challenges for public health monitoring and intervention. This paper introduces a novel multi …
challenges for public health monitoring and intervention. This paper introduces a novel multi …
Semi-supervised learning for fish species recognition
Fish species recognition and detection are essential for fishery industries. Accurate and
robust species classification and detection play a vital role in monitoring fish activities and …
robust species classification and detection play a vital role in monitoring fish activities and …
Real-world image deblurring via unsupervised domain adaptation
Most deep learning models for image deblurring are trained on pairs of clean images and
their blurry counterparts, where the blurry inputs are artificially generated. However, it is …
their blurry counterparts, where the blurry inputs are artificially generated. However, it is …
VAEWGAN-NCO in image deblurring framework using variational autoencoders and Wasserstein generative adversarial network
This article proposes a novel “Deep Salient Image Deblurring (DSID) Framework” for kernel-
free image deblurring that combines saliency detection and variational autoencoders and …
free image deblurring that combines saliency detection and variational autoencoders and …
When System Model Meets Image Prior: An Unsupervised Deep Learning Architecture for Accelerated Magnetic Resonance Imaging
Abstract Magnetic Resonance Imaging (MRI) is typically a slow process because of its
sequential data acquisition. To speed up this process, MR acquisition is often accelerated by …
sequential data acquisition. To speed up this process, MR acquisition is often accelerated by …
LiGAR: LiDAR-Guided Hierarchical Transformer for Multi-Modal Group Activity Recognition
Group Activity Recognition (GAR) remains challenging in computer vision due to the
complex nature of multi-agent interactions. This paper introduces LiGAR, a LIDAR-Guided …
complex nature of multi-agent interactions. This paper introduces LiGAR, a LIDAR-Guided …
The Surprising Effectiveness of Deep Orthogonal Procrustes Alignment in Unsupervised Domain Adaptation
Unsupervised domain adaptation (UDA) aims to transfer and adapt knowledge from a
labeled source domain to an unlabeled target domain. Traditionally, geometry-based …
labeled source domain to an unlabeled target domain. Traditionally, geometry-based …