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Found 15 papers, 4 papers with code
Amirkabir campus dataset: Real-world challenges and scenarios of Visual Inertial Odometry (VIO) for visually impaired people
Visual Inertial Odometry (VIO) algorithms estimate the accurate camera trajectory by using camera and Inertial Measurement Unit (IMU) sensors.
Identity-preserving Editing of Multiple Facial Attributes by Learning Global Edit Directions and Local Adjustments
Semantic facial attribute editing using pre-trained Generative Adversarial Networks (GANs) has attracted a great deal of attention and effort from researchers in recent years.
Enhancing Landmark Detection in Cluttered Real-World Scenarios with Vision Transformers
Visual place recognition tasks often encounter significant challenges in landmark detection due to the presence of irrelevant objects such as humans, cars, and trees, despite the remarkable progress achieved by previous models, especially in the context of transformers.
A comprehensive survey on semantic facial attribute editing using generative adversarial networks
Generating random photo-realistic images has experienced tremendous growth during the past few years due to the advances of the deep convolutional neural networks and generative models.
SRVIO: Super Robust Visual Inertial Odometry for dynamic environments and challenging Loop-closure conditions
In this paper, we have combined geometric and DNN-based methods to have the generality and speed of geometric SLAM frameworks and overcome most of these challenging conditions with the help of DNNs and deliver the most robust framework so far.
GMFIM: A Generative Mask-guided Facial Image Manipulation Model for Privacy Preservation
Generative adversarial networks (GANs) are shown to be very powerful in generating face images in high diversity and also in editing face images.
Face sketch to photo translation using generative adversarial networks
Translating face sketches to photo-realistic faces is an interesting and essential task in many applications like law enforcement and the digital entertainment industry.
Efficient Attention Branch Network with Combined Loss Function for Automatic Speaker Verification Spoof Detection
Many endeavors have sought to develop countermeasure techniques as enhancements on Automatic Speaker Verification (ASV) systems, in order to make them more robust against spoof attacks.
Ranked #1 on
Spoof Detection
on ASVspoof 2019 - LA
Adversarial Attacks against Face Recognition: A Comprehensive Study
Face recognition (FR) systems have demonstrated outstanding verification performance, suggesting suitability for real-world applications ranging from photo tagging in social media to automated border control (ABC).
Diverse and Styled Image Captioning Using SVD-Based Mixture of Recurrent Experts
We show that the proposed captioning model can generate a diverse and stylized image captions without the necessity of extra-labeling.
Convolutional Spiking Neural Networks for Spatio-Temporal Feature Extraction
Spiking neural networks (SNNs) can be used in low-power and embedded systems (such as emerging neuromorphic chips) due to their event-based nature.
Ranked #1 on
Image Classification
on N-MNIST
Convolutional Relational Machine for Group Activity Recognition
We present an end-to-end deep Convolutional Neural Network called Convolutional Relational Machine (CRM) for recognizing group activities that utilizes the information in spatial relations between individual persons in image or video.
A Multi-Stream Convolutional Neural Network Framework for Group Activity Recognition
In this work, we present a framework based on multi-stream convolutional neural networks (CNNs) for group activity recognition.
Zoom-RNN: A Novel Method for Person Recognition Using Recurrent Neural Networks
The overwhelming popularity of social media has resulted in bulk amounts of personal photos being uploaded to the internet every day.
Distinction between features extracted using deep belief networks
Data representation is an important pre-processing step in many machine learning algorithms.
