Search Results for author:
Found 16 papers, 5 papers with code
A Survey on Multi-Objective Neural Architecture Search
Recently, the expert-crafted neural architectures is increasing overtaken by the utilization of neural architecture search (NAS) and automatic generation (and tuning) of network structures which has a close relation to the Hyperparameter Optimization and Auto Machine Learning (AutoML).
Image/Video Deep Anomaly Detection: A Survey
AD strongly correlates with the important computer vision and image processing tasks such as image/video anomaly, irregularity and sudden event detection.
Multi-level Context Gating of Embedded Collective Knowledge for Medical Image Segmentation
These blocks adaptively recalibrate the channel-wise feature responses by utilizing a self-gating mechanism of the global information embedding of the feature maps.
Ranked #8 on
Lesion Segmentation
on ISIC 2018
Deep-HR: Fast Heart Rate Estimation from Face Video Under Realistic Conditions
In this paper, we propose a simple yet efficient approach to benefit the advantages of the Deep Neural Network (DNN) by simplifying HR estimation from a complex task to learning from very correlated representation to HR.
Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions
To strengthen feature propagation and encourage feature reuse, we use densely connected convolutions in the last convolutional layer of the encoding path.
Ranked #1 on
Lung Nodule Segmentation
on Lung Nodule
Online Signature Verification using Deep Representation: A new Descriptor
To deal with these difficulties and modeling the signatures efficiently, we propose a method that a one-class classifier per each user is built on discriminative features.
Semantic Video Segmentation: A Review on Recent Approaches
This paper gives an overview on semantic segmentation consists of an explanation of this field, it's status and relation with other vision fundamental tasks, different datasets and common evaluation parameters that have been used by researchers.
AVID: Adversarial Visual Irregularity Detection
Real-time detection of irregularities in visual data is very invaluable and useful in many prospective applications including surveillance, patient monitoring systems, etc.
Adversarially Learned One-Class Classifier for Novelty Detection
Our architecture is composed of two deep networks, each of which trained by competing with each other while collaborating to understand the underlying concept in the target class, and then classify the testing samples.
Deep-Anomaly: Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenes
The detection of abnormal behaviours in crowded scenes has to deal with many challenges.
STFCN: Spatio-Temporal FCN for Semantic Video Segmentation
Current work on convolutional neural networks(CNNs) has shown that CNNs provide advanced spatial features supporting a very good performance of solutions for both image and video analysis, especially for the semantic segmentation task.
Real-Time Anomalous Behavior Detection and Localization in Crowded Scenes
We propose a fusion strategy to combine the two descriptors as the output of our system.
Real-Time Anomaly Detection and Localization in Crowded Scenes
In this paper, we propose a method for real-time anomaly detection and localization in crowded scenes.
A Novel Approach For Finger Vein Verification Based on Self-Taught Learning
In this paper, we propose a method for user Finger Vein Authentication (FVA) as a biometric system.
IDSA: Intelligent Distributed Sensor Activation Algorithm For Target Tracking With Wireless Sensor Network
One important application of the Wireless Sensor Network(WSN) is target tracking, the aim of this application is converging to an event or object in an area.
Feature Representation for Online Signature Verification
Biometrics systems have been used in a wide range of applications and have improved people authentication.
