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Found 16 papers, 0 papers with code
Curriculum for Crowd Counting -- Is it Worthy?
In this work, we investigate the impact of curriculum learning in crowd counting using the density estimation method.
Multimodal Crowd Counting with Pix2Pix GANs
Recently, some studies have reported improvement in the accuracy of crowd counting models using a combination of RGB and thermal images.
A Comprehensive Survey On Client Selections in Federated Learning
Federated Learning (FL) is a rapidly growing field in machine learning that allows data to be trained across multiple decentralized devices.
Crowd Counting in Harsh Weather using Image Denoising with Pix2Pix GANs
Visual crowd counting estimates the density of the crowd using deep learning models such as convolution neural networks (CNNs).
Visual Crowd Analysis: Open Research Problems
However, despite the magnitude of the issue at hand, the significant technological advancements, and the consistent interest of the research community, there are still numerous challenges that need to be overcome.
LCDnet: A Lightweight Crowd Density Estimation Model for Real-time Video Surveillance
These models have achieved good accuracy over benchmark datasets.
CLIP: Train Faster with Less Data
CLIP combines two data-centric approaches i. e., curriculum learning and dataset pruning to improve the model learning accuracy and convergence speed.
Crowd Density Estimation using Imperfect Labels
Density estimation is one of the most widely used methods for crowd counting in which a deep learning model learns from head-annotated crowd images to estimate crowd density in unseen images.
DroneNet: Crowd Density Estimation using Self-ONNs for Drones
Video surveillance using drones is both convenient and efficient due to the ease of deployment and unobstructed movement of drones in many scenarios.
Revisiting Crowd Counting: State-of-the-art, Trends, and Future Perspectives
In this paper, we present a systematic and comprehensive review of the most significant contributions in the area of crowd counting.
ML-based Handover Prediction and AP Selection in Cognitive Wi-Fi Networks
Two well-known problems related to device mobility are handover prediction and access point selection.
Federated Learning for UAV Swarms Under Class Imbalance and Power Consumption Constraints
The usage of unmanned aerial vehicles (UAVs) in civil and military applications continues to increase due to the numerous advantages that they provide over conventional approaches.
Fully Automated 2D and 3D Convolutional Neural Networks Pipeline for Video Segmentation and Myocardial Infarction Detection in Echocardiography
Our model is implemented as a pipeline consisting of a 2D CNN that performs data preprocessing by segmenting the LV chamber from the apical four-chamber (A4C) view, followed by a 3D CNN that performs a binary classification to detect if the segmented echocardiography shows signs of MI.
Early Detection of Myocardial Infarction in Low-Quality Echocardiography
Myocardial infarction (MI), or commonly known as heart attack, is a life-threatening health problem worldwide from which 32. 4 million people suffer each year.
Left Ventricular Wall Motion Estimation by Active Polynomials for Acute Myocardial Infarction Detection
It further enables medical experts to gain an enhanced visualization capability of echo images through color-coded segments along with their "maximum motion displacement" plots helping them to better assess wall motion and LV Ejection-Fraction (LVEF).
Colorectal cancer diagnosis from histology images: A comparative study
Moreover, we conduct quantitative comparative evaluations among the traditional methods, transfer learning-based methods and the proposed adaptive approach for the particular task of cancer detection and identification from scarce and low-resolution histology images.
