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Found 33 papers, 10 papers with code
Evaluating Robustness of LLMs on Crisis-Related Microblogs across Events, Information Types, and Linguistic Features
The widespread use of microblogging platforms like X (formerly Twitter) during disasters provides real-time information to governments and response authorities.
Monitoring Critical Infrastructure Facilities During Disasters Using Large Language Models
Critical Infrastructure Facilities (CIFs), such as healthcare and transportation facilities, are vital for the functioning of a community, especially during large-scale emergencies.
Fine-grained Population Mapping from Coarse Census Counts and Open Geodata
Fine-grained population maps are needed in several domains, like urban planning, environmental monitoring, public health, and humanitarian operations.
A Survey on Computer Vision based Human Analysis in the COVID-19 Era
The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals.
Bias-Aware Face Mask Detection Dataset
In this work, we present a novel face mask detection dataset that contains images posted on Twitter during the pandemic from around the world.
Learning Program Representations for Food Images and Cooking Recipes
To validate our idea, we crowdsource programs for cooking recipes and show that: (a) projecting the image-recipe embeddings into programs leads to better cross-modal retrieval results; (b) generating programs from images leads to better recognition results compared to predicting raw cooking instructions; and (c) we can generate food images by manipulating programs via optimizing the latent code of a GAN.
A Real-time System for Detecting Landslide Reports on Social Media using Artificial Intelligence
This paper presents an online system that leverages social media data in real time to identify landslide-related information automatically using state-of-the-art artificial intelligence techniques.
Incidents1M: a large-scale dataset of images with natural disasters, damage, and incidents
In this work, we present the Incidents1M Dataset, a large-scale multi-label dataset which contains 977, 088 images, with 43 incident and 49 place categories.
Fight Detection from Still Images in the Wild
We also propose a new dataset, named Social Media Fight Images (SMFI), comprising real-world images of fight actions.
TBCOV: Two Billion Multilingual COVID-19 Tweets with Sentiment, Entity, Geo, and Gender Labels
The widespread usage of social networks during mass convergence events, such as health emergencies and disease outbreaks, provides instant access to citizen-generated data that carry rich information about public opinions, sentiments, urgent needs, and situational reports.
Landslide Detection in Real-Time Social Media Image Streams
Lack of global data inventories obstructs scientific modeling of and response to landslide hazards which are oftentimes deadly and costly.
MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification
This is the first dataset of its kind: social media images, disaster response, and multi-task learning research.
Mapping Vulnerable Populations with AI
Building functions shall be retrieved by parsing social media data like for instance tweets, as well as ground-based imagery, to automatically identify different buildings functions and retrieve further information such as the number of building stories.
Robust Training of Social Media Image Classification Models for Rapid Disaster Response
Images shared on social media help crisis managers gain situational awareness and assess incurred damages, among other response tasks.
HumAID: Human-Annotated Disaster Incidents Data from Twitter with Deep Learning Benchmarks
Social networks are widely used for information consumption and dissemination, especially during time-critical events such as natural disasters.
Deep Learning Benchmarks and Datasets for Social Media Image Classification for Disaster Response
In this study, we propose new datasets for disaster type detection, and informativeness classification, and damage severity assessment.
Detecting natural disasters, damage, and incidents in the wild
While most studies on social media are limited to text, images offer more information for understanding disaster and incident scenes.
GeoCoV19: A Dataset of Hundreds of Millions of Multilingual COVID-19 Tweets with Location Information
The past several years have witnessed a huge surge in the use of social media platforms during mass convergence events such as health emergencies, natural or human-induced disasters.
CrisisBench: Benchmarking Crisis-related Social Media Datasets for Humanitarian Information Processing
Time-critical analysis of social media streams is important for humanitarian organizations for planing rapid response during disasters.
Analysis of Social Media Data using Multimodal Deep Learning for Disaster Response
Multimedia content in social media platforms provides significant information during disaster events.
Ranked #1 on
Disaster Response
on CrisisMMD
Rapid Damage Assessment Using Social Media Images by Combining Human and Machine Intelligence
Rapid damage assessment is one of the core tasks that response organizations perform at the onset of a disaster to understand the scale of damage to infrastructures such as roads, bridges, and buildings.
How to make a pizza: Learning a compositional layer-based GAN model
From a visual perspective, every instruction step can be seen as a way to change the visual appearance of the dish by adding extra objects (e. g., adding an ingredient) or changing the appearance of the existing ones (e. g., cooking the dish).
Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images
In this paper, we introduce Recipe1M+, a new large-scale, structured corpus of over one million cooking recipes and 13 million food images.
Ranked #2 on
Cross-Modal Retrieval
on Recipe1M+
CrisisMMD: Multimodal Twitter Datasets from Natural Disasters
Despite extensive research that mainly focuses on textual content to extract useful information, limited work has focused on the use of imagery content or the combination of both content types.
Social and Information Networks Computers and Society
Learning Cross-Modal Embeddings for Cooking Recipes and Food Images
In this paper, we introduce Recipe1M, a new large-scale, structured corpus of over 1m cooking recipes and 800k food images.
Automatic Image Filtering on Social Networks Using Deep Learning and Perceptual Hashing During Crises
The extensive use of social media platforms, especially during disasters, creates unique opportunities for humanitarian organizations to gain situational awareness and launch relief operations accordingly.
Face-to-BMI: Using Computer Vision to Infer Body Mass Index on Social Media
A person's weight status can have profound implications on their life, ranging from mental health, to longevity, to financial income.
Is Saki #delicious? The Food Perception Gap on Instagram and Its Relation to Health
Studying how food is perceived in relation to what it actually is typically involves a laboratory setup.
Impact of Physical Activity on Sleep:A Deep Learning Based Exploration
In this paper we explore the use of deep learning to build sleep quality prediction models based on actigraphy data.
Robust Automated Human Activity Recognition and its Application to Sleep Research
Human Activity Recognition (HAR) is a powerful tool for understanding human behaviour.
Remote Health Coaching System and Human Motion Data Analysis for Physical Therapy with Microsoft Kinect
In order to generate the kinematic parameter from the noisy data captured by Kinect, we propose a kinematic filtering algorithm based on Unscented Kalman Filter and the kinematic model of human skeleton.
Evaluation of Pose Tracking Accuracy in the First and Second Generations of Microsoft Kinect
Microsoft Kinect camera and its skeletal tracking capabilities have been embraced by many researchers and commercial developers in various applications of real-time human movement analysis.
Unsupervised Temporal Segmentation of Repetitive Human Actions Based on Kinematic Modeling and Frequency Analysis
In this paper, we propose a method for temporal segmentation of human repetitive actions based on frequency analysis of kinematic parameters, zero-velocity crossing detection, and adaptive k-means clustering.
