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.
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.
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.
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.
Lack of global data inventories obstructs scientific modeling of and response to landslide hazards which are oftentimes deadly and costly.
This is the first dataset of its kind: social media images, disaster response, and multi-task learning research.
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.
Images shared on social media help crisis managers gain situational awareness and assess incurred damages, among other response tasks.
Social networks are widely used for information consumption and dissemination, especially during time-critical events such as natural disasters.
In this study, we propose new datasets for disaster type detection, and informativeness classification, and damage severity assessment.
While most studies on social media are limited to text, images offer more information for understanding disaster and incident scenes.
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.
Time-critical analysis of social media streams is important for humanitarian organizations for planing rapid response during disasters.
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.
Multimedia content in social media platforms provides significant information during disaster events.
Ranked #1 on Disaster Response on CrisisMMD
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).
In this paper, we introduce Recipe1M+, a new large-scale, structured corpus of over one million cooking recipes and 13 million food images.
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
In this paper, we introduce Recipe1M, a new large-scale, structured corpus of over 1m cooking recipes and 800k food images.
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.
A person's weight status can have profound implications on their life, ranging from mental health, to longevity, to financial income.
Studying how food is perceived in relation to what it actually is typically involves a laboratory setup.
In this paper we explore the use of deep learning to build sleep quality prediction models based on actigraphy data.
Human Activity Recognition (HAR) is a powerful tool for understanding human behaviour.
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.
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.
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.