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Found 14 papers, 4 papers with code
A Unified Simulation Framework for Visual and Behavioral Fidelity in Crowd Analysis
Simulation is a powerful tool to easily generate annotated data, and a highly desirable feature, especially in those domains where learning models need large training datasets.
Capsules as viewpoint learners for human pose estimation
We further test our network on multiple datasets, both in the RGB and depth domain, from seen and unseen viewpoints and in the viewpoint transfer task.
Interpretable part-whole hierarchies and conceptual-semantic relationships in neural networks
When it comes to critical applications as autonomous driving, security and safety, medicine and health, the lack of interpretability of the network behavior tends to induce skepticism and limited trustworthiness, despite the accurate performance of such systems in the given task.
Visual Sentiment Analysis: A Natural DisasterUse-case Task at MediaEval 2021
The Visual Sentiment Analysis task is being offered for the first time at MediaEval.
PanopTOP: a framework for generating viewpoint-invariant human pose estimation datasets
Human pose estimation (HPE) from RGB and depth images has recently experienced a push for viewpoint-invariant and scale-invariant pose retrieval methods.
DECA: Deep viewpoint-Equivariant human pose estimation using Capsule Autoencoders
Human Pose Estimation (HPE) aims at retrieving the 3D position of human joints from images or videos.
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Out-of-Distribution Detection Using Union of 1-Dimensional Subspaces
In this paper, we argue that OOD samples can be detected more easily if the training data is embedded into a low-dimensional space, such that the embedded training samples lie on a union of 1-dimensional subspaces.
Flood Detection via Twitter Streams using Textual and Visual Features
The paper presents our proposed solutions for the MediaEval 2020 Flood-Related Multimedia Task, which aims to analyze and detect flooding events in multimedia content shared over Twitter.
Visual Sentiment Analysis from Disaster Images in Social Media
While sentiment analysis of text streams has been widely explored in literature, sentiment analysis from images and videos is relatively new.
Deriving Emotions and Sentiments from Visual Content: A Disaster Analysis Use Case
Sentiment analysis aims to extract and express a person's perception, opinions and emotions towards an entity, object, product and a service, enabling businesses to obtain feedback from the consumers.
Sentiment Analysis from Images of Natural Disasters
Social media have been widely exploited to detect and gather relevant information about opinions and events.
Multi-Modal Machine Learning for Flood Detection in News, Social Media and Satellite Sequences
In this paper we present our methods for the MediaEval 2019 Mul-timedia Satellite Task, which is aiming to extract complementaryinformation associated with adverse events from Social Media andsatellites.
Active Learning for Event Detection in Support of Disaster Analysis Applications
Disaster analysis in social media content is one of the interesting research domains having abundance of data.
The S-Hock Dataset: Analyzing Crowds at the Stadium
In the dataset, a massive annotation has been carried out, focusing on the spectators at different levels of details: at a higher level, people have been labeled depending on the team they are supporting and the fact that they know the people close to them; going to the lower levels, standard pose information has been considered (regarding the head, the body) but also fine grained actions such as hands on hips, clapping hands etc.
