The AffectToolbox: Affect Analysis for Everyone

• URL: http://arxiv.org/abs/2402.15195v1
• Date: Fri, 23 Feb 2024 08:55:47 GMT
• Title: The AffectToolbox: Affect Analysis for Everyone
• Authors: Silvan Mertes, Dominik Schiller, Michael Dietz, Elisabeth Andr\'e, Florian Lingenfelser
• Abstract summary: AffectToolbox is a novel software system that aims to support researchers in developing affect-sensitive studies and prototypes. The proposed system addresses the challenges posed by existing frameworks, which often require profound programming knowledge and cater primarily to power-users or skilled developers. The architecture encompasses a variety of models for emotion recognition on multiple affective channels and modalities, as well as an elaborate fusion system to merge multi-modal assessments into a unified result.
• Score: 10.526991118781913
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the field of affective computing, where research continually advances at a rapid pace, the demand for user-friendly tools has become increasingly apparent. In this paper, we present the AffectToolbox, a novel software system that aims to support researchers in developing affect-sensitive studies and prototypes. The proposed system addresses the challenges posed by existing frameworks, which often require profound programming knowledge and cater primarily to power-users or skilled developers. Aiming to facilitate ease of use, the AffectToolbox requires no programming knowledge and offers its functionality to reliably analyze the affective state of users through an accessible graphical user interface. The architecture encompasses a variety of models for emotion recognition on multiple affective channels and modalities, as well as an elaborate fusion system to merge multi-modal assessments into a unified result. The entire system is open-sourced and will be publicly available to ensure easy integration into more complex applications through a well-structured, Python-based code base - therefore marking a substantial contribution toward advancing affective computing research and fostering a more collaborative and inclusive environment within this interdisciplinary field.

Related papers

• Collaborative AI in Sentiment Analysis: System Architecture, Data Prediction and Deployment Strategies [3.3374611485861116]
  Large language model (LLM) based artificial intelligence technologies have been a game-changer, particularly in sentiment analysis. However, integrating diverse AI models for processing complex multimodal data and the associated high costs of feature extraction presents significant challenges. This study introduces a collaborative AI framework designed to efficiently distribute and resolve tasks across various AI systems.
  arXiv  Detail & Related papers  (2024-10-17T06:14:34Z)
• The Design Space of in-IDE Human-AI Experience [6.05260196829912]
  Key findings stress the need for AI systems that are more personalized, proactive, and reliable. Our findings show that while Adopters appreciate advanced features and non-interruptive integration, Churners emphasize the need for improved reliability and privacy. Non-Users, in contrast, focus on skill development and ethical concerns as barriers to adoption.
  arXiv  Detail & Related papers  (2024-10-11T10:02:52Z)
• SIGMA: An Open-Source Interactive System for Mixed-Reality Task Assistance Research [5.27467559535251]
  We introduce an open-source system called SIGMA as a platform for conducting research on task-assistive agents in mixed-reality scenarios. We present the system's core capabilities, discuss its overall design and implementation, and outline directions for future research enabled by the system.
  arXiv  Detail & Related papers  (2024-05-16T21:21:09Z)
• LVLM-Interpret: An Interpretability Tool for Large Vision-Language Models [50.259006481656094]
  We present a novel interactive application aimed towards understanding the internal mechanisms of large vision-language models. Our interface is designed to enhance the interpretability of the image patches, which are instrumental in generating an answer. We present a case study of how our application can aid in understanding failure mechanisms in a popular large multi-modal model: LLaVA.
  arXiv  Detail & Related papers  (2024-04-03T23:57:34Z)
• Interactive Multi-Objective Evolutionary Optimization of Software Architectures [0.0]
  Putting the human in the loop brings new challenges to the search-based software engineering field. This paper explores how the interactive evolutionary computation can serve as a basis for integrating the human's judgment into the search process.
  arXiv  Detail & Related papers  (2024-01-08T19:15:40Z)
• Interactive Natural Language Processing [67.87925315773924]
  Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP. This paper offers a comprehensive survey of iNLP, starting by proposing a unified definition and framework of the concept.
  arXiv  Detail & Related papers  (2023-05-22T17:18:29Z)
• OpenAGI: When LLM Meets Domain Experts [51.86179657467822]
  Human Intelligence (HI) excels at combining basic skills to solve complex tasks. This capability is vital for Artificial Intelligence (AI) and should be embedded in comprehensive AI Agents. We introduce OpenAGI, an open-source platform designed for solving multi-step, real-world tasks.
  arXiv  Detail & Related papers  (2023-04-10T03:55:35Z)
• Flashlight: Enabling Innovation in Tools for Machine Learning [50.63188263773778]
  We introduce Flashlight, an open-source library built to spur innovation in machine learning tools and systems. We see Flashlight as a tool enabling research that can benefit widely used libraries downstream and bring machine learning and systems researchers closer together.
  arXiv  Detail & Related papers  (2022-01-29T01:03:29Z)
• MultiBench: Multiscale Benchmarks for Multimodal Representation Learning [87.23266008930045]
  MultiBench is a systematic and unified benchmark spanning 15 datasets, 10 modalities, 20 prediction tasks, and 6 research areas. It provides an automated end-to-end machine learning pipeline that simplifies and standardizes data loading, experimental setup, and model evaluation. It introduces impactful challenges for future research, including robustness to large-scale multimodal datasets and robustness to realistic imperfections.
  arXiv  Detail & Related papers  (2021-07-15T17:54:36Z)
• Modular approach to data preprocessing in ALOHA and application to a smart industry use case [0.0]
  The paper addresses a modular approach, integrated into the ALOHA tool flow, to support the data preprocessing and transformation pipeline. To demonstrate the effectiveness of the approach, we present some experimental results related to a keyword spotting use case.
  arXiv  Detail & Related papers  (2021-02-02T06:48:51Z)
• Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper [50.25428141435537]
  Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between machine learning, big data, streaming analytics, and the management of IT operations. Main aim of the AIOPS workshop is to bring together researchers from both academia and industry to present their experiences, results, and work in progress in this field.
  arXiv  Detail & Related papers  (2021-01-15T10:43:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.