A Multimedia Analytics Model for the Foundation Model Era

• URL: http://arxiv.org/abs/2504.06138v2
• Date: Thu, 10 Apr 2025 10:52:41 GMT
• Title: A Multimedia Analytics Model for the Foundation Model Era
• Authors: Marcel Worring, Jan Zahálka, Stef van den Elzen, Maximilian T. Fischer, Daniel A. Keim,
• Abstract summary: We propose a comprehensive multimedia analytics model specifically designed for the foundation model era.<n>Our model emphasizes integrated human-AI teaming based on visual analytics agents from both technical and conceptual perspectives.<n>The model addresses practical challenges in sensitive domains such as intelligence analysis, investigative journalism, and other fields handling complex, high-stakes data.
• Score: 16.984873281836762
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid advances in Foundation Models and agentic Artificial Intelligence are transforming multimedia analytics by enabling richer, more sophisticated interactions between humans and analytical systems. Existing conceptual models for visual and multimedia analytics, however, do not adequately capture the complexity introduced by these powerful AI paradigms. To bridge this gap, we propose a comprehensive multimedia analytics model specifically designed for the foundation model era. Building upon established frameworks from visual analytics, multimedia analytics, knowledge generation, analytic task definition, mixed-initiative guidance, and human-in-the-loop reinforcement learning, our model emphasizes integrated human-AI teaming based on visual analytics agents from both technical and conceptual perspectives. Central to the model is a seamless, yet explicitly separable, interaction channel between expert users and semi-autonomous analytical processes, ensuring continuous alignment between user intent and AI behavior. The model addresses practical challenges in sensitive domains such as intelligence analysis, investigative journalism, and other fields handling complex, high-stakes data. We illustrate through detailed case studies how our model facilitates deeper understanding and targeted improvement of multimedia analytics solutions. By explicitly capturing how expert users can optimally interact with and guide AI-powered multimedia analytics systems, our conceptual framework sets a clear direction for system design, comparison, and future research.

Related papers

• A Survey of Model Architectures in Information Retrieval [64.75808744228067]
  We focus on two key aspects: backbone models for feature extraction and end-to-end system architectures for relevance estimation.<n>We trace the development from traditional term-based methods to modern neural approaches, particularly highlighting the impact of transformer-based models and subsequent large language models (LLMs)<n>We conclude by discussing emerging challenges and future directions, including architectural optimizations for performance and scalability, handling of multimodal, multilingual data, and adaptation to novel application domains beyond traditional search paradigms.
  arXiv  Detail & Related papers  (2025-02-20T18:42:58Z)
• A Survey of World Models for Autonomous Driving [63.33363128964687]
  Recent breakthroughs in autonomous driving have been propelled by advances in robust world modeling.<n>This paper systematically reviews recent advances in world models for autonomous driving.
  arXiv  Detail & Related papers  (2025-01-20T04:00:02Z)
• Found in Translation: semantic approaches for enhancing AI interpretability in face verification [0.4222205362654437]
  This study extends previous work by integrating semantic concepts into XAI frameworks to bridge the comprehension gap between model outputs and human understanding. We propose a novel approach combining global and local explanations, using semantic features defined by user-selected facial landmarks. Results indicate that our semantic-based approach, particularly the most detailed set, offers a more nuanced understanding of model decisions than traditional methods.
  arXiv  Detail & Related papers  (2025-01-06T08:34:53Z)
• 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)
• Data Analysis in the Era of Generative AI [56.44807642944589]
  This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
  arXiv  Detail & Related papers  (2024-09-27T06:31:03Z)
• ParetoTracker: Understanding Population Dynamics in Multi-objective Evolutionary Algorithms through Visual Analytics [16.65441551504126]
  This paper introduces a visual analytics framework designed to support the comprehension and inspection of population dynamics. The framework caters to user engagement and exploration ranging from examining overall trends in performance metrics to conducting fine-grained inspections of evolutionary operations. The effectiveness of the framework is demonstrated through case studies and expert interviews focused on widely adopted benchmark optimization problems.
  arXiv  Detail & Related papers  (2024-08-08T15:46:11Z)
• Coding for Intelligence from the Perspective of Category [66.14012258680992]
  Coding targets compressing and reconstructing data, and intelligence. Recent trends demonstrate the potential homogeneity of these two fields. We propose a novel problem of Coding for Intelligence from the category theory view.
  arXiv  Detail & Related papers  (2024-07-01T07:05:44Z)
• Foundation Models for Decision Making: Problems, Methods, and Opportunities [124.79381732197649]
  Foundation models pretrained on diverse data at scale have demonstrated extraordinary capabilities in a wide range of vision and language tasks. New paradigms are emerging for training foundation models to interact with other agents and perform long-term reasoning. Research at the intersection of foundation models and decision making holds tremendous promise for creating powerful new systems.
  arXiv  Detail & Related papers  (2023-03-07T18:44:07Z)
• DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanations [119.1953397679783]
  We focus on advancing the state-of-the-art in interpreting multimodal models. Our proposed approach, DIME, enables accurate and fine-grained analysis of multimodal models.
  arXiv  Detail & Related papers  (2022-03-03T20:52:47Z)

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.