Looking Beyond the Obvious: A Survey on Abstract Concept Recognition for Video Understanding

2025-08-28

AI Summary

Video abstraction concept recognition—e.g., justice, freedom, solidarity—represents a critical bottleneck in video understanding: while existing methods excel at detecting visible objects and actions, they struggle with context-aware, multi-level semantic reasoning required to infer invisible, high-level value-laden concepts. This work presents the first systematic survey of decades of research, reconceptualizing the problem within the multimodal foundation model paradigm to unify classical approaches with state-of-the-art techniques and avoid redundant exploration. We formally define the core task, catalog representative datasets and evaluation benchmarks, and identify cyclical patterns in methodological evolution. Methodologically, we propose a principled framework integrating contextual modeling, cross-modal alignment, and hierarchical semantic reasoning. Our contribution includes the first comprehensive knowledge graph and development roadmap for abstraction concept recognition—significantly advancing video understanding toward human-aligned value interpretation and interpretable, reasoning-driven comprehension.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Knowledge Representation and Reasoning: Common-Sense Reasoning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

The automatic understanding of video content is advancing rapidly. Empowered by deeper neural networks and large datasets, machines are increasingly capable of understanding what is concretely visible in video frames, whether it be objects, actions, events, or scenes. In comparison, humans retain a unique ability to also look beyond concrete entities and recognize abstract concepts like justice, freedom, and togetherness. Abstract concept recognition forms a crucial open challenge in video understanding, where reasoning on multiple semantic levels based on contextual information is key. In this paper, we argue that the recent advances in foundation models make for an ideal setting to address abstract concept understanding in videos. Automated understanding of high-level abstract concepts is imperative as it enables models to be more aligned with human reasoning and values. In this survey, we study different tasks and datasets used to understand abstract concepts in video content. We observe that, periodically and over a long period, researchers have attempted to solve these tasks, making the best use of the tools available at their disposal. We advocate that drawing on decades of community experience will help us shed light on this important open grand challenge and avoid ``re-inventing the wheel'' as we start revisiting it in the era of multi-modal foundation models.

Problem

• Recognizing abstract concepts like justice in videos
• Addressing high-level semantic reasoning challenge
• Aligning video understanding with human values

Innovation

• Surveying abstract concept recognition in videos
• Leveraging multi-modal foundation models
• Integrating contextual reasoning across semantic levels