Compositional Scene Representation Learning via Reconstruction: A Survey (2202.07135v4)

Abstract: Visual scenes are composed of visual concepts and have the property of combinatorial explosion. An important reason for humans to efficiently learn from diverse visual scenes is the ability of compositional perception, and it is desirable for artificial intelligence to have similar abilities. Compositional scene representation learning is a task that enables such abilities. In recent years, various methods have been proposed to apply deep neural networks, which have been proven to be advantageous in representation learning, to learn compositional scene representations via reconstruction, advancing this research direction into the deep learning era. Learning via reconstruction is advantageous because it may utilize massive unlabeled data and avoid costly and laborious data annotation. In this survey, we first outline the current progress on reconstruction-based compositional scene representation learning with deep neural networks, including development history and categorizations of existing methods from the perspectives of the modeling of visual scenes and the inference of scene representations; then provide benchmarks, including an open source toolbox to reproduce the benchmark experiments, of representative methods that consider the most extensively studied problem setting and form the foundation for other methods; and finally discuss the limitations of existing methods and future directions of this research topic.

• The paper demonstrates that reconstruction-based methods enable effective compositional scene representation learning from unlabeled data.
• The paper categorizes diverse methodologies and benchmarks key approaches while providing an open source toolbox for reproducibility.
• The paper highlights future directions, stressing model robustness and unsupervised paradigms for handling complex, dynamic scenes.

The paper, titled "Compositional Scene Representation Learning via Reconstruction: A Survey," comprehensively reviews advances in the field of compositional scene representation learning, specifically through reconstruction methods using deep neural networks. Understanding and representing visual scenes compositionally is crucial because scenes consist of various visual concepts that combine in complex ways, a challenge often referred to as the combinatorial explosion. This ability is a key aspect of human perception and is highly desirable for artificial intelligence systems aiming to interpret and learn from diverse visual data effectively.

Key Points of the Survey

1. Compositional Perception and AI:
   • The paper underscores the importance of enabling AI systems with compositional perception similar to humans, allowing these systems to generalize from a wide array of visual scenes without exhaustive data annotation.
2. Reconstruction-Based Methods:
   • A primary focus of the survey is on reconstruction-based methods where models learn scene representations by reconstructing the input data. This approach leverages large amounts of unlabeled data, sidestepping the need for costly and labor-intensive data labeling. This is particularly advantageous in deep learning, where annotated datasets can be a limiting factor.
3. Development History and Methodologies:
   • The survey details the progression of these methods, categorizing various approaches based on how they model visual scenes and how they infer scene representations. The categorization provides a structured overview of the landscape, helping to identify commonalities and distinctions among different approaches.
4. Benchmarks and Open Source Tools:
   • The authors present benchmarks for evaluating representative methods in the field. These benchmarks focus on the most extensively studied problem settings and establish a foundation for further research and development. Additionally, the paper mentions an open source toolbox designed to reproduce these benchmark experiments, aiming to foster reproducibility and transparency in the field.
5. Current Limitations and Future Directions:
   • The paper concludes by discussing the existing limitations in the field. Despite significant progress, challenges remain in areas such as handling dynamic and highly complex scenes, integrating prior knowledge, and achieving real-time performance. The authors suggest potential future directions like improving model robustness, exploring unsupervised and semi-supervised learning paradigms, and developing better evaluation metrics.

Implications for AI Research

This survey provides a valuable resource for researchers in computer vision and artificial intelligence, summarizing key advancements and ongoing challenges in compositional scene representation learning. By presenting a clear picture of current methodologies and benchmarks, it helps researchers understand the state-of-the-art and identify areas needing further exploration. The emphasis on using unlabeled data for learning is particularly relevant, as it aligns with broader trends in AI towards more scalable and autonomous learning systems.