Disentanglement Beyond Static vs. Dynamic: A Benchmark and Evaluation Framework for Multi-Factor Sequential Representations

2025-10-20

AI Summary

To address the challenge of learning disentangled representations from real-world sequential data characterized by complex, coupled factors, this paper introduces the first benchmark platform encompassing six diverse datasets across video, audio, and time-series modalities. Methodologically: (1) it employs Koopman operator theory to model temporal dependencies, transcending the conventional static-dynamic dichotomy; (2) it incorporates a latent-variable exploration phase enabling automatic semantic factor alignment; and (3) it integrates vision-language models to support zero-shot automatic annotation and unsupervised evaluation. Contributions include: the first multimodal, multi-factor disentanglement benchmark; a scalable, modular modeling paradigm; and an end-to-end disentangled learning and evaluation pipeline requiring no manual annotations. Experiments demonstrate state-of-the-art performance in multi-factor disentanglement quality, cross-modal generalization, and interpretability.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Learning disentangled representations in sequential data is a key goal in deep learning, with broad applications in vision, audio, and time series. While real-world data involves multiple interacting semantic factors over time, prior work has mostly focused on simpler two-factor static and dynamic settings, primarily because such settings make data collection easier, thereby overlooking the inherently multi-factor nature of real-world data. We introduce the first standardized benchmark for evaluating multi-factor sequential disentanglement across six diverse datasets spanning video, audio, and time series. Our benchmark includes modular tools for dataset integration, model development, and evaluation metrics tailored to multi-factor analysis. We additionally propose a post-hoc Latent Exploration Stage to automatically align latent dimensions with semantic factors, and introduce a Koopman-inspired model that achieves state-of-the-art results. Moreover, we show that Vision-Language Models can automate dataset annotation and serve as zero-shot disentanglement evaluators, removing the need for manual labels and human intervention. Together, these contributions provide a robust and scalable foundation for advancing multi-factor sequential disentanglement.

Problem

• Addressing multi-factor sequential disentanglement in real-world data
• Overcoming limitations of static and dynamic two-factor approaches
• Providing standardized benchmarks and tools for evaluation

Innovation

• Introduced first multi-factor sequential disentanglement benchmark
• Proposed Koopman-inspired model achieving state-of-the-art performance
• Used Vision-Language Models for automated annotation and evaluation