Sequential Representation Learning via Static-Dynamic Conditional Disentanglement

Autor:	Simon, Mathieu Cyrille, Frossard, Pascal, De Vleeschouwer, Christophe
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	This paper explores self-supervised disentangled representation learning within sequential data, focusing on separating time-independent and time-varying factors in videos. We propose a new model that breaks the usual independence assumption between those factors by explicitly accounting for the causal relationship between the static/dynamic variables and that improves the model expressivity through additional Normalizing Flows. A formal definition of the factors is proposed. This formalism leads to the derivation of sufficient conditions for the ground truth factors to be identifiable, and to the introduction of a novel theoretically grounded disentanglement constraint that can be directly and efficiently incorporated into our new framework. The experiments show that the proposed approach outperforms previous complex state-of-the-art techniques in scenarios where the dynamics of a scene are influenced by its content. Comment: Accepted at ECCV 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2408.05599 Zobrazit plný text záznamu View this record from Arxiv