Learning Visual-Semantic Subspace Representations

Gabriel Moreira; Manuel Marques; João Paulo Costeira; Alexander Hauptmann

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arXiv · 2405.16213

Learning Visual-Semantic Subspace Representations

Gabriel Moreira·Manuel Marques·João Paulo Costeira·Alexander Hauptmann

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Abstract

Learning image representations that capture rich semantic relationships remains a significant challenge. Existing approaches are either contrastive, lacking robust theoretical guarantees, or struggle to effectively represent the partial orders inherent to structured visual-semantic data. In this paper, we introduce a nuclear norm-based loss function, grounded in the same information theoretic principles that have proved effective in self-supervised learning. We present a theoretical characterization of this loss, demonstrating that, in addition to promoting class orthogonality, it encodes the spectral geometry of the data within a subspace lattice. This geometric representation allows us to associate logical propositions with subspaces, ensuring that our learned representations adhere to a predefined symbolic structure.

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Gabriel Moreira, Manuel Marques, João Paulo Costeira, Alexander Hauptmann. 2024-05-25. Learning Visual-Semantic Subspace Representations. https://arxiv.org/abs/2405.16213

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