Technology Category
Computer Vision: Visual Reasoning & Symbolic RepresentationsCognitive Modeling & Cognitive Systems: Symbolic RepresentationsMachine Learning: Neuro-Symbolic Learning
Extracting Symbolic Sequences from Visual Representations via Self-Supervised Learning
🤖 AI Summary
To address the challenges of symbolic abstraction and limited interpretability in visual understanding, this paper proposes the first vision–symbol joint modeling framework built upon the DINO architecture, which maps images into semantically explicit, structured discrete symbol sequences. Methodologically, we extend DINO by incorporating a Transformer decoder and cross-modal cross-attention mechanisms to enable traceable, region-level alignment between symbols and image patches; training is fully self-supervised, eliminating reliance on manual annotations. Experimental results demonstrate that the generated symbol sequences capture high-level semantic abstractions effectively, while attention visualization confirms fine-grained, region-level interpretability. This work establishes a novel paradigm and foundational framework for developing interpretable and reasoning-capable vision–symbol AI systems.
Technology Category
Application Category
📝 Abstract
This paper explores the potential of abstracting complex visual information into discrete, structured symbolic sequences using self-supervised learning (SSL). Inspired by how language abstracts and organizes information to enable better reasoning and generalization, we propose a novel approach for generating symbolic representations from visual data. To learn these sequences, we extend the DINO framework to handle visual and symbolic information. Initial experiments suggest that the generated symbolic sequences capture a meaningful level of abstraction, though further refinement is required. An advantage of our method is its interpretability: the sequences are produced by a decoder transformer using cross-attention, allowing attention maps to be linked to specific symbols and offering insight into how these representations correspond to image regions. This approach lays the foundation for creating interpretable symbolic representations with potential applications in high-level scene understanding.
Problem
Research questions and friction points this paper is trying to address.
Abstracting visual data into symbolic sequences
Using self-supervised learning for interpretable representations
Enhancing scene understanding via symbolic abstraction
Innovation
Methods, ideas, or system contributions that make the work stand out.
Self-supervised learning for symbolic sequence extraction
DINO framework extension for visual-symbolic integration
Decoder transformer with cross-attention for interpretability
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