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arXiv · 2509.09732

Decomposing Visual Classification: Assessing Tree-Based Reasoning in VLMs

Sary Elmansoury·Islam Mesabah·Gerrit Großmann·Peter Neigel·Raj Bhalwankar·Daniel Kondermann·Sebastian J. Vollmer
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Abstract

Vision language models (VLMs) excel at zero-shot visual classification, but their performance on fine-grained tasks and large hierarchical label spaces is understudied. This paper investigates whether structured, tree-based reasoning can enhance VLM performance. We introduce a framework that decomposes classification into interpretable decisions using decision trees and evaluates it on fine-grained (GTSRB) and coarse-grained (CIFAR-10) datasets. Although the model achieves 98.2% accuracy in understanding the tree knowledge, tree-based reasoning consistently underperforms standard zero-shot prompting. We also explore enhancing the tree prompts with LLM-generated classes and image descriptions to improve alignment. The added description enhances the performance of the tree-based and zero-shot methods. Our findings highlight limitations of structured reasoning in visual classification and offer insights for designing more interpretable VLM systems.

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Sary Elmansoury, Islam Mesabah, Gerrit Großmann, Peter Neigel, Raj Bhalwankar, Daniel Kondermann, Sebastian J. Vollmer. 2025-09-10. Decomposing Visual Classification: Assessing Tree-Based Reasoning in VLMs. https://arxiv.org/abs/2509.09732

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