Visually Interpretable Subtask Reasoning for Visual Question Answering

• URL: http://arxiv.org/abs/2505.08084v1
• Date: Mon, 12 May 2025 21:37:06 GMT
• Title: Visually Interpretable Subtask Reasoning for Visual Question Answering
• Authors: Yu Cheng, Arushi Goel, Hakan Bilen,
• Abstract summary: We introduce VISTAR (Visually Interpretable Subtask-Aware Reasoning Model), a subtask-driven training framework that enhances interpretability and reasoning.<n>Instead of relying on external relational models, VISTAR fine-tunes MLLMs to produce structured Subtask-of-Thought rationales.<n>Experiments show that VISTAR consistently improves reasoning accuracy while maintaining interpretability.
• Score: 35.29789706461531
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Answering complex visual questions like `Which red furniture can be used for sitting?' requires multi-step reasoning, including object recognition, attribute filtering, and relational understanding. Recent work improves interpretability in multimodal large language models (MLLMs) by decomposing tasks into sub-task programs, but these methods are computationally expensive and less accurate due to poor adaptation to target data. To address this, we introduce VISTAR (Visually Interpretable Subtask-Aware Reasoning Model), a subtask-driven training framework that enhances both interpretability and reasoning by generating textual and visual explanations within MLLMs. Instead of relying on external models, VISTAR fine-tunes MLLMs to produce structured Subtask-of-Thought rationales (step-by-step reasoning sequences). Experiments on two benchmarks show that VISTAR consistently improves reasoning accuracy while maintaining interpretability. Our code and dataset will be available at https://github.com/ChengJade/VISTAR.

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