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arXiv · 2606.22409

Gold Points Sniper: Self-guided Visual Reasoning in VLM for Fine-grained Action Understanding

Haodi Liu·Xinhang Yang·Kunda Yan·Sen Cui·Zeyu Zhang·Changshui Zhang
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Abstract

Robots operating in everyday environments must understand fine-grained human actions, intentions, and contextual cues from broad views where people occupy only small regions, a capability unmet by current systems. While open-vocabulary action recognition methods remain limited to assigning predefined labels, and vision-language models (VLMs) face an inherent trade-off between informational richness and factual fidelity in their outputs, neither approach achieves the deep semantic interpretation required for reliable human-robot interaction. We propose Gold Points Sniper (GPS), a novel framework that empowers lightweight VLMs with self-guided multimodal reasoning capabilities for fine-grained human action understanding. Our approach comprises three key modules: Gold Points Extractor trains VLMs to identify critical action-relevant details, Selective Socratic Questioner validates and refines these details through selective self-questioning, and Semantic Entailment Evaluator quantitatively assesses factual consistency using semantic entailment classification. Extensive experiments on our curated instruction-tuning dataset based on the CAP benchmark demonstrate that GPS-enhanced lightweight VLMs achieve substantial performance improvements, with some models reaching performance comparable to proprietary GPT-4o while maintaining superior factual accuracy. Our work establishes a reliable foundation for fine-grained action understanding in domestic robotics, enabling robots to safely interpret human behavior through information-dense yet factually grounded descriptions. Source code, training configurations, annotation prompts, and dataset details are released at https://github.com/Haodi-Liu/GPS-Gold-Point-Sniper.

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Haodi Liu, Xinhang Yang, Kunda Yan, Sen Cui, Zeyu Zhang, Changshui Zhang. 2026-06-21. Gold Points Sniper: Self-guided Visual Reasoning in VLM for Fine-grained Action Understanding. https://arxiv.org/abs/2606.22409

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