English(EN) PSGAgent: Enhancing MLLMs' Dynamic Visual Perception for Panoptic Scene Graph Generation via Agentic Reasoning

新的PSGAgent框架增强了MLLM的视觉感知能力，用于场景图生成

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-06 05:00

研究人员推出了一种新颖的PSGAgent框架，旨在提高多模态大语言模型（MLLM）在全景场景图生成方面的视觉感知能力。这种代理方法利用了“思考-行动-观察”循环，采用区域放大工具来详细检查小物体或边界关系，并使用视觉高亮工具进行聚焦分割。该框架采用两阶段流程进行训练，包括多模态思维链数据集和具有分层奖励的组序列策略优化方法，从而在PSG基准测试中取得了最先进的性能。 AI

影响增强了MLLM理解复杂视觉场景的能力，有望改进图像分析和解释方面的应用。

排序理由这是一篇详细介绍改进MLLM视觉感知新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Qingyun Liu, Yuankun Liu, Jiaye Feng, Jie Huang, Zonghong Dai, Haochen Li, Jingkun Wang, Weiping Li, Tong Mo · 2026-11-06 05:00

PSGAgent：通过代理推理增强MLLM的动态视觉感知以进行全景场景图生成

arXiv:2611.07408v1 Announce Type: cross Abstract: Panoptic Scene Graph generation (PSG) requires holistic yet fine-grained reasoning over complex relations among objects. However, current Multimodal Large Language Models (MLLMs) applied to PSG suffer from a perception bottleneck:…

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PSGAgent：通过代理推理增强MLLM的动态视觉感知以进行全景场景图生成

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