# 全景成对失真图

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-04-13 08:00
- AIHOT 链接：https://aihot.virxact.com/items/cmnyt3gat00nesl0f4ndghtll
- 原文链接：https://arxiv.org/abs/2604.11004

## AI 摘要

本文提出Distortion Graph（DG）任务，将图像对表示为基于区域的结构化拓扑，以图结构编码失真类型、严重程度及质量评分。工作贡献包括区域级数据集PandaSet、多难度基准PandaBench及高效架构Panda。实验表明，当前多模态大语言模型难以处理区域级失真，而基于PandaSet训练或DG提示可有效激发区域级理解能力，为细粒度结构化图像质量评估提供新方向。

## 正文

In this work, we introduce a new perspective on comparative image assessment by representing an image pair as a structured composition of its regions. In contrast, existing methods focus on whole image analysis, while implicitly relying on region-level understanding. We extend the intra-image notion of a scene graph to inter-image, and propose a novel task of Distortion Graph (DG). DG treats paired images as a structured topology grounded in regions, and represents dense degradation information such as distortion type, severity, comparison and quality score in a compact interpretable graph structure. To realize the task of learning a distortion graph, we contribute (i) a region-level dataset, PandaSet, (ii) a benchmark suite, PandaBench, with varying region-level difficulty, and (iii) an efficient architecture, Panda, to generate distortion graphs. We demonstrate that PandaBench poses a significant challenge for state-of-the-art multimodal large language models (MLLMs) as they fail to understand region-level degradations even when fed with explicit region cues. We show that training on PandaSet or prompting with DG elicits region-wise distortion understanding, opening a new direction for fine-grained, structured pairwise image assessment.