《Nano Banana Pro：低阶视觉全能选手？基于14项任务与40个数据集的综合评估》

摘要

文本到图像生成模型的快速发展正在彻底改变视觉内容创作方式。尽管Nano Banana Pro等商业产品已获得广泛关注，但其作为传统低层级视觉挑战通用解决方案的潜力仍待深入探索。本研究旨在回答一个关键问题：Nano Banana Pro是否堪称低层级视觉全能选手？我们通过对涵盖40个异构数据集的14类低层级任务进行系统化零样本评估，在未经微调的情况下使用简易文本提示，将Nano Banana Pro与最先进的专项模型进行对标。深入分析揭示出显著的性能二分现象：虽然Nano Banana Pro展现出卓越的主观视觉质量，其生成的高频细节甚至优于专项模型，但在传统基于参考图像的量化指标上表现欠佳。我们将此差异归因于生成模型固有的随机性特性，使其难以满足传统指标对像素级一致性的严苛要求。本报告确认Nano Banana Pro作为低层级视觉任务零样本解决方案的竞争力，同时指出要达到领域专家级保真度仍面临重大挑战。

English

The rapid evolution of text-to-image generation models has revolutionized visual content creation. While commercial products like Nano Banana Pro have garnered significant attention, their potential as generalist solvers for traditional low-level vision challenges remains largely underexplored. In this study, we investigate the critical question: Is Nano Banana Pro a Low-Level Vision All-Rounder? We conducted a comprehensive zero-shot evaluation across 14 distinct low-level tasks spanning 40 diverse datasets. By utilizing simple textual prompts without fine-tuning, we benchmarked Nano Banana Pro against state-of-the-art specialist models. Our extensive analysis reveals a distinct performance dichotomy: while Nano Banana Pro demonstrates superior subjective visual quality, often hallucinating plausible high-frequency details that surpass specialist models, it lags behind in traditional reference-based quantitative metrics. We attribute this discrepancy to the inherent stochasticity of generative models, which struggle to maintain the strict pixel-level consistency required by conventional metrics. This report identifies Nano Banana Pro as a capable zero-shot contender for low-level vision tasks, while highlighting that achieving the high fidelity of domain specialists remains a significant hurdle.

《Nano Banana Pro：低阶视觉全能选手？基于14项任务与40个数据集的综合评估》

Is Nano Banana Pro a Low-Level Vision All-Rounder? A Comprehensive Evaluation on 14 Tasks and 40 Datasets

摘要

Support