SearchAD：面向自动驾驶的大规模稀有图像检索数据集
SearchAD: Large-Scale Rare Image Retrieval Dataset for Autonomous Driving
为视觉研究者提供了一个面向长尾/稀有类别的语义图像检索基准，可用于评测文本-图像与图像-图像检索模型在极稀有类别上的零样本与微调表现，并支撑检索驱动的数据筛选与长尾感知研究。
原文依据 SearchAD emphasizes semantic image retrieval with a well-defined data split, enabling text-to-image and image-to-image retrieval, few-shot learning
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论从大规模数据集中检索稀有且安全关键的驾驶场景，对于构建鲁棒的自动驾驶（AD）系统至关重要。随着数据集规模持续增长，关键挑战已从收集更多数据转向高效识别最相关的样本。我们提出 SearchAD，一个面向自动驾驶的大规模稀有图像检索数据集，包含来自 11 个既有数据集的超过 42.3 万帧图像。SearchAD 提供超过 51.3 万个边界框的高质量人工标注，覆盖 90 个稀有类别。它专门针对定位极稀有类别的“大海捞针”问题，其中部分类别在整个数据集中出现次数不足 50 次。与聚焦实例级检索的现有基准不同，SearchAD 强调语义图像检索，并提供定义明确的数据划分，支持文本到图像与图像到图像检索、少样本学习以及多模态检索模型的微调。综合评测表明，基于文本的方法由于具有更强的内在语义 grounding 而优于基于图像的方法。尽管直接将空间视觉特征与语言对齐的模型取得了最佳的零样本结果，且我们的微调基线显著提升了性能，但绝对检索能力仍不尽如人意。借助在公开基准服务器上的留出测试集，SearchAD 建立了首个面向自动驾驶中检索驱动的数据筛选与长尾感知研究的大规模数据集：https://iis-esslingen.github.io/searchad/
英文原文摘要 1542 字符
Retrieving rare and safety-critical driving scenarios from large-scale datasets is essential for building robust autonomous driving (AD) systems. As dataset sizes continue to grow, the key challenge shifts from collecting more data to efficiently identifying the most relevant samples. We introduce SearchAD, a large-scale rare image retrieval dataset for AD containing over 423k frames drawn from 11 established datasets. SearchAD provides high-quality manual annotations of more than 513k bounding boxes covering 90 rare categories. It specifically targets the "needle-in-a-haystack" problem of locating extremely rare classes, with some appearing fewer than 50 times across the entire dataset. Unlike existing benchmarks, which focused on instance-level retrieval, SearchAD emphasizes semantic image retrieval with a well-defined data split, enabling text-to-image and image-to-image retrieval, few-shot learning, and fine-tuning of multi-modal retrieval models. Comprehensive evaluations show that text-based methods outperform image-based ones due to stronger inherent semantic grounding. While models directly aligning spatial visual features with language achieve the best zero-shot results, and our fine-tuning baseline significantly improves performance, absolute retrieval capabilities remain unsatisfactory. With a held-out test set on a public benchmark server, SearchAD establishes the first large-scale dataset for retrieval-driven data curation and long-tail perception research in AD: https://iis-esslingen.github.io/searchad/