SearchAD: Large-Scale Rare Image Retrieval Dataset for Autonomous Driving

TL;DR

SearchAD is a large-scale dataset designed for semantic image retrieval of rare driving scenarios, facilitating research in autonomous driving safety and long-tail perception.

Contribution

It introduces a comprehensive dataset with annotations for rare classes, enabling diverse retrieval tasks and benchmarking for autonomous driving applications.

Findings

Text-based retrieval methods outperform image-based ones.

Spatial visual features align well with language in zero-shot models.

Fine-tuning improves retrieval performance, but results are still limited.

Abstract

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/