# Focus on the Challenges: Analysis of a User-friendly Data Search Approach with CLIP in the Automotive Domain

arXiv: https://arxiv.org/abs/2304.10247 · HTML: https://mlsanity.com/paper/2304.10247/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2023.

## Built on by (most cited first, 5 of 5)

- [Unveiling Objects with SOLA: An Annotation-Free Image Search on the Object Level for Automotive Data Sets](https://mlsanity.com/paper/2312.01860/) (2023)
- [Data Quality Matters: Quantifying Image Quality Impact on Machine Learning Performance](https://mlsanity.com/paper/2503.22375/) (2025)
- [Mcity Data Engine: Iterative Model Improvement Through Open-Vocabulary Data Selection](https://mlsanity.com/paper/2504.21614/) (2025)
- [CLIPping the Limits: Finding the Sweet Spot for Relevant Images in Automated Driving Systems Perception Testing](https://mlsanity.com/paper/2404.05309/) (2024)
- [From Big Data to Fast Data: Towards High-Quality Datasets for Machine Learning Applications from Closed-Loop Data Collection](https://mlsanity.com/paper/2603.29474/) (2026)

## Abstract (arXiv)

Handling large amounts of data has become a key for developing automated driving systems. Especially for developing highly automated driving functions, working with images has become increasingly challenging due to the sheer size of the required data. Such data has to satisfy different requirements to be usable in machine learning-based approaches. Thus, engineers need to fully understand their large image data sets for the development and test of machine learning algorithms. However, current approaches lack automatability, are not generic and are limited in their expressiveness. Hence, this paper aims to analyze a state-of-the-art text and image embedding neural network and guides through the application in the automotive domain. This approach enables the search for similar images and the search based on a human understandable text-based description. Our experiments show the automatability and generalizability of our proposed method for handling large data sets in the automotive domain.