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Automating Threshold Selection for Relevant Image Retrieval in Automated Driving Systems Perception Testing


Core Concepts
An automated method for determining a threshold value to efficiently retrieve relevant images from a dataset for perception testing of automated driving systems, balancing false positives and false negatives.
Abstract
The paper presents a method for automatically determining a threshold value to efficiently retrieve relevant images from a dataset for perception testing of automated driving systems (ADS). The key points are: Existing approaches using CLIP (Contrastive Language-Image Pre-Training) can sort images by similarity to a textual prompt, but require manual definition of a threshold to select the relevant images. The authors propose an automated method to determine the threshold, modeling the distribution of cosine distances between image and prompt vectors as a sum of two Gaussian distributions. The threshold is set at the intersection of the two distributions, balancing false positives and false negatives. A fallback method using a single Gaussian distribution is provided in case the two-distribution model does not fit well. Experiments on the ACDC dataset show the method can effectively retrieve relevant images for prompts like 'snow', 'fog', 'rain', and 'night', with performance comparable to manually optimizing the F1 score. The fallback method is demonstrated on the 'traffic light' prompt, where the two-distribution model does not fit well, but the single Gaussian-based threshold still provides reasonable results. The automated threshold selection reduces manual effort in creating partial datasets for ADS perception testing, while maintaining a balance between false positives and false negatives.
Stats
The ACDC dataset contains 8012 images, including 4006 clear sky, 1000 fog, 1006 night, 1000 rain, and 1000 snow images.
Quotes
"Malfunctioning automated driving systems (ADS) carry the risk of causing damage and injuring or, in the worst case, killing people. ADS make their decisions based on their perception. It is therefore of utmost importance to test the perception excessively and ensure its robustness." "The disadvantage of this procedure is that the images in the database are only sorted. It is not determined up to which image the result matches the text request."

Key Insights Distilled From

by Philipp Rigo... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.05309.pdf
CLIPping the Limits

Deeper Inquiries

How could the proposed method be extended to handle more complex prompts that require understanding of multiple visual concepts

To handle more complex prompts that involve multiple visual concepts, the proposed method could be extended by incorporating a hierarchical approach. This approach would involve breaking down the complex prompt into simpler sub-prompts, each focusing on a specific visual concept. The images could then be sorted based on their similarity to each sub-prompt individually. By combining the results from each sub-prompt, a comprehensive understanding of the complex prompt can be achieved. Additionally, techniques such as multi-task learning could be employed to train the system to simultaneously recognize and sort images based on multiple visual concepts within the same prompt.

What other techniques beyond Gaussian mixture modeling could be explored to determine the threshold in a more robust manner

Beyond Gaussian mixture modeling, other techniques that could be explored to determine the threshold in a more robust manner include: Machine Learning Algorithms: Utilizing machine learning algorithms such as decision trees, random forests, or support vector machines to learn the optimal threshold based on the distribution of cosine distances. Clustering Methods: Employing clustering algorithms like k-means or DBSCAN to group images based on their cosine distances and then determining the threshold based on the cluster boundaries. Statistical Analysis: Conducting statistical tests to identify significant changes in the distribution of cosine distances and using these insights to set the threshold dynamically. Deep Learning Approaches: Leveraging deep learning models, such as neural networks, to learn the threshold value directly from the data, potentially using techniques like reinforcement learning for optimization.

How could the method be integrated with active learning approaches to iteratively refine the image retrieval for ADS perception testing

Integrating the method with active learning approaches can enhance the image retrieval process for ADS perception testing by iteratively refining the results. This integration could involve the following steps: Initial Training: Begin with a set of labeled images to train the model and set an initial threshold for image retrieval. Active Learning Selection: Use the current model to select a batch of images that are most uncertain or on the threshold boundary for manual labeling. Manual Labeling: Have human annotators label the selected images, adding them to the training set. Re-training and Threshold Adjustment: Update the model with the newly labeled images and adjust the threshold based on the updated model's performance. Iterative Process: Repeat the active learning cycle iteratively, gradually improving the model's performance and fine-tuning the threshold for more accurate image retrieval. By integrating active learning, the method can adapt and improve over time, focusing on the most informative images for training and refining the threshold to optimize the image retrieval process for ADS perception testing.
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