Object Detection Edge Case Analysis Using Visual Concepts
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Solution Overview
Problem
Existing data slice finding techniques for machine learning models require additional metadata or cross-modal embeddings for interpretability, leading to resource-intensive processes in terms of cost and time, and lack transparency in identifying model failures.
Innovation Solution
A machine learning network that leverages self-supervised semantic segmentation to generate visual concepts as metadata, providing interpretable visualizations and interactions through a user interface, and coordinates the retrieval of additional image samples for supplemental training datasets without requiring additional metadata.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If additional metadata or cross-modal embeddings are used for data slice finding, then interpretability of model failures is improved, but resource intensity (cost and time) increases
Solution Approach 1:
The system uses self-supervised learning to automatically generate visual concepts from image data without requiring manual annotation or external metadata. The model serves itself by extracting meaningful visual representations that enable interpretability of failure modes while avoiding the resource costs of traditional metadata approaches
Solution Approach 2:
The system extracts visual concepts directly from the image data itself rather than relying on external metadata or cross-modal embeddings. By taking out and utilizing the inherent visual information in the images, the system achieves interpretability without the additional resource burden of external data sources
2Reliability
If traditional data slice finding techniques are used, then model validation is performed, but transparency in identifying model failures is reduced
Solution Approach 1:
The system transforms the abstract and opaque failure identification process into a visually interpretable form by generating visual concepts that highlight specific regions and features in images. This visual transformation makes model failures transparent and understandable, while maintaining rigorous model validation
Solution Approach 2:
Visual concepts serve as an intermediary between the model's internal decision-making process and human interpretation. These concepts bridge the gap by providing a visual representation of what the model focuses on, making the validation process transparent without compromising reliability
Data Source
AI summary
Methods for a machine learning network that provide efficient, scalable, and granular analyses during validation of an object detection model are disclosed. The system described herein is configured to use extraction of visual concepts to provide interpretable metadata to a data slice finding technique. The identified, poor-performing slices are then provided to a user for selection as to which slice or slices to focus on when preparing a subsequent training dataset that is to be used to further refine the object detection model. The system then coordinates with a large language model and with a vision and language foundational model to augment the original validation dataset with supplementary image samples that are determined to be associated with the problems currently causing poor performance of the model.