Embedding Provenance in Computer Vision Datasets with JSON-LD
With the ubiquity of computer vision in industry, the importance of image provenance is becoming more apparent. Provenance provides information about the origin and derivation of some resource, e.g., an image dataset, enabling users to trace data changes to better understand the expected behaviors of downstream models trained on such data. Provenance may also help with data maintenance by ensuring compliance, supporting audits and improving reusability. Typically, if provided, provenance is stored separately, e.g., within a text file, leading to a loss of descriptive information for key details like image capture settings, data preprocessing steps, and model architecture or iteration. Images often lack the information detailing the parameters of their creation or compilation. This paper proposes a novel schema designed to structure image provenance in a manageable and coherent format. The approach utilizes JavaScript Object Notation for Linked Data (JSON-LD), embedding this provenance directly within the image file. This offers two significant benefits: (1) it aligns image descriptions with a robust schema inspired by and linked to established standards, and (2) it ensures that provenance remains intrinsically tied to images, preventing loss of information and enhancing system qualities, e.g., maintainability and adaptability. This approach emphasizes maintaining the direct connection between vision resources and their provenance.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Benchmarking EfficientTAM on FMO datasets
Fast and tiny object tracking remains a challenge in computer vision and in this paper we first introduce a JSON metadata file associated with four open source datasets of Fast Moving Objects (FMOs) image sequences. In a…
Object TrackingLearning Transformation-Aware Embeddings for Image Forensics
A dramatic rise in the flow of manipulated image content on the Internet has led to an aggressive response from the media forensics research community. New efforts have incorporated increased usage of techniques from com…
Image ForensicsObject RecognitionModel Provenance via Model DNA
Understanding the life cycle of the machine learning (ML) model is an intriguing area of research (e.g., understanding where the model comes from, how it is trained, and how it is used). This paper focuses on a novel pro…
modelRepresentation LearningyProv4ML: Effortless Provenance Tracking for Machine Learning Systems
The rapid growth of interest in large language models (LLMs) reflects their potential for flexibility and generalization, and attracted the attention of a diverse range of researchers. However, the advent of these techni…
Image Provenance Analysis at Scale
Prior art has shown it is possible to estimate, through image processing and computer vision techniques, the types and parameters of transformations that have been applied to the content of individual images to obtain ne…
Authorship VerificationFact Checking