#5838. Context-based image explanations for deep neural networks

July 2026publication date
Proposal available till 17-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Computer Vision and Pattern Recognition;
Signal Processing;
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Abstract:
With the increased use of machine learning in decision-making scenarios, there has been a growing interest in explaining and understanding the outcomes of machine learning models. Despite this growing interest, existing works on interpretability and explanations have been mostly intended for expert users. Explanations for general users have been neglected in many usable and practical applications (e.g., image tagging, caption generation). It is important for non-technical users to understand features and how they affect an instance-specific prediction to satisfy the need for justification. In this paper, we propose a model-agnostic method for generating context-based explanations aiming for general users. We implement partial masking on segmented components to identify the contextual importance of each segment in scene classification tasks. We then generate explanations based on feature importance.
Keywords:
Contextual importance; DNNs; Explainable AI; Visual explanations

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