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Det-LIME: Detector-Aware, Multi-Instance Local Interpretable Model-Agnostic Explanations for Automated Marine Mammal Detection | AI Learning Platform

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Det-LIME: Detector-Aware, Multi-Instance Local Interpretable Model-Agnostic Explanations for Automated Marine Mammal Detection

Jiayi Zhou, David W. Johnston, Brinnae Bent1 min readarXivSep 15, 2026

Key Takeaway for Students

Despite the rapid uptake of black-box object detectors in marine mammal research and monitoring, explainability techniques are rarely integrated into conservation workflows. Furthermore, most classification-oriented explainability tools are ill-suited to detection tasks involving imagery of social organisms or those with colonial life histories, as they ignore multiple detectio

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This paper introduces a novel explainability method, Det-LIME, specifically designed for object detection tasks, which is crucial for debugging and improving black-box models in real-world applications like conservation. It addresses the limitations of existing classification-oriented explainability tools when applied to multi-instance detection scenarios, providing more biologically relevant insights.

Read original on arXiv

Career Insight

medium demand

Why it matters

Skills to learn

Implement LIME for classification tasks using libraries like LIME or SHAPAdapt explainability techniques for object detection modelsDevelop custom explainability methods for multi-instance scenariosEvaluate the biological relevance of AI model explanationsIntegrate explainability into MLOps workflows for computer vision

Interview questions

Explain the core differences between LIME and SHAP, and why LIME might be preferred for certain types of models or tasks.
How would you adapt a classification-oriented explainability method like LIME to provide meaningful explanations for an object detection model that outputs multiple bounding boxes and class probabilities?
Design a system for continuously monitoring and explaining the predictions of a deployed marine mammal detection model, considering the need for multi-instance explanations and potential data drift.

Relevant for ~18 months · generated by the enrichment pipeline