Det-LIME: Detector-Aware, Multi-Instance Local Interpretable Model-Agnostic Explanations for Automated Marine Mammal Detection
Paper recorded by Signals 4 on 2026-09-15 in cs.AI. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-15 on arXiv · recorded by Signals 4 on 2026-09-16
Category: cs.AI · 人工智能 · first seen 2026-09-16
Abstract
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 detections within a scene an
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Cite this page: Det-LIME: Detector-Aware, Multi-Instance Local Interpretable Model-Agnostic Explanations for Automated Marine Mammal Detection: the #49 most recent of 300 cs.AI papers we have recorded (as of 2026-09-15). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/det-lime-detector-aware-multi-instance-local-interpretable-model-agnostic-explan.html
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