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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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#49 most recent of 300 cs.AI papers we have recorded · ↑ newer: LimiX-2: A Contextual Mechanism Network Towards General Structured-Dat · ↓ older: JustFit: 200K-Token LLM Serving on a 24 GiB Laptop with Just-in-Time S
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
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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