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

• URL: http://arxiv.org/abs/2609.17479v1
• Date: Tue, 15 Sep 2026 17:20:16 GMT
• Title: Det-LIME: Detector-Aware, Multi-Instance Local Interpretable Model-Agnostic Explanations for Automated Marine Mammal Detection
• Abstract summary: We propose Det-LIME, a detector-aware, multi-instance adaptation of Local Interpretable Model-Agnostic Explanations (LIME)<n>Det-LIME produces instance-specific, box-aligned explanations by combining per-detection weighting, a proximity kernel that emphasizes regions near each box, and Intersection-over-Union-based matching to track the same instance across perturbations.
• Score: 5.042506569800239
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 and produce single-instance outputs that blur evidence across individuals. These methods also generate low-resolution, often biologically irrelevant visuals, limiting their utility for debugging, targeted data augmentation, and refined data collection. We proposed Det-LIME, a detector-aware, multi-instance adaptation of Local Interpretable Model-Agnostic Explanations (LIME) that produced instance-specific, box-aligned explanations by combining per-detection weighting, a proximity kernel that emphasizes regions near each box, and Intersection-over-Union-based matching to track the same instance across perturbations. We evaluated Det-LIME on aerial drone imagery for harbor seal detection, with an additional seabird case study to assess generality, and compared it with vanilla LIME, Stabilized LIME, Deterministic LIME, and gradient-based attribution methods. Using the Attribution Ratio and Max Saliency Hit Rate metrics, we showed that Det-LIME consistently improved multi-instance attribution. In practice, these higher-resolution, instance-aware explanations provide insight into model outputs and support post-processing, debugging, and actionable improvements in modeling and data collection or augmentation.

Related papers

• EIVE: End-to-End Instance-Specific Visual Explanations for Detection Transformers [7.91708974258006]
  We propose an End-to-end Instance-specific Visual Explanation framework (EIVE)<n>EIVE directly generates instance-level saliency maps following the forward pass of Detection Transformer (DETR)-like models.<n> Experiments on MS 2017, ExDark, and Cityscapes demonstrate that EIVE produces high-quality instance-level saliency maps.
  arXiv  Detail & Related papers  (2026-06-01T02:56:31Z)
• Low-Cost Black-Box Detection of LLM Hallucinations via Dynamical System Prediction [3.6209977238182276]
  Large Language Models (LLMs) frequently generate plausible but non-factual content, a phenomenon known as hallucination.<n>We propose a new method that treats the LLM as a black-box dynamical system.<n>Our method achieves state-of-the-art performance with reduced resource overhead.
  arXiv  Detail & Related papers  (2026-05-06T17:07:29Z)
• AgentIAD: Tool-Augmented Single-Agent for Industrial Anomaly Detection [9.208080780536504]
  AgentIAD is a tool-driven framework that enables multi-stage visual inspection.<n>AgentIAD achieves a new state-of-the-art 97.62% classification accuracy on MMAD.
  arXiv  Detail & Related papers  (2025-12-15T18:57:04Z)
• CLIP Meets Diffusion: A Synergistic Approach to Anomaly Detection [49.11819337853632]
  Anomaly detection is a complex problem due to the ambiguity in defining anomalies, the diversity of anomaly types, and the scarcity of training data.<n>We propose CLIPfusion, a method that leverages both discriminative and generative foundation models.<n>We believe that our method underscores the effectiveness of multi-modal and multi-model fusion in tackling the multifaceted challenges of anomaly detection.
  arXiv  Detail & Related papers  (2025-06-13T13:30:15Z)
• VDC: Versatile Data Cleanser based on Visual-Linguistic Inconsistency by Multimodal Large Language Models [46.72546879204724]
  In the real-world, datasets may contain dirty samples, such as poisoned samples from backdoor attack, noisy labels in crowdsourcing, and even hybrids of them. Existing detectors only focus on detecting poisoned samples or noisy labels, that are often prone to weak generalization when dealing with dirty samples from other domains. We propose versatile data cleanser (VDC) leveraging the surpassing capabilities of multimodal large language models (MLLM) in cross-modal alignment and reasoning.
  arXiv  Detail & Related papers  (2023-09-28T07:37:18Z)
• ODAM: Gradient-based instance-specific visual explanations for object detection [51.476702316759635]
  gradient-weighted Object Detector Activation Maps (ODAM) ODAM produces heat maps that show the influence of regions on the detector's decision for each predicted attribute. We propose Odam-NMS, which considers the information of the model's explanation for each prediction to distinguish duplicate detected objects.
  arXiv  Detail & Related papers  (2023-04-13T09:20:26Z)
• SIOD: Single Instance Annotated Per Category Per Image for Object Detection [67.64774488115299]
  We propose the Single Instance annotated Object Detection (SIOD), requiring only one instance annotation for each existing category in an image. Degraded from inter-task (WSOD) or inter-image (SSOD) discrepancies to the intra-image discrepancy, SIOD provides more reliable and rich prior knowledge for mining the rest of unlabeled instances. Under the SIOD setting, we propose a simple yet effective framework, termed Dual-Mining (DMiner), which consists of a Similarity-based Pseudo Label Generating module (SPLG) and a Pixel-level Group Contrastive Learning module (PGCL)
  arXiv  Detail & Related papers  (2022-03-29T08:49:51Z)
• Functional Anomaly Detection: a Benchmark Study [4.444788548423704]
  Anomaly detection can now rely on measurements sampled at a very high frequency. It is the purpose of this paper to investigate the performance of recent techniques for anomaly detection in the functional setup on real datasets.
  arXiv  Detail & Related papers  (2022-01-13T18:20:32Z)
• Towards Real-World Prohibited Item Detection: A Large-Scale X-ray Benchmark [53.9819155669618]
  This paper presents a large-scale dataset, named as PIDray, which covers various cases in real-world scenarios for prohibited item detection. With an intensive amount of effort, our dataset contains $12$ categories of prohibited items in $47,677$ X-ray images with high-quality annotated segmentation masks and bounding boxes. The proposed method performs favorably against the state-of-the-art methods, especially for detecting the deliberately hidden items.
  arXiv  Detail & Related papers  (2021-08-16T11:14:16Z)
• Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders [51.691585766702744]
  We propose a variant of Adversarial Autoencoder which uses a mirrored Wasserstein loss in the discriminator to enforce better semantic-level reconstruction. We put forward an alternative measure of anomaly score to replace the reconstruction-based metric. Our method outperforms the current state-of-the-art methods for anomaly detection on several OOD detection benchmarks.
  arXiv  Detail & Related papers  (2020-03-24T08:26:58Z)
• EHSOD: CAM-Guided End-to-end Hybrid-Supervised Object Detection with Cascade Refinement [53.69674636044927]
  We present EHSOD, an end-to-end hybrid-supervised object detection system. It can be trained in one shot on both fully and weakly-annotated data. It achieves comparable results on multiple object detection benchmarks with only 30% fully-annotated data.
  arXiv  Detail & Related papers  (2020-02-18T08:04:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.