Active prototype triage reduces expert review in animal detection
Resolve, Clear, or Defer: Training-Free Active Prototype Triage for Ecological Detection Pipelines
Computer Vision and Pattern Recognition
Summary
Large collections of images from camera traps and aerial surveys contain many animals but also many false or unclear detections that experts must review. The authors propose a method that uses previously reviewed examples to help decide which new detections can be automatically identified, which are false positives, and which still need expert checks. This method requires no additional training and uses different pretrained models to better separate animals from backgrounds. Their approach reduces the number of cases needing expert review while improving how well real animals are identified.
What this means in practice
- •For wildlife monitoring teams: Reduce expert time spent reviewing detections by automatically confirming or discarding many camera trap and aerial survey images using reviewed prototypes.
- •For environmental data analysts: Improve accuracy in identifying animal species from large ecological image datasets by reusing prior expert labels to guide review priorities without retraining models.
Authors
Anirudh Potlapally, Brijesh Nanda Brunda Yogananda, Jianyang Gu, Maanav Jajodia, Luke Meyers, Wei-Lun Chao, Hari Subramoni
Abstract
Ecological monitoring increasingly relies on camera traps and aerial surveys that generate large image collections. High-recall animal detectors can localize candidate animals at scale. However, they also produce many detections that still require expert review, including false positives, ambiguous cases, and animals difficult to identify at the species level. Since exhaustive review requires substantial effort, we propose training-free active prototype triage, where reviewed detections become reusable evidence to (1) Resolve supported foreground classes, (2) Clear likely background false positives, or (3) Defer insufficiently supported cases for expert review. Across camera-trap and aerial datasets, foreground recognition and false-positive rejection benefit from different pretrained representations: BioCLIP2 provides foreground evidence while DINOv3 provides background evidence, reducing erroneous foreground clearing while improving automatic foreground recovery. Deferring unsupported cases avoids the higher error rates caused by forced decisions. Compared with confidence-thresholded multiclass k-NN and uncertainty-sampling logistic regression, prototype triage improves foreground recovery while maintaining competitive or lower review burden. Acquisition goes beyond class discovery: even after classes are represented, prototype choice determines how reliably evidence propagates through the remaining detections. Our results suggest that once animal localization reaches high recall, the challenge shifts from finding animals to deciding which examples to review, how to reuse them, and when evidence does not support an automatic decision.