# Weakly Supervised Faster-RCNN+FPN to classify animals in camera trap images

arXiv: https://arxiv.org/abs/2208.14060 · HTML: https://mlsanity.com/paper/2208.14060/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 32 references that are in the map.
- First posted on arXiv 30 Aug 2022.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet](https://mlsanity.com/paper/1904.00760/) (2019)
- [Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach](https://mlsanity.com/paper/1703.08448/) (2017)
- [Auto-Keras: An Efficient Neural Architecture Search System](https://mlsanity.com/paper/1806.10282/) (2018)
- [Salient Object Detection: A Discriminative Regional Feature Integration Approach](https://mlsanity.com/paper/1410.5926/) (2014)
- [STC: A Simple to Complex Framework for Weakly-supervised Semantic Segmentation](https://mlsanity.com/paper/1509.03150/) (2015)
- [C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection](https://mlsanity.com/paper/1904.05647/) (2019)
- [Joint Learning of Saliency Detection and Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/1909.04161/) (2019)
- [Needles in Haystacks: On Classifying Tiny Objects in Large Images](https://mlsanity.com/paper/1908.06037/) (2019)
- [Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection](https://mlsanity.com/paper/1604.05766/) (2016)

## Built on by (most cited first, 1 of 1)

- [Weakly supervised framework for wildlife detection and counting in challenging Arctic environments: a case study on caribou (Rangifer tarandus)](https://mlsanity.com/paper/2601.18891/) (2026)

## Abstract (arXiv)

Camera traps have revolutionized the animal research of many species that were previously nearly impossible to observe due to their habitat or behavior. They are cameras generally fixed to a tree that take a short sequence of images when triggered. Deep learning has the potential to overcome the workload to automate image classification according to taxon or empty images. However, a standard deep neural network classifier fails because animals often represent a small portion of the high-definition images. That is why we propose a workflow named Weakly Object Detection Faster-RCNN+FPN which suits this challenge. The model is weakly supervised because it requires only the animal taxon label per image but doesn't require any manual bounding box annotations. First, it automatically performs the weakly-supervised bounding box annotation using the motion from multiple frames. Then, it trains a Faster-RCNN+FPN model using this weak supervision. Experimental results have been obtained with two datasets from a Papua New Guinea and Missouri biodiversity monitoring campaign, then on an easily reproducible testbed.