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Found 26 papers, 9 papers with code
Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection
In this paper we propose a method that leverages temporal context from the unlabeled frames of a novel camera to improve performance at that camera.
Recognition in Terra Incognita
The challenge is learning recognition in a handful of locations, and generalizing animal detection and classification to new locations where no training data is available.
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Distribution shifts -- where the training distribution differs from the test distribution -- can substantially degrade the accuracy of machine learning (ML) systems deployed in the wild.
Efficient Pipeline for Camera Trap Image Review
Biologists all over the world use camera traps to monitor biodiversity and wildlife population density.
A deep active learning system for species identification and counting in camera trap images
However, the accuracy of results depends on the amount, quality, and diversity of the data available to train models, and the literature has focused on projects with millions of relevant, labeled training images.
Extending the WILDS Benchmark for Unsupervised Adaptation
Unlabeled data can be a powerful point of leverage for mitigating these distribution shifts, as it is frequently much more available than labeled data and can often be obtained from distributions beyond the source distribution as well.
Benchmarking Representation Learning for Natural World Image Collections
In order to facilitate progress in this area we present two new natural world visual classification datasets, iNat2021 and NeWT.
The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and Counting
We present the Caltech Fish Counting Dataset (CFC), a large-scale dataset for detecting, tracking, and counting fish in sonar videos.
Align and Distill: Unifying and Improving Domain Adaptive Object Detection
We address these problems by introducing: (1) A unified benchmarking and implementation framework, Align and Distill (ALDI), enabling comparison of DAOD methods and supporting future development, (2) A fair and modern training and evaluation protocol for DAOD that addresses benchmarking pitfalls, (3) A new DAOD benchmark dataset, CFC-DAOD, enabling evaluation on diverse real-world data, and (4) A new method, ALDI++, that achieves state-of-the-art results by a large margin.
Ranked #1 on
Unsupervised Domain Adaptation
on CFC-DAOD
The iWildCam 2018 Challenge Dataset
Camera traps are a valuable tool for studying biodiversity, but research using this data is limited by the speed of human annotation.
Synthetic Examples Improve Generalization for Rare Classes
The ability to detect and classify rare occurrences in images has important applications - for example, counting rare and endangered species when studying biodiversity, or detecting infrequent traffic scenarios that pose a danger to self-driving cars.
The iWildCam 2019 Challenge Dataset
We use the Caltech Camera Traps dataset, collected from the American Southwest, as training data.
The iWildCam 2020 Competition Dataset
Can we leverage data from other modalities, such as citizen science data and remote sensing data?
The iWildCam 2021 Competition Dataset
The challenge is to classify species and count individual animals across sequences in the test cameras.
Can poachers find animals from public camera trap images?
To protect the location of camera trap data containing sensitive, high-target species, many ecologists randomly obfuscate the latitude and longitude of the camera when publishing their data.
Image-to-Image Translation of Synthetic Samples for Rare Classes
The natural world is long-tailed: rare classes are observed orders of magnitudes less frequently than common ones, leading to highly-imbalanced data where rare classes can have only handfuls of examples.
ElephantBook: A Semi-Automated Human-in-the-Loop System for Elephant Re-Identification
African elephants are vital to their ecosystems, but their populations are threatened by a rise in human-elephant conflict and poaching.
Species Distribution Modeling for Machine Learning Practitioners: A Review
How many species live there?
Seeing biodiversity: perspectives in machine learning for wildlife conservation
Data acquisition in animal ecology is rapidly accelerating due to inexpensive and accessible sensors such as smartphones, drones, satellites, audio recorders and bio-logging devices.
Domain Adaptation for Rare Classes Augmented with Synthetic Samples
While domain adaptation is generally applied on completely synthetic source domains and real target domains, we explore how domain adaptation can be applied when only a single rare class is augmented with simulated samples.
The Auto Arborist Dataset: A Large-Scale Benchmark for Multiview Urban Forest Monitoring Under Domain Shift
We introduce baseline results on our dataset across modalities as well as metrics for the detailed analysis of generalization with respect to geographic distribution shifts, vital for such a system to be deployed at-scale.
Teaching Computer Vision for Ecology
Computer vision can accelerate ecology research by automating the analysis of raw imagery from sensors like camera traps, drones, and satellites.
Vision Models Can Be Efficiently Specialized via Few-Shot Task-Aware Compression
To address this, we ask: can we quickly compress large generalist models into accurate and efficient specialists?
MammalNet: A Large-scale Video Benchmark for Mammal Recognition and Behavior Understanding
Monitoring animal behavior can facilitate conservation efforts by providing key insights into wildlife health, population status, and ecosystem function.
Reflections from the Workshop on AI-Assisted Decision Making for Conservation
In this white paper, we synthesize key points made during presentations and discussions from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for Research on Computation and Society at Harvard University on October 20-21, 2022.
Application-Driven Innovation in Machine Learning
As applications of machine learning proliferate, innovative algorithms inspired by specific real-world challenges have become increasingly important.
