Search Results for author: Sara Beery

Found 24 papers, 8 papers with code

Reflections from the Workshop on AI-Assisted Decision Making for Conservation

no code implementations17 Jul 2023 Lily Xu, Esther Rolf, Sara Beery, Joseph R. Bennett, Tanya Berger-Wolf, Tanya Birch, Elizabeth Bondi-Kelly, Justin Brashares, Melissa Chapman, Anthony Corso, Andrew Davies, Nikhil Garg, Angela Gaylard, Robert Heilmayr, Hannah Kerner, Konstantin Klemmer, Vipin Kumar, Lester Mackey, Claire Monteleoni, Paul Moorcroft, Jonathan Palmer, Andrew Perrault, David Thau, Milind Tambe

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.

Decision Making

MammalNet: A Large-scale Video Benchmark for Mammal Recognition and Behavior Understanding

no code implementations CVPR 2023 Jun Chen, Ming Hu, Darren J. Coker, Michael L. Berumen, Blair Costelloe, Sara Beery, Anna Rohrbach, Mohamed Elhoseiny

Monitoring animal behavior can facilitate conservation efforts by providing key insights into wildlife health, population status, and ecosystem function.

Vision Models Can Be Efficiently Specialized via Few-Shot Task-Aware Compression

no code implementations25 Mar 2023 Denis Kuznedelev, Soroush Tabesh, Kimia Noorbakhsh, Elias Frantar, Sara Beery, Eldar Kurtic, Dan Alistarh

To address this, we ask: can we quickly compress large generalist models into accurate and efficient specialists?

Teaching Computer Vision for Ecology

no code implementations5 Jan 2023 Elijah Cole, Suzanne Stathatos, Björn Lütjens, Tarun Sharma, Justin Kay, Jason Parham, Benjamin Kellenberger, Sara Beery

Computer vision can accelerate ecology research by automating the analysis of raw imagery from sensors like camera traps, drones, and satellites.

The Auto Arborist Dataset: A Large-Scale Benchmark for Multiview Urban Forest Monitoring Under Domain Shift

no code implementations CVPR 2022 Sara Beery, Guanhang Wu, Trevor Edwards, Filip Pavetic, Bo Majewski, Shreyasee Mukherjee, Stanley Chan, John Morgan, Vivek Rathod, Jonathan Huang

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.


Extending the WILDS Benchmark for Unsupervised Adaptation

1 code implementation ICLR 2022 Shiori Sagawa, Pang Wei Koh, Tony Lee, Irena Gao, Sang Michael Xie, Kendrick Shen, Ananya Kumar, Weihua Hu, Michihiro Yasunaga, Henrik Marklund, Sara Beery, Etienne David, Ian Stavness, Wei Guo, Jure Leskovec, Kate Saenko, Tatsunori Hashimoto, Sergey Levine, Chelsea Finn, Percy Liang

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.

Domain Adaptation for Rare Classes Augmented with Synthetic Samples

no code implementations23 Oct 2021 Tuhin Das, Robert-Jan Bruintjes, Attila Lengyel, Jan van Gemert, Sara Beery

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.

Domain Adaptation

ElephantBook: A Semi-Automated Human-in-the-Loop System for Elephant Re-Identification

no code implementations29 Jun 2021 Peter Kulits, Jake Wall, Anka Bedetti, Michelle Henley, Sara Beery

African elephants are vital to their ecosystems, but their populations are threatened by a rise in human-elephant conflict and poaching.

Image-to-Image Translation of Synthetic Samples for Rare Classes

no code implementations23 Jun 2021 Edoardo Lanzini, Sara Beery

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.

Classification Image-to-Image Translation +1

Can poachers find animals from public camera trap images?

no code implementations21 Jun 2021 Sara Beery, Elizabeth Bondi

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.

The iWildCam 2021 Competition Dataset

no code implementations7 May 2021 Sara Beery, Arushi Agarwal, Elijah Cole, Vighnesh Birodkar

The challenge is to classify species and count individual animals across sequences in the test cameras.

object-detection Object Detection

The iWildCam 2020 Competition Dataset

no code implementations21 Apr 2020 Sara Beery, Elijah Cole, Arvi Gjoka

Can we leverage data from other modalities, such as citizen science data and remote sensing data?

Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection

3 code implementations CVPR 2020 Sara Beery, Guanhang Wu, Vivek Rathod, Ronny Votel, Jonathan Huang

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.

object-detection Video Object Detection +1

A deep active learning system for species identification and counting in camera trap images

1 code implementation22 Oct 2019 Mohammad Sadegh Norouzzadeh, Dan Morris, Sara Beery, Neel Joshi, Nebojsa Jojic, Jeff Clune

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.

Active Learning Decision Making +1

The iWildCam 2019 Challenge Dataset

no code implementations15 Jul 2019 Sara Beery, Dan Morris, Pietro Perona

We use the Caltech Camera Traps dataset, collected from the American Southwest, as training data.

Transfer Learning

Efficient Pipeline for Camera Trap Image Review

1 code implementation15 Jul 2019 Sara Beery, Dan Morris, Siyu Yang

Biologists all over the world use camera traps to monitor biodiversity and wildlife population density.

General Classification

Synthetic Examples Improve Generalization for Rare Classes

no code implementations11 Apr 2019 Sara Beery, Yang Liu, Dan Morris, Jim Piavis, Ashish Kapoor, Markus Meister, Neel Joshi, Pietro Perona

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.

Few-Shot Learning Self-Driving Cars

The iWildCam 2018 Challenge Dataset

no code implementations11 Apr 2019 Sara Beery, Grant van Horn, Oisin Mac Aodha, Pietro Perona

Camera traps are a valuable tool for studying biodiversity, but research using this data is limited by the speed of human annotation.

Recognition in Terra Incognita

2 code implementations ECCV 2018 Sara Beery, Grant van Horn, Pietro Perona

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.

Classification General Classification

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