Iterative Human and Automated Identification of Wildlife Images
@article{Miao2021IterativeHA, title={Iterative Human and Automated Identification of Wildlife Images}, author={Zhongqi Miao and Ziwei Liu and Kaitlyn M. Gaynor and Meredith S. Palmer and Stella X. Yu and Wayne M. Getz}, journal={Nat. Mach. Intell.}, year={2021}, volume={3}, pages={885-895} }
1Department of Environmental Science, Policy, and Management, University of California, Berkeley, Berkeley, CA, USA. 2International Computer Science Institute, University of California, Berkeley, Berkeley, CA, USA. 3School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore. 4National Center for Ecological Analysis and Synthesis, University of California, Santa Barbara, Santa Barbara, CA, USA. 5Department of Ecology and Evolutionary Biology, Princeton…
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References
SHOWING 1-10 OF 60 REFERENCES
ImageNet: A large-scale hierarchical image database
- Computer Science2009 IEEE Conference on Computer Vision and Pattern Recognition
- 2009
A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Snapshot Safari: a large-scale collaborative to monitor Africa’s remarkable biodiversity
- Environmental Science
- 2021
Volume 117| Number 1/2 January/February 2021 Commentary https://doi.org/10.17159/sajs.2021/8134 Snapshot Safari: A large-scale collaborative to monitor Africa’s remarkable biodiversity AUTHORS: Lain…
Robust ecological analysis of camera trap data labelled by a machine learning model
- BiologyMethods in Ecology and Evolution
- 2021
It is concluded that, with adequate testing and evaluation in an ecological context, a machine learning model can generate labels for direct use in ecological analyses without the need for manual validation.
Postwar wildlife recovery in an African savanna: evaluating patterns and drivers of species occupancy and richness
- Environmental ScienceAnimal Conservation
- 2020
As local and global disturbances reshape African savannas, an understanding of how animal communities recover and respond to landscape features can inform conservation and restoration. Here, we…
Energy-based Out-of-distribution Detection
- Computer ScienceNeurIPS
- 2020
This work proposes a unified framework for OOD detection that uses an energy score, and shows that energy scores better distinguish in- and out-of-distribution samples than the traditional approach using the softmax scores.
What Should Not Be Contrastive in Contrastive Learning
- Computer ScienceICLR
- 2021
This work introduces a contrastive learning framework which does not require prior knowledge of specific, task-dependent invariances, and learns to capture varying and invariant factors for visual representations by constructing separate embedding spaces, each of which is invariant to all but one augmentation.
Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2
- Environmental Science, Computer SciencebioRxiv
- 2020
By including many species from several locations, the species model is potentially applicable to many camera trap studies in North America and the empty-animal model can facilitate removal of images without animals globally.
"How many images do I need?" Understanding how sample size per class affects deep learning model performance metrics for balanced designs in autonomous wildlife monitoring
- Environmental Science, Computer ScienceEcol. Informatics
- 2020
Three critical factors affecting automated image species recognition performance for camera traps
- Environmental Science, Computer ScienceEcology and evolution
- 2020
The capabilities of deep learning systems trained on camera trap images using modestly sized training data are tested, performance when considering unseen background locations is compared, and the gradient of lower bound performance is quantified to provide a guideline of data requirements in correspondence to performance expectations.
A Simple Framework for Contrastive Learning of Visual Representations
- Computer ScienceICML
- 2020
It is shown that composition of data augmentations plays a critical role in defining effective predictive tasks, and introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning.