Comparing Class-Aware and Pairwise Loss Functions for Deep Metric Learning in Wildlife Re-Identification †
Abstract
:1. Introduction
- highlight the need for increased research into loss functions and neural network architecture specifically for wildlife re-identification;
- improve on the state-of-the-art results in numerous animal re-identification tasks;
- contribute two new benchmark datasets with results;
- provide minor support for a choice of triplet loss with a VGG-11 backbone as an initial architecture and loss.
2. Related Work
2.1. Animal Biometrics and Computer Vision
- Universality: all the individuals in the population must have such a feature;
- Uniqueness: two or more individuals should have a different form of the same feature.
2.2. Classification vs. Similarity Learning
2.3. Similarity Learning
2.4. Sampling Techniques for Pairwise Training
2.4.1. Hard Negative Mining
2.4.2. Semi-Hard Negative Mining
2.5. Challenges in Deep Metric Learning
2.6. Animal Biometrics Using Image Features
2.7. Loss Functions
2.7.1. Pairwise Loss
2.7.2. Class Distribution Based Loss
2.8. Motivation
3. Materials and Methods
3.1. Data
3.2. Neural Network Architectures
3.3. Training
3.4. Metrics Measured
3.5. Embedding Dimension Size
4. Results
5. Discussion
5.1. Class Aware vs. Pairwise Loss
5.2. Backbone Architectures
5.3. Flanks vs. Faces
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
DCNN | Deep Convolutional Neural Network |
P-NCA | Proxy Neighbourhood Component Analysis |
Proxy-NCA | Proxy Neighbourhood Component Analysis |
MAP | Mean Average Precision |
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Short Biography of Authors
| Nkosikhona Dlamini is a student at the University of Witwatersrand studying towards a Master of Science in Computer science. Completed honours degree in Computer science at the University of Pretoria. Currently employed at the Tshwane university of Technology. He spent four years working at the CSIR as a research and development technologist focusing on NLP for South African languages. |
| Terence L. van Zyl holds the Nedbank Research and Innovation Chair at the University of Johannesburg where he is a Professor in the Institute for Intelligent Systems. He is an NRF rated scientist who received his PhD and MSc in Computer Science from the University of Johannesburg for his thesis on agent-based complex adaptive systems. He has over 15 years of experience researching and innovating large scale streaming analytics systems for government and industry. His research interests include data-driven science and engineering, prescriptive analytics, machine learning, meta-heuristic optimisation, complex adaptive systems, high-performance computing, and artificial intelligence. |
Animal | N | I | Split | N | I | |
---|---|---|---|---|---|---|
Lion | 750 | 98 | 7.7 ± 4.0 | Train Test | 594 156 | 79 19 |
Nyala | 1934 | 274 | 7.1 ± 5.1 | Train Test | 1213 729 | 179 95 |
Zebra | 2460 | 45 | 54.7 ± 7.3 | Train Test | 1989 471 | 36 9 |
Chimp | 5078 | 78 | 65.1 ± 17.0 | Train Test | 3908 1170 | 62 16 |
Panda | 6462 | 218 | 29.64 ± 8.0 | Train Test | 5546 916 | 174 44 |
Tiger | 3651 | 182 | 20.1 ± 15.0 | Train Test | 1887 1764 | 107 75 |
MAP@R% | |||
---|---|---|---|
Dataset | D-64 | D-128 | D-512 |
Chimp | 8.4 ± 0 | 9.1 ± 1 | 9.1 ± 0 |
Nyala | 38.0 ± 1 | 38.6 ± 1 | 38.5 ± 1 |
Zebra | 29.8 ± 2 | 29.6 ± 1 | 30.6 ± 2 |
Lion | 48.8 ± 2 | 50.6 ± 1 | 50.5 ± 2 |
Tiger | 21.6 ± 3 | 23.2 ± 2 | 23.0 ± 3 |
Panda | 27.5 ± 1 | 28.4 ± 2 | 28.1 ± 1 |
Top-1/Recall@1 | |||||||
---|---|---|---|---|---|---|---|
Faces | Flanks | ||||||
Architecture | Loss | Lions | Chimps | Pandas | Nyala | Zebra | Tiger |
VGG-11 | Triplet | 66.5 ± 2 | 79.0 ± 1 | 91.2 ± 1 | 68.7 ± 2 | 94.6 ± 0 | 88.9 ± 1 |
P-NCA | 68.2 ± 3 | 78.9 ± 1 | 89.3 ± 2 | 68.4 ± 2 | 93.8 ± 2 | 87.0 ± 1 | |
VGG-19 | Triplet | 70.2 ± 2 | 70.6 ± 0 | 86.3 ± 2 | 72.3 ± 0 | 82.8 ± 1 | 86.3 ± 2 |
P-NCA | 71.3 ± 3 | 66.3 ± 0 | 90.9 ± 0 | 69.2 ± 3 | 82.7 ± 0 | 84.4 ± 1 | |
ResNet-18 | Triplet | 67.8 ± 1 | 79.2 ± 2 | 90.0 ± 0 | 64.9 ± 2 | 94.8 ± 1 | 87.1 ± 1 |
P-NCA | 66.8 ± 3 | 77.9 ± 0 | 90.1 ± 1 | 64.1 ± 0 | 93.6 ± 2 | 84.8 ± 1 | |
ResNet-152 | Triplet | 63.2 ± 2 | 71.2 ± 1 | 87.6 ± 3 | 61.0 ± 3 | 80.7 ± 0 | 76.5 ± 2 |
P-NCA | 61.0 ± 1 | 69.5 ± 1 | 83.4 ± 0 | 59.7 ± 0 | 79.1 ± 3 | 75.5 ± 2 | |
DenseNet-201 | Triplet | 70.1 ± 1 | 79.7 ± 2 | 89.6 ± 1 | 67.1 ± 2 | 89.1 ± 0 | 85.0 ± 1 |
P-NCA | 69.5 ± 3 | 78.2 ± 2 | 90.7 ± 1 | 66.3 ± 1 | 87.5 ± 0 | 85.6 ± 1 | |
Prior Research | - | - | 77.5 ± 0 | 92.1 ± – | 72.1 ± 0 | 72.6 ± 0 | 86.3 ± 0 |
MAP@R | |||||||
---|---|---|---|---|---|---|---|
Faces | Flanks | ||||||
Architecture | Loss | Lions | Chimps | Pandas | Nyala | Zebra | Tiger |
VGG-11 | Triplet | 16.5 ± 2 | 12.9 ± 2 | 32.0 ± 2 | 11.2 ± 0 | 16.8 ± 1 | 22.8 ± 1 |
P-NCA | 17.7 ± 1 | 13.8 ± 3 | 31.8 ± 1 | 11.0 ± 1 | 16.5 ± 0 | 22.9 ± 2 | |
VGG-19 | Triplet | 18.0 ± 2 | 11.7 ± 1 | 25.0 ± 0 | 10.8 ± 1 | 16.7 ± 2 | 21.8 ± 1 |
P-NCA | 17.7 ± 0 | 12.0 ± 2 | 28.7 ± 0 | 9.7 ± 3 | 16.4 ± 3 | 20.0 ± 1 | |
ResNet-18 | Triplet | 18.5 ± 0 | 11.2 ± 2 | 26.3 ± 1 | 9.9 ± 2 | 19.0 ± 0 | 24.6 ± 4 |
P-NCA | 19.0 ± 1 | 11.5 ± 1 | 24.9 ± 0 | 9.5 ± 1 | 18.2 ± 1 | 21.7 ± 2 | |
ResNet-152 | Triplet | 17.3 ± 2 | 10.1 ± 0 | 26.9 ± 1 | 8.2 ± 0 | 12.1 ± 3 | 12.5 ± 3 |
P-NCA | 17.1 ± 0 | 9.4 ± 3 | 20.3 ± 1 | 9.0 ± 2 | 11.9 ± 2 | 11.0 ± 1 | |
DenseNet-201 | Triplet | 20.8 ± 1 | 9.9 ± 2 | 31.1 ± 1 | 11.0 ± 2 | 15.9 ± 2 | 22.3 ± 1 |
P-NCA | 20.2 ± 2 | 11.6 ± 3 | 28.4 ± 2 | 10.4 ± 1 | 16.0 ± 1 | 23.2 ± 3 |
MAP@R | ||||||
---|---|---|---|---|---|---|
Faces | Flanks | |||||
Architecture | Lions | Chimps | Panda | Nyala | Zebra | Tiger |
VGG-11 | 17.7 ± 1 | 13.8 ± 3 | 32.0 ± 2 | 11.2 ± 0 | 16.7 ± 2 | 22.8 ± 1 |
ResNet-18 | 19.0 ± 1 | 11.2 ± 2 | 26.3 ± 1 | 9.9 ± 2 | 19.0 ± 0 | 24.6 ± 4 |
DenseNet-201 | 20.8 ± 1 | 11.6 ± 3 | 31.1 ± 1 | 11.0 ± 2 | 16.0 ± 1 | 23.2 ± 3 |
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Dlamini, N.; van Zyl, T.L. Comparing Class-Aware and Pairwise Loss Functions for Deep Metric Learning in Wildlife Re-Identification. Sensors 2021, 21, 6109. https://doi.org/10.3390/s21186109
Dlamini N, van Zyl TL. Comparing Class-Aware and Pairwise Loss Functions for Deep Metric Learning in Wildlife Re-Identification. Sensors. 2021; 21(18):6109. https://doi.org/10.3390/s21186109
Chicago/Turabian StyleDlamini, Nkosikhona, and Terence L. van Zyl. 2021. "Comparing Class-Aware and Pairwise Loss Functions for Deep Metric Learning in Wildlife Re-Identification" Sensors 21, no. 18: 6109. https://doi.org/10.3390/s21186109