Modeling Semantic Correlation and Hierarchy for Real-World Wildlife Recognition
- Authors
- Kim, Dong-Jin; Miao, Zhongqi; Guo, Yunhui; Yu, Stella X.
- Issue Date
- Mar-2023
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Wildlife; Training; Neural networks; Semantics; Correlation; Data models; Birds; Wildlife recognition; active learning; class imbalance
- Citation
- IEEE SIGNAL PROCESSING LETTERS, v.30, pp.259 - 263
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE SIGNAL PROCESSING LETTERS
- Volume
- 30
- Start Page
- 259
- End Page
- 263
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/185467
- DOI
- 10.1109/LSP.2023.3257725
- ISSN
- 1070-9908
- Abstract
- We explore the challenges of human-in-the-loop frameworks to label wildlife recognition datasets with a neural network. In wildlife imagery, the main challenges for a model to assist human annotation are two-fold: (1) the training dataset is usually imbalanced, which makes the model's suggestion biased, and (2) there are complex taxonomies in the classes. We establish a simple and efficient baseline, including the debiasing loss function and the hyperbolic network architecture, to address these issues. Moreover, we propose leveraging the semantic correlation to train the model more effectively by adding a co-occurrence layer to our model during training. We demonstrate the efficacy of our method in both a real-world wildlife areal survey recognition dataset and the public image classification dataset, CIFAR100-LT, CIFAR10-LT, and iNaturalist.
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