A framework for assessing variations in ecological networks to support wildlife conservation and management

Ji, Yunrui; Wei, Xuelei; Li, Diqiang; Zhao, Jiacang; Li, Jiahua; Feng, Siqin

doi:10.1016/j.ecolind.2023.110936

Cited by 4 publications

(1 citation statement)

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“…Wildlife identification is a crucial component of monitoring and conserving biodiversity in the wild. Wildlife monitoring serves as the foundation for wildlife conservation management [1] and plays an importance role in this regard. Through collecting and analyzing data on various aspects of wildlife, such as habitat utilization, migration patterns, quantity distribution, and behavioral activities, monitoring provides valuable information [2] and insights for conservation organizations and government agencies.…”

Section: Introductionmentioning

confidence: 99%

DJAN: Deep Joint Adaptation Network for Wildlife Image Recognition

Zhang,

Zhang

2023

Animals

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Wildlife recognition is of utmost importance for monitoring and preserving biodiversity. In recent years, deep-learning-based methods for wildlife image recognition have exhibited remarkable performance on specific datasets and are becoming a mainstream research direction. However, wildlife image recognition tasks face the challenge of weak generalization in open environments. In this paper, a Deep Joint Adaptation Network (DJAN) for wildlife image recognition is proposed to deal with the above issue by taking a transfer learning paradigm into consideration. To alleviate the distribution discrepancy between the known dataset and the target task dataset while enhancing the transferability of the model’s generated features, we introduce a correlation alignment constraint and a strategy of conditional adversarial training, which enhance the capability of individual domain adaptation modules. In addition, a transformer unit is utilized to capture the long-range relationships between the local and global feature representations, which facilitates better understanding of the overall structure and relationships within the image. The proposed approach is evaluated on a wildlife dataset; a series of experimental results testify that the DJAN model yields state-of-the-art results, and, compared to the best results obtained by the baseline methods, the average accuracy of identifying the eleven wildlife species improves by 3.6 percentage points.

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Section: Introductionmentioning

confidence: 99%