DJAN: Deep Joint Adaptation Network for Wildlife Image Recognition

SIMPLE SUMMARY: Identifying wildlife species is crucial in various wildlife monitoring tasks. In this paper, a wildlife image recognition approach is implemented based on deep learning with a joint adaptation network. This paper presents a joint adversarial learning approach and a cross-domain local and global representation learning approach. Utilizing the two approaches, a Deep Joint Adaptation Network model for wildlife image recognition is designed. The proposed model can yield high accuracy in wildlife image recognition and is beneficial to improve the generalization ability in complex environments. Our research is of the utmost importance for wildlife recognition and wildlife biodiversity monitoring. ABSTRACT: 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.