# Automatic Bird Species Recognition from Images with Feature Enhancement and Contrastive Learning
###### Abstract  
Accurate bird species recognition is crucial for ecological conservation, wildlife monitoring, and biological research, yet it poses significant challenges due to the high variability within species and the subtle similarities between different species. This paper introduces an automatic bird species recognition method from images that leverages feature enhancement and contrast learning to address these challenges. Our method incorporates a multi-scale feature fusion module to comprehensively capture information from bird images across diverse scales and perspectives. Additionally, an attention feature enhancement module is integrated to address noise and occlusion within images, thus enhancing the model's robustness. Furthermore, employing a siamese network architecture allows effective learning of common features within instances of the same class and distinctions between different bird species. Evaluated on the CUB200-2011 dataset, our proposed method achieves state-of-the-art performance, surpassing existing methods with an accuracy of 91.3% and F1 score of 90.6%. Moreover, our approach showcases a notable advantage in scenarios with limited training data. When utilizing only 5% of the training data, our model still achieves a recognition accuracy of 65.2%, which is significantly higher than existing methods under similar data constraints. Notably, our model exhibits faster execution times compared to existing methods, rendering it suitable for real-time applications. bird species recognition; feature enhancement; contrastive learning; multi-scale feature +
Footnote †: journal: Applied  
0000-0001-999-7000-0000-0000]F. [PERSON] 00000-0001-9999-7000]X. [PERSON] 0000-0001-9999-7000]F. [PERSON] 0000-0002-0002-3333-0000]C. [PERSON] 0000-0002-3333-0000]C. [PERSON] 0000-0002-3333-0000  
Deep learning revolutionizes bird species recognition by enabling machines to automatically extract and learn informative features from images, surpassing the limitations of traditional methods [12; 13]. Within the realm of deep learning, two primary approaches have emerged: strongly supervised and weakly supervised classification [14; 15]. Strongly supervised methods, while achieving high accuracy, demand a significant investment of time and effort in meticulously annotating training data [16; 17]. These annotations typically encompass not only the bird species label but also detailed information such as bounding boxes around the bird and annotations for various body parts [18]. This labor-intensive process can become a bottleneck, especially when dealing with large datasets or rare bird species [19]. In contrast, weakly supervised methods rely solely on image-level category labels, significantly reducing annotation effort [20; 21]. As a result, weakly supervised methods have become a major focus of research in fine-grained bird species recognition. [PERSON] et al. [22] modeled the dynamic way humans observe objects to improve fine-grained classification performance. [PERSON] et al. [23] introduced a diverse visual attention network that reduces the reliance on strongly supervised information during network training. [PERSON] et al. [24] utilized an attention mechanism to predict separate features for both the bird and its individual parts before combining them for classification. While image-level labels simplify data collection, their limited information necessitates both large labeled datasets and complex models for high accuracy in weakly supervised bird recognition [21]. This leads to three challenges: High computing power is a barrier for real-time and resource-constrained applications due to the complexity of models required to learn from simple labels; Large, labeled datasets for weakly supervised bird recognition are expensive and time-consuming to create, especially for complex images [19]; Visually similar birds, especially rare species, challenge weakly supervised recognition due to limitations of image-level labels.