A progressive deep learning framework for fine-grained primate behavior recognition

Abstract

Fine-grained primate behavior recognition is crucial for conservation efforts and longitudinal research, but a lack of suitable datasets and complex network structures has hindered its progress. To address these challenges, we propose a novel approach that employs a region-focused deep convolutional neural network and a labeled fine-grained recognition dataset. Our model leverages a progressive attention training strategy that emphasizes discriminative region attention and encourages complementarity with multiple leading levels. Additionally, we incorporate a region-focused image generator that identifies informative local regions and provides a priori knowledge for the subsequent layer. Our experiments demonstrate that our model surpasses state-of-the-art performance on fine-grained primate behavior recognition benchmark datasets, even outperforming some models by leveraging auxiliary information. Our approach produces the automated fine-grained action recognition of primate behavior, making it a significant step for exploiting large datasets in ethology and conservation. This approach can be readily applied to a more comprehensive range of species and scenarios.