Automatic recognition of endangered animal behavior is crucial for biodiversity conservation and improving animal welfare, yet traditional manual observation remains inefficient and invasive. This work contributes directly to sustainable wildlife management by enabling non-invasive, scalable, and efficient monitoring, which supports long-term ecological balance and aligns with several United Nations Sustainable Development Goals (SDGs), particularly SDG 15 (Life on Land) and SDG 12 (Responsible Consumption and Production). The current deep learning approaches often struggle with the scarcity of behavioral data and complex environments, leading to poor model generalization. To address these challenges, this study focuses on endangered animal behavior monitoring and proposes a multimodal learning framework termed ABCLIP. This model leverages multimodal contrastive learning between video-and-text pairs, utilizing natural language supervision to enhance representation ability. The framework integrates pre-training, prompt learning, and fine-tuning to optimize performance specifically for small-scale animal behavior datasets, with a focus on the specific social and ecological behaviors of giant pandas. The experimental results demonstrate that ABCLIP achieves remarkable accuracy and robustness in recognizing endangered animal behaviors, attaining Top-1 and Top-5 accuracy of 82.50% and 99.25%, respectively, on the LoTE-Animal dataset, which outperforms strong baseline methods such as SlowFast (78.54%/97.55%). Furthermore, in zero-shot recognition scenarios for unseen behaviors, ABCLIP achieves an accuracy of 58.00%. This study highlights the potential of multimodal contrastive learning in wildlife monitoring and provides efficient technical support for precise protection measures and scientific management of endangered species.
Liu et al. (Thu,) studied this question.