WildIng: 一种用于地理域转移的野生动物图像不变表示模型
WildIng: A Wildlife Image Invariant Representation Model for Geographical Domain Shift
AI总结：
WildIng通过整合文本描述与图像特征，提升野生动物在不同地理区域的识别准确率，有效解决地理域转移问题。
摘要：
野生动物监测对于研究生物多样性丧失和气候变化至关重要。相机陷阱图像提供了一种非侵入性方法，用于分析动物种群并识别随时间变化的生态模式。然而，手动分析耗时且资源密集。深度学习，特别是基础模型，已被应用于自动化野生动物识别，在测试数据与训练数据来自同一地理区域时表现出强劲性能。然而，尽管这些模型有潜力，它们在新地理区域的泛化能力有限，导致性能显著下降。例如，训练一个高级视觉-语言模型，如CLIP配合适配器，在非洲数据集上获得84.77%的准确率。然而，当模型在美洲数据集上测试时，性能急剧下降至16.17%。这一限制部分源于现有模型主要依赖图像表示，使其对地理数据分布转移敏感，如背景变化、光照和环境条件的变化。为此，我们引入WildIng，一种用于地理域转移的野生动物图像不变表示模型。WildIng整合文本描述与图像特征，创建更稳健的地理域转移表示。通过利用文本描述，我们的方法捕捉一致的语义信息，如物种外观的详细描述，从而在不同地理区域之间提高泛化能力。实验显示，WildIng在地理域转移条件下将基础模型如BioCLIP的准确性提高了30%。我们评估了WildIng在两个来自不同地区的数据集上，即美洲和非洲。代码和模型在https://github.com/Julian075/CATALOG/tree/WildIng上公开。
英文摘要：
Wildlife monitoring is crucial for studying biodiversity loss and climate change. Camera trap images provide a non-intrusive method for analyzing animal populations and identifying ecological patterns over time. However, manual analysis is time-consuming and resource-intensive. Deep learning, particularly foundation models, has been applied to automate wildlife identification, achieving strong performance when tested on data from the same geographical locations as their training sets. Yet, despite their promise, these models struggle to generalize to new geographical areas, leading to significant performance drops. For example, training an advanced vision-language model, such as CLIP with an adapter, on an African dataset achieves an accuracy of 84.77%. However, this performance drops significantly to 16.17% when the model is tested on an American dataset. This limitation partly arises because existing models rely predominantly on image-based representations, making them sensitive to geographical data distribution shifts, such as variation in background, lighting, and environmental conditions. To address this, we introduce WildIng, a Wildlife image Invariant representation model for geographical domain shift. WildIng integrates text descriptions with image features, creating a more robust representation to geographical domain shifts. By leveraging textual descriptions, our approach captures consistent semantic information, such as detailed descriptions of the appearance of the species, improving generalization across different geographical locations. Experiments show that WildIng enhances the accuracy of foundation models such as BioCLIP by 30% under geographical domain shift conditions. We evaluate WildIng on two datasets collected from different regions, namely America and Africa. The code and models are publicly available at https://github.com/Julian075/CATALOG/tree/WildIng.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
