发表机构
University of Bristol; School of Computer Science（布里斯托大学; 计算机科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对NACTI数据集长尾类不平衡问题，基于PyTorch Wildlife模型评估LTR方法，优化配置取得高准确率，在多测试集评估中展现强泛化能力，虽当前LTR优化有局限，但相关数据和代码已发布。
AI 中文摘要
与大多数自然世界的“野外”数据集一样，北美相机陷阱图像（NACTI）数据集存在长尾类不平衡，最大类覆盖其370万张图像的50%以上。基于PyTorch Wildlife模型，系统评估长尾识别（LTR）方法以基准测试物种识别性能，包括专门的损失函数和对LTR敏感的正则化。优化配置在NACTI测试分割上实现了99.40%的Top-1准确率，显著优于标准基线和先前报告的最佳性能。为评估域转移下的鲁棒性，在三个独立的减少偏差测试集上扩展评估。结果表明LTR增强模型比标准交叉熵方法具有更强的泛化能力，但当前LTR优化不能完全克服表示瓶颈。所有数据集分割、关键代码和网络权重均已发布。
英文摘要
As with most ``in the wild'' collections of the natural world, the North America Camera Trap Images (NACTI) dataset exhibits long-tailed class imbalance, with the largest class covering over 50% of its 3.7M images. Building on the PyTorch Wildlife model, we systematically evaluate Long-Tail Recognition (LTR) methodologies to benchmark species recognition performance, including specialised loss functions and LTR-sensitive regularisation. Our optimised configuration achieves state-of-the-art 99.40% Top-1 accuracy on the NACTI test split, significantly outperforming standard baselines and previously reported top performances. To assess robustness under domain shifts (e.g., night-time captures, occlusion, motion-blur), we extend our evaluation across three independent reduced-bias test sets (including ENA-Detection, Caltech Camera Traps and Missouri Camera Traps). Across these out-of-distribution (OOD) evaluations, our LTR-enhanced model consistently demonstrates substantially stronger generalisation capabilities compared to standard cross-entropy approaches. However, qualitative and quantitative analyses underline that current LTR optimisations cannot fully overcome representational bottlenecks, resulting in catastrophic predictive breakdown for rare `Tail' classes under severe domain shift. For maximum reproducibility, all dataset splits, key code, and network weights are published with this paper at https://github.com/ZehuaLiuY/Species-Classification.
