基于特权池化的细粒度物种识别：通过监督注意力提升样本效率
Fine-grained Species Recognition with Privileged Pooling: Better Sample Efficiency Through Supervised Attention
AI总结：
针对生态应用中动物物种识别因长尾分布和数据集偏差导致样本效率低的问题，提出利用关键点标注作为特权信息监督的特权池化视觉注意力机制，使模型聚焦判别性区域，实验证明其能更高效利用小训练集并提升泛化能力。
摘要：
我们提出了一种监督图像分类方案，利用训练数据关键点标注形式的特权信息，从小型和/或偏差训练集中学习强模型。主要动机是生态应用（如生物多样性建模）中的动物物种识别，由于稀有物种导致的长尾分布，以及相机陷阱中重复场景背景等强数据集偏差，该任务极具挑战。为应对这些挑战，我们提出通过关键点标注监督的视觉注意力机制，以突出重要对象部位。这种特权信息以新颖的特权池化操作实现，仅在训练时需要，帮助模型聚焦于具判别性的区域。在三个不同动物物种数据集的实验中，我们展示了带特权池化的深度网络能更高效利用小训练集并实现更好泛化。
英文摘要：
We propose a scheme for supervised image classification that uses privileged information, in the form of keypoint annotations for the training data, to learn strong models from small and/or biased training sets. Our main motivation is the recognition of animal species for ecological applications such as biodiversity modelling, which is challenging because of long-tailed species distributions due to rare species, and strong dataset biases such as repetitive scene background in camera traps. To counteract these challenges, we propose a visual attention mechanism that is supervised via keypoint annotations that highlight important object parts. This privileged information, implemented as a novel privileged pooling operation, is only required during training and helps the model to focus on regions that are discriminative. In experiments with three different animal species datasets, we show that deep networks with privileged pooling can use small training sets more efficiently and generalize better.
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。
