论文标题
长尾相机陷阱识别的平衡域专家
Balancing Domain Experts for Long-Tailed Camera-Trap Recognition
论文作者
论文摘要
相机陷阱图像中的标签分布是高度不平衡且长尾巴的，导致神经网络倾向于偏向经常出现的头层。尽管已经探索了长尾学习来解决数据失衡，但很少有研究考虑摄像机陷阱特征，例如多域和多帧设置。在这里，我们提出了一个统一的框架，并介绍了两个数据集以进行长尾相机陷阱识别。我们首先设计域专家，每个专家都学会平衡由数据失衡引起的不完善的决策边界，并相互补充以产生域平衡的决策界限。另外，我们提出了集中在移动对象上的流量一致性损失，期望多框架的类激活图与输入图像的光流图相匹配。此外，引入了两个长尾相机陷阱数据集WCS-LT和DMZ-LT，以验证我们的方法。实验结果显示了我们框架的有效性，并且提出的方法的表现优于先前的隐性域样本方法。
Label distributions in camera-trap images are highly imbalanced and long-tailed, resulting in neural networks tending to be biased towards head-classes that appear frequently. Although long-tail learning has been extremely explored to address data imbalances, few studies have been conducted to consider camera-trap characteristics, such as multi-domain and multi-frame setup. Here, we propose a unified framework and introduce two datasets for long-tailed camera-trap recognition. We first design domain experts, where each expert learns to balance imperfect decision boundaries caused by data imbalances and complement each other to generate domain-balanced decision boundaries. Also, we propose a flow consistency loss to focus on moving objects, expecting class activation maps of multi-frame matches the flow with optical flow maps for input images. Moreover, two long-tailed camera-trap datasets, WCS-LT and DMZ-LT, are introduced to validate our methods. Experimental results show the effectiveness of our framework, and proposed methods outperform previous methods on recessive domain samples.
