使用众包昆虫图像进行细粒度分类的计算机视觉算法性能评估
Performance of computer vision algorithms for fine-grained classification using crowdsourced insect images
AI总结：
本文系统评估了CNN、ViT和LBVT三类共九种计算机视觉算法在昆虫细粒度分类中的性能、嵌入质量、计算成本和梯度活动，发现ViT推理最快、LBVT性能最佳、CNN提供折中。
摘要：
通过细粒度分类，我们识别独特特征以区分同一超类中的不同类别。我们专注于昆虫纲的物种识别，因为昆虫对生物多样性监测至关重要，并且是许多生态系统的基础。借助公民科学活动，人们在野外收集了数十亿张图像。一旦这些图像被标注，专家就可以利用它们绘制分布图。然而，标注过程非常耗时，这正是计算机视觉发挥作用的地方。计算机视觉领域提供了广泛的算法，每种算法各有优缺点；我们如何确定哪种算法与我们的应用需求一致？为了回答这个问题，我们对九种算法进行了全面而详细的评估，这些算法涵盖深度卷积网络（CNN）、视觉Transformer（ViT）和基于局部性的视觉Transformer（LBVT），并从四个不同方面进行比较：分类性能、嵌入质量、计算成本和梯度活动。我们提供了该领域此前尚未获得的见解，证明了这些算法在多大程度上能够解决昆虫纲的细粒度任务。我们发现，ViT在推理速度和计算成本方面表现最佳，而LBVT在性能和嵌入质量方面优于其他算法；CNN则在各项指标之间提供了一种折中方案。
英文摘要：
With fine-grained classification, we identify unique characteristics to distinguish among classes of the same super-class. We are focusing on species recognition in Insecta, as they are critical for biodiversity monitoring and at the base of many ecosystems. With citizen science campaigns, billions of images are collected in the wild. Once these are labelled, experts can use them to create distribution maps. However, the labelling process is time-consuming, which is where computer vision comes in. The field of computer vision offers a wide range of algorithms, each with its strengths and weaknesses; how do we identify the algorithm that is in line with our application? To answer this question, we provide a full and detailed evaluation of nine algorithms among deep convolutional networks (CNN), vision transformers (ViT), and locality-based vision transformers (LBVT) on 4 different aspects: classification performance, embedding quality, computational cost, and gradient activity. We offer insights that we haven't yet had in this domain proving to which extent these algorithms solve the fine-grained tasks in Insecta. We found that the ViT performs the best on inference speed and computational cost while the LBVT outperforms the others on performance and embedding quality; the CNN provide a trade-off among the metrics.
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。
