对比深度模型与植物专家，发现顶尖系统已逼近人类最高水平。
Overview of ExpertLifeCLEF 2018: how far automated identification systems are from the best experts?
- 用9位植物专家与19个深度学习模型对比识别效果
- 最佳模型准确率接近最资深专家水平
- 为自动化系统与人类专家差距提供量化基准
近年来，得益于深度学习的发展，动植物自动识别能力显著提升。然而，当前关键问题是：这些自动化系统距离人类顶尖专家还有多远？事实上，即使最资深的专家在验证生物体视觉或音频观察时也常存在混淆或分歧，因为单一图像通常无法提供确定物种的完整信息。量化这种不确定性，并与自动化系统表现进行比较，对计算机科学家和自然学家均具重要意义。本文介绍的LifeCLEF 2018 ExpertCLEF挑战旨在实现这一对比。共评估了由4个研究团队开发的19个深度学习系统，与9位法国植物学专家的表现进行对照。主要结果表明，当前最先进的深度学习模型性能已接近最先进的人类专家水平。本文详细介绍了挑战所用资源与评估方法，总结了各参与团队的技术方案，并对主要成果进行了分析。
原文摘要 · Abstract (English)
Automated identification of plants and animals has improved considerably in the last few years, in particular thanks to the recent advances in deep learning. The next big question is how far such automated systems are from the human expertise. Indeed, even the best experts are sometimes confused and/or disagree between each others when validating visual or audio observations of living organism. A picture actually contains only a partial information that is usually not sufficient to determine the right species with certainty. Quantifying this uncertainty and comparing it to the performance of automated systems is of high interest for both computer scientists and expert naturalists. The LifeCLEF 2018 ExpertCLEF challenge presented in this paper was designed to allow this comparison between human experts and automated systems. In total, 19 deep-learning systems implemented by 4 different research teams were evaluated with regard to 9 expert botanists of the French flora. The main outcome of this work is that the performance of state-of-the-art deep learning models is now close to the most advanced human expertise. This paper presents more precisely the resources and assessments of the challenge, summarizes the approaches and systems employed by the participating research groups, and provides an analysis of the main outcomes.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。