论文标题
升华：用于评估语言增强视觉模型的基准和工具包
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models
论文作者
论文摘要
从自然语言监督中学习视觉表示，最近在许多开创性作品中表现出了巨大的希望。通常，这些具有语言的视觉模型表现出对各种数据集和任务的强大可传递性。但是，由于缺乏易于使用的评估工具包和公共基准，评估这些模型的可转让性仍然很具有挑战性。为了解决这个问题，我们构建了高级版（评估语言的视觉任务级传输），这是评估（预训练）语言增强视觉模型的第一个基准和工具包。升华由三个组成部分组成。 （i）数据集。作为下游评估套件，它由20个图像分类数据集和35个对象检测数据集组成，每个数据集都用外部知识增强。 （ii）工具包。开发了自动高参数调谐工具包，以促进下游任务的模型评估。 （iii）指标。多种评估指标用于测量样品效率（零射和少量）和参数效率（线性探测和完整模型微调）。 Levater是野外计算机视觉的平台（CVINW），并在https://computer-vision-n-wild.github.io/elevater/上公开发行。
Learning visual representations from natural language supervision has recently shown great promise in a number of pioneering works. In general, these language-augmented visual models demonstrate strong transferability to a variety of datasets and tasks. However, it remains challenging to evaluate the transferablity of these models due to the lack of easy-to-use evaluation toolkits and public benchmarks. To tackle this, we build ELEVATER (Evaluation of Language-augmented Visual Task-level Transfer), the first benchmark and toolkit for evaluating(pre-trained) language-augmented visual models. ELEVATER is composed of three components. (i) Datasets. As downstream evaluation suites, it consists of 20 image classification datasets and 35 object detection datasets, each of which is augmented with external knowledge. (ii) Toolkit. An automatic hyper-parameter tuning toolkit is developed to facilitate model evaluation on downstream tasks. (iii) Metrics. A variety of evaluation metrics are used to measure sample-efficiency (zero-shot and few-shot) and parameter-efficiency (linear probing and full model fine-tuning). ELEVATER is a platform for Computer Vision in the Wild (CVinW), and is publicly released at at https://computer-vision-in-the-wild.github.io/ELEVATER/
