The problem of organizing and finding images in a user's directory has become increasingly challenging due to the rapid growth in the number of images captured on personal devices. This paper presents a solution that utilizes zero shot learning to create image queries with only user provided text descriptions. The paper's primary contribution is the development of an algorithm that utilizes pre-trained models to extract features from images. The algorithm uses OWL to check for the presence of bounding boxes and sorts images based on cosine similarity scores. The algorithm's output is a list of images sorted in descending order of similarity, helping users to locate specific images more efficiently. The paper's experiments were conducted using a custom dataset to simulate a user's image directory and evaluated the accuracy, inference time, and size of the models. The results showed that the VGG model achieved the highest accuracy, while the Resnet50 and InceptionV3 models had the lowest inference time and size. The papers proposed algorithm provides an effective and efficient solution for organizing and finding images in a users local directory. The algorithm's performance and flexibility make it suitable for various applications, including personal image organization and search engines. Code and dataset for zero-search are available at: https://github.com/NainaniJatinZ/zero-search

本文提出了一种利用零-shot学习技术来进行图像检索的解决方案，使用预训练模型从图像中提取特征，并利用OWL检查边界框的出现，通过余弦相似分数对图像进行排序。该算法的性能和灵活性使其适用于各种应用，包括个人图像组织和搜索引擎。

ZeroSearch: 使用零样本学习从文本中进行本地图像搜索