ZeroSearch: Local Image Search from Text with Zero Shot Learning

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

A CNN-LSTM Combination Network for Cataract Detection using Eye Fundus Images

According to multiple authoritative authorities, including the World Health Organization, vision-related impairments and disorders are becoming a significant issue. According to a recent report, one of the leading causes of irreversible blindness in persons over the age of 50 is delayed cataract treatment. A cataract is a cloudy spot in the eye's lens that causes visual loss. Cataracts often develop slowly and consequently result in difficulty in driving, reading, and even recognizing faces. This necessitates the development of rapid and dependable diagnosis and treatment solutions for ocular illnesses. Previously, such visual illness diagnosis were done manually, which was time-consuming and prone to human mistake. However, as technology advances, automated, computer-based methods that decrease both time and human labor while producing trustworthy results are now accessible. In this study, we developed a CNN-LSTM-based model architecture with the goal of creating a low-cost diagnostic system that can classify normal and cataractous cases of ocular disease from fundus images. The proposed model was trained on the publicly available ODIR dataset, which included fundus images of patients' left and right eyes. The suggested architecture outperformed previous systems with a state-of-the-art 97.53% accuracy.