What are the challenges in zero-shot and few-shot learning?

Train a model to identify the type of content in images, for example for a content moderation use case, use this template to perform image classification with ... Extracting information using image classification · Intro to image segmentation, classification, and machine learning · Calculate impervious surfaces from ... In this paper we investigate image classification with computational resource limits at test time. Two such settings are: 1. anytime classification. We investigate and develop methods to extract and combine low-level features that represent the emotional content of an image, and use these for image emotion ... Image classification is a fundamental task in computer vision and machine learning. It involves categorizing and labeling images based on specific rules applied ... ... image logging platform that combines image classification and calorie estimation to support dietary management. This thesis consists of four studies ... What is Image Classification? Image classification uses machine learning to learn from labeled training data. It categorizes images into predefined listings ... Apr 4, 2025 ... Hybrid text image classifier. HTIC models—which mix an optimal CNN and VGG16 for image data with ROBERTA and MySQL for text data—achieve ... Jul 22, 2024 ... Data imbalance and bias are significant hurdles in image classification. Poor or biased training data can severely impact model performance. Nov 23, 2022 ... Image classification is a computer vision task where label(s) are assigned to an entire image. The label should be representative of the main ...