Zero-Shot Learning

Zero-Shot Learning (ZSL) is a machine learning paradigm where a model can recognize classes that were not present in its training data by transferring knowledge from seen to unseen classes through shared semantic representations. Instead of learning directly from labeled examples of every class, ZSL models learn a mapping between visual features and semantic descriptors such as text descriptions, attribute vectors, or class embeddings. At inference time, the model compares visual features against semantic descriptions of novel classes to make predictions without any task-specific training examples. Zero-Shot Classification in Geospatial ContextsVision-language models like CLIP have enabled zero-shot classification of satellite imagerySatellite ImagerySatellite imagery consists of photographs and data captured by Earth observation satellites orbiting the planet. Thes... using natural language descriptions. An analyst can classify land cover types simply by providing text prompts like "dense urban area" or "irrigated agricultural field" without training a custom model. This capability is transformative for rapid geospatial assessment when there is no time to collect and annotate training data, such as in disaster response scenarios. Zero-shot object detectionObject DetectionObject Detection is a computer vision technique that identifies and localizes specific objects within images or video... can identify features described in text without prior examples, enabling flexible and adaptive geospatial analysisGeospatial AnalysisGeospatial analysis applies statistical methods and specialized software to interpret spatial data, uncovering patter... workflows. Geographic scene understanding benefits from zero-shot models that can interpret unfamiliar landscapes through compositional understanding of their elements. Capabilities and Current LimitationsZero-shot learning dramatically reduces deployment barriers by eliminating the need for task-specific labeled data. It enables truly flexible analysis systems that can be directed through natural language rather than requiring model retraining for each new task. However, zero-shot performance typically falls short of fully supervised models, particularly for specialized geospatial categories that are poorly represented in the pretraining data. Generalized zero-shot learning, which must handle both seen and unseen classes simultaneously, remains challenging. Performance improves significantly when even a few labeled examples are available, bridging into few-shot learningFew-Shot LearningFew-Shot Learning enables machine learning models to recognize new categories from only a handful of labeled examples....