Image Classification

Image classification studies how to assign one or more labels to an image, supporting applications from species identification and medical diagnosis to document processing and remote sensing. Common approaches include convolutional neural networks, vision transformers, and pretrained vision-language models, trained with supervised, self-supervised, transfer, or few-shot learning. Researchers also develop data augmentation, hierarchical prediction, uncertainty estimation, and efficient models for settings such as ImageNet, corruption benchmarks, and specialized domain datasets. A central challenge is that strong accuracy on standard test sets may not transfer under distribution shifts or reveal reliance on spurious cues, while improving robustness, calibration, and efficiency can involve trade-offs.