Image Level Classification

Image-level classification aims to assign a single label to an entire image, representing its overall content. Current research focuses on improving accuracy and efficiency through techniques like data fusion (combining information from multiple sources), leveraging contextual information (e.g., incorporating scene understanding from vision-language models), and employing novel architectures such as vision transformers and bio-inspired classifiers. These advancements are driving progress in diverse applications, including medical image analysis (e.g., breast cancer diagnosis), remote sensing (e.g., climate zone classification), and robotics (e.g., visual place recognition), where efficient and accurate image understanding is crucial.