Fine Grained Image Retrieval

Fine-grained image retrieval (FGIR) focuses on retrieving images of highly similar objects, such as different bird species or car models, which poses significant challenges due to subtle visual differences. Current research emphasizes developing robust and efficient models, often employing deep learning architectures like visual transformers and hashing techniques, to improve retrieval accuracy while managing computational complexity. These advancements are crucial for applications ranging from large-scale media search to specialized identification tasks, driving progress in both computer vision and information retrieval. The field is also exploring innovative approaches like incorporating textual descriptions and sketches to enhance retrieval precision and address the limitations of relying solely on visual features.