Fine-grained object recognition with Gnostic Fields

Much object recognition research is concerned with basic-level classification, in which objects differ greatly in visual shape and appearance, e.g., desk vs duck. In contrast, fine-grained classification involves recognizing objects at a subordinate level, e.g., Wood duck vs Mallard duck. At the basic-level objects tend to differ greatly in shape and appearance, but these differences are usually much more subtle at the subordinate level, making fine-grained classification especially challenging. In this work, we show that Gnostic Fields, a brain-inspired model of object categorization, excel at fine-grained recognition. Gnostic Fields exceeded state-of-the-art methods on benchmark bird classification and dog breed recognition datasets, achieving a relative improvement on the Caltech-UCSD Bird-200 (CUB-200) dataset of 30.5% over the state-of-the-art and a 25.5% relative improvement on the Stanford Dogs dataset. We also demonstrate that Gnostic Fields can be sped up, enabling real-time classification in less than 70 ms per image.