  Fine-Grained Visual Categorization (FGVC) has achieved significant progress
recently. However, the number of fine-grained species could be huge and
dynamically increasing in real scenarios, making it difficult to recognize
unseen objects under the current FGVC framework. This raises an open issue to
perform large-scale fine-grained identification without a complete training
set. Aiming to conquer this issue, we propose a retrieval task named One-Shot
Fine-Grained Instance Retrieval (OSFGIR). "One-Shot" denotes the ability of
identifying unseen objects through a fine-grained retrieval task assisted with
an incomplete auxiliary training set. This paper first presents the detailed
description to OSFGIR task and our collected OSFGIR-378K dataset. Next, we
propose the Convolutional and Normalization Networks (CN-Nets) learned on the
auxiliary dataset to generate a concise and discriminative representation.
Finally, we present a coarse-to-fine retrieval framework consisting of three
components, i.e., coarse retrieval, fine-grained retrieval, and query
expansion, respectively. The framework progressively retrieves images with
similar semantics, and performs fine-grained identification. Experiments show
our OSFGIR framework achieves significantly better accuracy and efficiency than
existing FGVC and image retrieval methods, thus could be a better solution for
large-scale fine-grained object identification.
