5 accepted papers
Few-shot fine-grained image classification (FSFG) aims to recognize novel fine-grained categories from only a few labeled samples. Existing FSFG methods primarily focus on fine-grained feature extraction and modeling query-support interactions within training episodes containing a small number of cl
Few-shot class-incremental learning (FSCIL) aims at recognizing novel classes continually with limited novel class samples. A mainstream baseline for FSCIL is first to train the whole model in the base session, then freeze the feature extractor in the incremental sessions. Despite achieving high ove…
Recently proposed Fine-Grained Few-Shot Class-Incremental Learning (FG-FSCIL) offers a practical and efficient solution for enabling models to incrementally learn new fine-grained categories under limited data conditions. However, existing methods still settle for the fine-grained feature extraction…
The rapidly growing scale of data in practice poses demands on the efficiency of retrieval models. However for fine-grained image retrieval task there are inherent contradictions in the design of hashing based efficient models. Firstly the limited information embedding capacity of low-dimensional bi…
Recently, a number of Few-Shot Fine-Grained Image Classification (FS-FGIC) methods have been proposed, but they primarily focus on better fine-grained feature extraction while overlooking two important issues. The first one is how to extract discriminative features for Fine-Grained Image Classificat…