Task-Adaptive Multi-Source Representations for Few-Shot Image Recognition

Conventional few-shot learning (FSL) mainly focuses on knowledge transfer from a single source dataset to a recognition scenario with only a few training samples available but still similar to the source domain. In this paper, we consider a more practical FSL setting where multiple semantically different datasets are available to address a wide range of FSL tasks, especially for some recognition scenarios beyond natural images, such as remote sensing and medical imagery. It can be referred to as multi-source cross-domain FSL. To tackle the problem, we propose a two-stage learning scheme, termed learning and adapting multi-source representations (LAMR). In the first stage, we propose a multi-head network to obtain efficient multi-domain representations, where all source domains share the same backbone except for the last parallel projection layers for domain specialization. We train the representations in a multi-task setting where each in-domain classification task is taken by a cosine classifier. In the second stage, considering that instance discrimination and class discrimination are crucial for robust recognition, we propose two contrastive objectives for adapting the pre-trained representations to be task-specialized on the few-shot data. Careful ablation studies verify that LAMR significantly improves representation transferability, showing consistent performance boosts. We also extend LAMR to single-source FSL by introducing a dataset-splitting strategy that equally splits one source dataset into sub-domains. The empirical results show that LAMR can achieve SOTA performance on the BSCD-FSL benchmark and competitive performance on mini-ImageNet, highlighting its versatility and effectiveness for FSL of both natural and specific imaging.