1 research outputs found
Boosting Few-shot Fine-grained Recognition with Background Suppression and Foreground Alignment
Few-shot fine-grained recognition (FS-FGR) aims to recognize novel
fine-grained categories with the help of limited available samples.
Undoubtedly, this task inherits the main challenges from both few-shot learning
and fine-grained recognition. First, the lack of labeled samples makes the
learned model easy to overfit. Second, it also suffers from high intra-class
variance and low inter-class difference in the datasets. To address this
challenging task, we propose a two-stage background suppression and foreground
alignment framework, which is composed of a background activation suppression
(BAS) module, a foreground object alignment (FOA) module, and a local to local
(L2L) similarity metric. Specifically, the BAS is introduced to generate a
foreground mask for localization to weaken background disturbance and enhance
dominative foreground objects. What's more, considering the lack of labeled
samples, we compute the pairwise similarity of feature maps using both the raw
image and the refined image. The FOA then reconstructs the feature map of each
support sample according to its correction to the query ones, which addresses
the problem of misalignment between support-query image pairs. To enable the
proposed method to have the ability to capture subtle differences in confused
samples, we present a novel L2L similarity metric to further measure the local
similarity between a pair of aligned spatial features in the embedding space.
Extensive experiments conducted on multiple popular fine-grained benchmarks
demonstrate that our method outperforms the existing state-of-the-art by a
large margin.Comment: Preprint under review in TCSVT Journa