TALDS-Net: Task-Aware Adaptive Local Descriptors Selection for Few-shot
  Image Classification
Qian Qiao Yu Xie Ziyin Zeng Fanzhang Li
Few-shot image classification aims to classify images from unseen novel
classes with few samples. Recent works demonstrate that deep local descriptors
exhibit enhanced representational capabilities compared to image-level
features. However, most existing methods solely rely on either employing all
local descriptors or directly utilizing partial descriptors, potentially
resulting in the loss of crucial information. Moreover, these methods primarily
emphasize the selection of query descriptors while overlooking support
descriptors. In this paper, we propose a novel Task-Aware Adaptive Local
Descriptors Selection Network (TALDS-Net), which exhibits the capacity for
adaptive selection of task-aware support descriptors and query descriptors.
Specifically, we compare the similarity of each local support descriptor with
other local support descriptors to obtain the optimal support descriptor subset
and then compare the query descriptors with the optimal support subset to
obtain discriminative query descriptors. Extensive experiments demonstrate that
our TALDS-Net outperforms state-of-the-art methods on both general and
fine-grained datasets.