TALDS-Net: Task-Aware Adaptive Local Descriptors Selection for Few-shot Image Classification
This work addresses the challenge of few-shot image classification for computer vision applications, representing an incremental improvement by focusing on descriptor selection.
The paper tackles the problem of few-shot image classification by proposing TALDS-Net, which adaptively selects task-aware local descriptors for both support and query images, resulting in outperforming state-of-the-art methods on general and fine-grained datasets.
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.