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Few-shot classification (FSC) is a highly challenging task, as only a small number of labeled samples are available when identifying new categories. Distance metric learning-based methods have emerged as a prominent approach to FSC, which typically use a distance function to measure the difference between query and support samples for identifying the class membership of the query sample. However, these methods simply treat each channel difference between query and support features equally when computing the class scores. Since different channels in the learned feature seek for different patterns, these distance metrics fail to consider that different channels are of different importance to FSC, and thus cannot accurately measure the similarity between samples. To address this issue, we propose a Q uery- C entric D istance M odulator (QCDM) to generate query-related weights for each channel difference adaptively. Specifically, since the distribution of difference between a query sample and all support samples in a particular channel can reflect the importance of this channel to the classification of the query sample, we take this difference vector as input and generate a query-specific channel weight through a meta-network. QCDM can guide FSC models to focus on discriminative channel differences and achieve better generalization. QCDM is a plug-and-play module that can be seamlessly integrated with existing distance metric learning-based FSC methods. Extensive experimental results indicate that our method can effectively improve the performance of distance metric learning-based FSC methods. The source code is available in https://github.com/Wu-Wenxiao/QCDM. • A plug-and-play query-centric distance modulator for distance metric learning-based FSC methods. • Weight generation through data inconsistency within each channel from different classes. • Decoupled from the process of feature extraction and integrated with any given backbone. [ABSTRACT FROM AUTHOR]

The deep learning based methods have improved the visual tracking precision significantly. However, the background distraction and the high precise localization remain challenging problems. Despite that some methods have fused the deep and shallow layer features to solve these problems, the existing fusion methods, like simply concatenating or adding the features from the different layers, cannot take the advantage of both the deep and shallow layer features fully. In this paper, we propose a new adaptive feature fusion method, called the instance-based feature pyramid (IBFP) to obtain the discriminative high-resolution feature, which not only inherits the discriminative information from the deep layer feature, but also keeps the high precision localization information of the shallow layer feature. For utilizing the deep and shallow features effectively, we design an instance-based upsampling (IBU) module to fuse them, and a compressed space channel selection (CSCS) module to re-weight the feature channels adaptively. We insert the IBU and CSCS modules in the Siamese tracker for end-to-end training and testing. By using the proposed IBU and CSCS modules, we fuse the deep and shallow features in a series manner. Experiments on large-scale benchmark datasets demonstrate that the proposed modules boost the capabilities of distinguishing the targets and the similar distractors and perform favorably against the state-of-the-art. [ABSTRACT FROM AUTHOR]