Cross-domain few-shot classification (CDFSC) is a challenging and tough task
due to the significant distribution discrepancies across different domains. To
address this challenge, many approaches aim to learn transferable
representations. Multilayer perceptron (MLP) has shown its capability to learn
transferable representations in various downstream tasks, such as unsupervised
image classification and supervised concept generalization. However, its
potential in the few-shot settings has yet to be comprehensively explored. In
this study, we investigate the potential of MLP to assist in addressing the
challenges of CDFSC. Specifically, we introduce three distinct frameworks
incorporating MLP in accordance with three types of few-shot classification
methods to verify the effectiveness of MLP. We reveal that MLP can
significantly enhance discriminative capabilities and alleviate distribution
shifts, which can be supported by our expensive experiments involving 10
baseline models and 12 benchmark datasets. Furthermore, our method even
compares favorably against other state-of-the-art CDFSC algorithms.

使用多层感知器（MLP）解决跨领域少样本分类（CDFSC）的挑战，发现 MLP 可以显著提高判别能力和减轻分布偏移，与其他先进的 CDFSC 算法相比，显示出更好的性能。

多层感知器优化跨领域少样本分类

Improving Cross-domain Few-shot Classification with Multilayer  Perceptron

Despite the remarkable recent progress, person re-identification (Re-ID)
approaches are still suffering from the failure cases where the discriminative
body parts are missing. To mitigate such cases, we propose a simple yet
effective Horizontal Pyramid Matching (HPM) approach to fully exploit various
partial information of a given person, so that correct person candidates can be
still identified even even some key parts are missing. Within the HPM, we make
the following contributions to produce a more robust feature representation for
the Re-ID task: 1) we learn to classify using partial feature representations
at different horizontal pyramid scales, which successfully enhance the
discriminative capabilities of various person parts; 2) we exploit average and
max pooling strategies to account for person-specific discriminative
information in a global-local manner. To validate the effectiveness of the
proposed HPM, extensive experiments are conducted on three popular benchmarks,
including Market-1501, DukeMTMC-ReID and CUHK03. In particular, we achieve mAP
scores of 83.1%, 74.5% and 59.7% on these benchmarks, which are the new
state-of-the-arts. Our code is available on Github

通过提出的 Horizontal Pyramid Matching 方法，我们可以在一定程度上减缓由于缺失身体部位而出现的识别失败情况，通过对不同的部分进行分类来增强各个人物部位的辨别能力，在全局和局部方面运用平均和最大池化策略来区分不同的人物信息，并在三个流行的基准测试中分别获得马记得 1501，DukeMTMC-ReID 和 CUHK03 的 mAP 分别为 83.1％，74.5％和 59.7％，从而达到新的最佳水平。