In recent years, Wi-Fi sensing has garnered significant attention due to its numerous benefits, such as privacy protection, low cost, and penetration ability. Extensive research has been conducted in this field, focusing on areas such as gesture recognition, people identification, and fall detection. However, many data-driven methods encounter challenges related to domain shift, where the model fails to perform well in environments different from the training data. One major factor contributing to this issue is the limited availability of Wi-Fi sensing datasets, which makes models learn excessive irrelevant information and over-fit to the training set. Unfortunately, collecting large-scale Wi-Fi sensing datasets across diverse scenarios is a challenging task. To address this problem, we propose CrossFi, a siamese network-based approach that excels in both in-domain scenario and cross-domain scenario, including few-shot, zero-shot scenarios, and even works in few-shot new-class scenario where testing set contains new categories. The core component of CrossFi is a sample-similarity calculation network called CSi-Net, which improves the structure of the siamese network by using an attention mechanism to capture similarity information, instead of simply calculating the distance or cosine similarity. Based on it, we develop an extra Weight-Net that can generate a template for each class, so that our CrossFi can work in different scenarios. Experimental results demonstrate that our CrossFi achieves state-of-the-art performance across various scenarios. In gesture recognition task, our CrossFi achieves an accuracy of 98.17% in in-domain scenario, 91.72% in one-shot cross-domain scenario, 64.81% in zero-shot cross-domain scenario, and 84.75% in one-shot new-class scenario. To facilitate future research, we will release the code for our model upon publication.

翻译：近年来，Wi-Fi感知因其隐私保护、成本低廉和穿透能力强等诸多优势而受到广泛关注。该领域已开展了大量研究，主要集中在手势识别、人员身份识别和跌倒检测等方面。然而，许多数据驱动方法面临域偏移的挑战，即模型在与训练数据不同的环境中表现不佳。造成这一问题的主要因素之一是Wi-Fi感知数据集的有限可用性，这导致模型学习了过多的无关信息并对训练集产生过拟合。遗憾的是，在不同场景下收集大规模Wi-Fi感知数据集是一项具有挑战性的任务。为解决此问题，我们提出了CrossFi，一种基于孪生网络的方法，该方法在域内场景和跨域场景（包括少样本、零样本场景）中均表现优异，甚至能在测试集包含新类别的少样本新类场景中工作。CrossFi的核心组件是名为CSi-Net的样本相似度计算网络，它通过使用注意力机制来捕获相似性信息，改进了孪生网络的结构，而非简单地计算距离或余弦相似度。在此基础上，我们开发了一个额外的Weight-Net，可为每个类别生成模板，从而使我们的CrossFi能够在不同场景下工作。实验结果表明，我们的CrossFi在各种场景下均实现了最先进的性能。在手势识别任务中，CrossFi在域内场景的准确率达到98.17%，在单样本跨域场景中达到91.72%，在零样本跨域场景中达到64.81%，在单样本新类场景中达到84.75%。为促进未来研究，我们将在论文发表后公开模型代码。