Radio Frequency Fingerprint Identification (RFFI), which leverages hardware-specific impairments in Internet of Things (IoT) devices, is widely used for device authentication and spoofing attack detection to enhance communication security. However, the existing RFFI methods heavily depend on large-scale training datasets in deep learning (DL), with severe overfitting issues if the training samples are scarce. This paper proposes a few-shot learning framework that combines feature augmentation and meta-learning to overcome these challenges. A novel data augmentation technique based on grad class activation maps (Grad-CAM) is introduced to address the scarcity of training samples, which generates augmented samples by adjusting the weights of receptive fields in feature maps, forming an auxiliary dataset for training. In meta-training, the auxiliary dataset is used to construct tasks comprising support and query sets. By extracting common features from the limited sample size, the framework trains a meta-model with robust generalization capabilities. In the deployment phase, a fine-tuning strategy further optimizes the classifier using a small labeled dataset from new IoT devices, allowing rapid adaptation with high accuracy. The proposed framework is evaluated on the large-scale open-source dataset, achieving an accuracy of 94.1% under 8-way 5-shot, with only 25 samples per device in meta-training. Meta-learning boosts performance by 15%-20%, with meta-training feature augmentation further increasing accuracy by 5%-6.6%. Compared to baseline methods, the proposed framework improves the accuracy by 30%, which outperforms the state-of-the-art algorithms by 4%-25%.

Avatars are one of the most important elements in virtual environments. Real-time facial retargeting technology is of vital importance in AR/VR interactions, the filmmaking, and the entertainment industry, and blendshapes for avatars are one of its important materials. Previous works either focused on the characters with the same topology, which cannot be generalized to universal avatars, or used optimization methods that have high demand on the dataset. In this paper, we adopt the essence of deep learning and feature transfer to realize deformation transfer, thereby generating blendshapes for target avatars based on the given sources. We proposed a Variational Autoencoder (VAE) to extract the latent space of the avatars and then use a Multilayer Perceptron (MLP) model to realize the translation between the latent spaces of the source avatar and target avatars. By decoding the latent code of different blendshapes, we can obtain the blendshapes for the target avatars with the same semantics as that of the source. We qualitatively and quantitatively compared our method with both classical and learning-based methods. The results revealed that the blendshapes generated by our method achieves higher similarity to the groundtruth blendshapes than the state-of-art methods. We also demonstrated that our method can be applied to expression transfer for stylized characters with different topologies.