Face cartoonization remains a challenging task due to significant geometric deformations between facial photos and cartoons, as well as the absence of paired training data for supervised learning. Existing methods struggle to generate high-quality cartoonized avatars with attribute consistency. To address this challenge, this paper proposes an unsupervised facial cartoonization method based on cross-domain aligned and deformable vector quantization (CADQ). Firstly, we construct textual descriptions with facial attributes for both photo datasets and cartoon collections. Attribute consistency during transformation is enforced through individually contrastive learning between image-text cross-modal features and globally distribution alignment across photo-cartoon domains. Secondly, a deformable Transformer with dual attention is introduced during the transformation process, which queries corresponding cartoon codebook entries based on image features to simulate cross-domain geometric deformations. Experimental results demonstrate that the proposed method can convert facial photos into high-quality cartoons with attribute consistency, outperforming existing state-of-the-art approaches. Furthermore, the method can be effectively extended to unsupervised cross-domain generation of other artistic portrait styles, achieving superior or highly competitive performance. Our code has been released at: https://github.com/IIP-Lab-XDU/CADQ.

This paper studies offline text-independent writer identification of Chinese handwriting. The Bag of Features method is adopted for Chinese writer identification and performs much better than previous state-of-the-art methods. The feature adopted is scale invariant transform feature (SIFT) descriptor for it can extract local directional information from Chinese characters. Instead of Hard Voting, we use two newly devised coding strategies: Improved Fisher Kernels and Locality-constrained Linear Coding, to encode each SIFT descriptor. To make these coding strategies suitable to this new application area, absolute average pooling function is utilized. At last the K-nearest-neighbor classifier is used to identify the author of a handwriting image. Experimental results are conducted on a newly collected dataset of Chinese handwriting, CASIA Offline DB 2.1. Experimental results show our approach not only outperforms previous state-of-the-art methods, but also the traditional Bag of Word method using Hard Voting.