Despite advances in Large Language Models (LLMs) and Multimodal LLMs (MLLMs) for visual document understanding (VDU), visual information extraction (VIE) from relation-rich documents remains challenging due to the layout diversity and limited training data. While existing synthetic document generators attempt to address data scarcity, they either rely on manually designed layouts and templates, or adopt rule-based approaches that limit layout diversity. Besides, current layout generation methods focus solely on topological patterns without considering textual content, making them impractical for generating documents with complex associations between the contents and layouts. In this paper, we propose a Relation-rIch visual Document GEnerator (RIDGE) that addresses these limitations through a two-stage approach: (1) Content Generation, which leverages LLMs to generate document content using a carefully designed Hierarchical Structure Text format which captures entity categories and relationships, and (2) Content-driven Layout Generation, which learns to create diverse, plausible document layouts solely from easily available Optical Character Recognition (OCR) results, requiring no human labeling or annotations efforts. Experimental results have demonstrated that our method significantly enhances the performance of document understanding models on various VIE benchmarks.

Face recognition (FR) has achieved unprecedented success in high-resolution face recognition (HRFR) scenarios. However, applying the models trained on HRFR tasks to cross-resolution face recognition (CRFR) or low-resolution face recognition (LRFR) results in performance decline. This is attributed to the challenges in extracting identity-preserving features from low-resolution images. To address this, we present Lipface, an FR model fortified with Lipschitz constraints, designed to perform robustly across varying face resolutions. Also, recognizing the differential impact of training images based on their quality, we adopt the feature norm as an image quality metric. This allows our model to weigh the significance of samples during training dynamically. Experiments demonstrate that, under various backbones and training datasets, Lipface outperforms state-of-the-art FR methods. Across two HRFR datasets (LFW and IJBB), one LRFR dataset (Tinyface), and two CRFR datasets (SCface and XQLFW), Lipface achieves an average improvement ranging from 1.92% to 6.07% across the five datasets.

Although face anti-spoofing (FAS) methods have achieved remarkable performance on specific domains or attack types, few studies have focused on the simultaneous presence of domain changes and unknown attacks, which is closer to real application scenarios. To handle domain-generalized unknown attacks, we introduce a new method, DGUA-FAS, which consists of a Transformer-based feature extractor and a synthetic unknown attack sample generator (SUASG). The SUASG network simulates unknown attack samples to assist the training of the feature extractor. Experimental results show that our method achieves superior performance on domain generalization FAS with known or unknown attacks.

Existing face anti-spoofing (FAS) models have achieved high performance on specific datasets. However, for the application of real-world systems, the FAS model should generalize to the data from unknown domains rather than only achieve good results on a single baseline. As vision transformer models have demonstrated astonishing performance and strong capability in learning discriminative information, we investigate applying transformers to distinguish the face presentation attacks over unknown domains. In this work, we propose the Domain-invariant Vision Transformer (DiVT) for FAS, which adopts two losses to improve the generalizability of the vision transformer. First, a concentration loss is employed to learn a domain-invariant representation that aggregates the features of real face data. Second, a separation loss is utilized to union each type of attack from different domains. The experimental results show that our proposed method achieves state-of-the-art performance on the protocols of domain-generalized FAS tasks. Compared to previous domain generalization FAS models, our proposed method is simpler but more effective.