Face anti-spoofing (FAS) aims to construct a robust system that can withstand diverse attacks. While recent efforts have concentrated mainly on cross-domain generalization, two significant challenges persist: limited semantic understanding of attack types and training redundancy across domains. We address the first by integrating vision-language models (VLMs) to enhance the perception of visual input. For the second challenge, we employ a meta-domain strategy to learn a unified model that generalizes well across multiple domains. Our proposed InstructFLIP is a novel instruction-tuned framework that leverages VLMs to enhance generalization via textual guidance trained solely on a single domain. At its core, InstructFLIP explicitly decouples instructions into content and style components, where content-based instructions focus on the essential semantics of spoofing, and style-based instructions consider variations related to the environment and camera characteristics. Extensive experiments demonstrate the effectiveness of InstructFLIP by outperforming SOTA models in accuracy and substantially reducing training redundancy across diverse domains in FAS. The project website is available at https://kunkunlin1221.github.io/InstructFLIP.

Human-object interaction (HOI) detection seeks complicated relationships between humans and objects, yet struggles persist in correctly associating multiple objects with a single human in complex interaction scenarios. In this paper, we tackle such an issue by introducing a novel interaction anchor that employs flexible strategies across different decoder layers with Barlow constraint and Interactivity-Instance Fusion. The proposed modules are both additive and easily implementable in existing approaches, offering computational efficiency within transformer-based models to compact cross-interactivity. Extensive experiments validate the effectiveness of our method, demonstrating comparable performance on HICO-DET and V-COCO for HOI detection.

Diffusion models revolutionize image generation by leveraging natural language to guide the creation of multimedia content. Despite significant advancements in such generative models, challenges persist in depicting detailed human-object interactions, especially regarding pose and object placement accuracy. We introduce a training-free method named Reasoning and Correcting Diffusion (ReCorD) to address these challenges. Our model couples Latent Diffusion Models with Visual Language Models to refine the generation process, ensuring precise depictions of HOIs. We propose an interaction-aware reasoning module to improve the interpretation of the interaction, along with an interaction correcting module to refine the output image for more precise HOI generation delicately. Through a meticulous process of pose selection and object positioning, ReCorD achieves superior fidelity in generated images while efficiently reducing computational requirements. We conduct comprehensive experiments on three benchmarks to demonstrate the significant progress in solving text-to-image generation tasks, showcasing ReCorD's ability to render complex interactions accurately by outperforming existing methods in HOI classification score, as well as FID and Verb CLIP-Score. Project website is available at https://alberthkyhky.github.io/ReCorD/ .

Recently, federated learning has gained increasing attention for privacy-preserving computation since the learning paradigm allows to train models without the need for exchanging the data across different institutions distributively. However, heterogeneity of computational capabilities of edge devices is seldom discussed and analyzed in the current literature for heterogeneous federated learning. To address this issue, we propose a novel heterogeneous federated learning framework based on multi-branch deep neural network models which enable the selection of a proper sub-branch model for the client devices according to their computational capabilities. Meanwhile, we also present an aggregation method for model training, MFedAvg, that performs branch-wise averaging-based aggregation. With extensive experiments on MNIST, FashionMNIST, MedMNIST, and CIFAR-10, it demonstrates that our proposed approaches can achieve satisfactory performance with guaranteed convergence and effectively utilize all the available resources for training across different devices with lower communication cost than its homogeneous counterpart.