Visual reasoning requires multimodal perception and commonsense cognition of the world.
Recently, multiple vision-language models (VLMs) have been proposed with excellent …

Abstract Domain generalization (DG) aims to learn a model that generalizes well to unseen
target domains utilizing multiple source domains without re-training. Most existing DG works …

Abstract Sparsely-gated Mixture of Expert (MoE), an emerging deep model architecture, has
demonstrated a great promise to enable high-accuracy and ultra-efficient model inference …

As machine learning models in critical fields increasingly grapple with multimodal data, they
face the dual challenges of handling a wide array of modalities, often incomplete due to …

Domain generalization (DG) aims to generalize the knowledge learned from multiple source
domains to unseen target domains. Existing DG techniques can be subsumed under two …

Domain generalization (DG) aims at solving distribution shift problems in various scenes.
Existing approaches are based on Convolution Neural Networks (CNNs) or Vision …

Graph neural networks (GNNs) have found extensive applications in learning from graph
data. However, real-world graphs often possess diverse structures and comprise nodes and …

Although the generalization of face anti-spo-ofing (FAS) is increasingly concerned, it is still
in the initial stage to solve it based on Vision Transformer (ViT). In this paper, we present a …

Deepfakes have recently raised significant trust issues and security concerns among the
public. Compared to CNN face forgery detectors, ViT-based methods take advantage of the …

Mixture of experts (MoE) model is a statistical machine learning design that aggregates
multiple expert networks using a softmax gating function in order to form a more intricate and …