7 accepted papers
Source Free Unsupervised Domain Adaptation (SFUDA) is critical for deploying deep learning models across diverse clinical settings. However, existing methods are typically designed for low-gap, specific domain shifts and cannot generalize into a unified, multi-modalities, and multi-target framework,
Drawing insights from Large Language Models, researchers have developed several large-scale Electroencephalogram (EEG) models (LEMs) to learn a generalized representation adaptable to various tasks. However, such LEMs are scarce and neglecting the potential in reconstruction tasks. Meanwhile, how to…
Vertical federated learning (VFL) trains model when the features of data samples are scattered over multiple clients. To improve efficiency, a promising approach is to find a coreset of the data samples and use it as a smaller training set. However, existing methods produce a large coreset when ther…
Multimodal emotion recognition based on electroencephalography (EEG) and eye movements has attracted increasing attention due to their high performance and complementary properties. However, there are two challenges that hinder its practical applications: the inconvenient EEG data collection and hig…
In extreme multi-label text classification (XMC), labels usually follow a long-tailed distribution, where most labels only contain a small number of documents and limit the performance of XMC. Data augmentation (DA) is a simple but effective strategy to solve such low-resource problems. In this pape…
Electroencephalogram (EEG) classification tasks have received increasing attention because its high application value. Meanwhile, the great success of general pre-training models in language processing areas inspires us to excavate the potential of an EEG pre-trained model. This model is expected to…
The goal of this paper is to extract the visual-language correspondence from a pre-trained text-to-image diffusion model, in the form of segmentation map, i.e., simultaneously generating images and segmentation masks for the corresponding visual entities described in the text prompt. We make the fol…