15 accepted papers
Human emotional states in real life are varied and complex. It is difficult for existing methods to capture robust facial expression features dynamically, especially in a large head pose and occlusion. In this paper, a novel end-to-end graph-guided spatiotemporal convolutional network (GSTCN) is pro…
The performance of traditional beamforming algorithms is influenced by the number of microphones, with performance improving as the number increases. However, in practice, the number of microphones is often limited. In this paper, we propose a novel virtual microphone estimation method that combines…
Motor imagery (MI) is a widely used paradigm in brain-computer interfaces (BCIs). Despite recent advancements, MI classification still faces challenges such as limited data availability and poor performance for new users. In particular, feature alignment based on deep learning in zero-calibration cr…
One of the research focuses of large language models (LLMs) is the ability to generate action plans. Recent studies have revealed that the performance of LLMs can be significantly improved by integrating external tools. Based on this, we propose a benchmark framework called PlanningArena, which aims…
Electrocardiogram (ECG) is essential for the clinical diagnosis of arrhythmias and other heart diseases, but deep learning methods based on ECG often face limitations due to the need for high-quality annotations. Although previous ECG self-supervised learning (eSSL) methods have made significant pro…
Generating logically coherent video from text (T2V) for reasoning-intensive tasks like mathematical problem-solving presents a significant challenge for Vision-Language Models (VLMs). Therefore, we introduce VisualEDU, a benchmark based on Manim package to rigorously evaluate VLM capabilities in pro
Few-shot learning (FSL) poses a considerable challenge since it aims to improve the model generalization ability with limited labeled data. Previous works usually attempt to construct class-specific prototypes and then predict novel classes using these prototypes. However, the feature distribution r…
Dental caries has been widely recognized as one of the most prevalent chronic diseases in the field of public health. Despite advancements in automated diagnosis across various medical domains, it remains a substantial challenge for dental caries detection due to its inherent variability and intrica…
Multi-channel speech enhancement extracts speech using multiple microphones that capture spatial cues. Effectively utilizing directional information is therefore key. Deep learning shows great potential on multi-channel speech enhancement and often takes short-time Fourier Transform (STFT) as inputs…
Multi-channel speech enhancement leverages multiple microphones to extract target speech signals amid background noise. Effectively utilizing directional cues is key for robust enhancement. While deep learning shows promise for multi-channel speech processing, most methods operate on short-time Four…
Medical vision-language representation learning has garnered considerable attention owing to its applicability to extracting generic representations from the image and text modality. However, it still remains challenging to acquire a more comprehensive understanding of intra- and inter-modal semanti…
EEG signals of different individuals belong to different domains and have different data distributions because great individual distinctions exist in EEG signals. The existing methods on EEG-based emotion recognition often ignore this property and need to collect extensive EEG data for new subjects…
There are many methods for detecting forged audio produced by conversion and synthesis. However, as a simpler method of forgery, splicing has not attracted widespread attention. Based on the characteristic that the tampering operation will cause singularities at high-frequency components, we propose…
Cross-subject emotion recognition is one of the most challenging tasks in electroencephalogram (EEG)-based emotion recognition. To guarantee the constancy of feature representations across domains and to eliminate differences between domains, we explored the feasibility of combining temporal convolu…
In this paper, we focus on exploring modality-temporal mutual information for RGB-D action recognition. In order to learn time-varying information and multi-modal features jointly, we propose a novel deep bilinear learning framework. In the framework, we propose bilinear blocks that consist of two l…