Facial 3D Regional Structural Motion Representation Using Lightweight Point Cloud Networks for Micro-Expression Recognition

3D Regional Structural Motion Representation Using Lightweight Point Cloud Networks for Micro-Expression Recognition Academic Background Micro-expressions (MEs) are brief and subtle facial expressions in human emotional expression, typically lasting between 1⁄25 and 1⁄5 of a second. Due to their spontaneity, rapidity, and difficulty to control, mic...

Multi-scale Hyperbolic Contrastive Learning for Cross-subject EEG Emotion Recognition

Cross-Subject EEG Emotion Recognition Research Based on Multi-Scale Hyperbolic Contrastive Learning Academic Background Electroencephalography (EEG), as a physiological signal, plays an important role in the field of affective computing. Compared with traditional non-physiological cues (such as facial expressions or voice), EEG signals have higher ...

Multimodal Sentiment Analysis with Mutual Information-Based Disentangled Representation Learning

Disentangled Representation Learning in Multimodal Sentiment Analysis Using Mutual Information: An Innovative Study Academic Background With the rapid development of social media, the amount of user-generated multimedia content (such as tweets and videos) has increased dramatically. These multimedia data typically include three modalities: visual (...

Spectro-Temporal Modulations Incorporated Two-Stream Robust Speech Emotion Recognition

Research on Two-Stream Robust Speech Emotion Recognition Based on Spectro-Temporal Modulation Features Academic Background Speech Emotion Recognition (SER) is a technology that identifies emotions by analyzing the emotional content in human speech. It has broad application potential in areas such as human-computer interaction, customer service mana...

Neural Mechanisms of Relational Learning and Fast Knowledge Reassembly in Plastic Neural Networks

Neural Mechanisms and Relational Learning: Rapid Knowledge Reassembly in Neural Networks Background Humans and animals possess a remarkable ability to learn relationships between items in experience (such as stimuli, objects, and events), enabling structured generalization and rapid information assimilation. A fundamental type of such relational le...

Learning with Enriched Inductive Biases for Vision-Language Models

Learning with Enriched Inductive Biases for Vision-Language Models Research Background and Problem Statement In recent years, Vision-Language Models (VLMs) have made significant progress in the fields of computer vision and natural language processing. These models are pre-trained on large-scale image-text pairs to construct a unified multimodal re...