most citedERNIE 5.0 Technical Report

2 citations · 3 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CL20262 cited

ERNIE 5.0 Technical Report

Haifeng Wang, Hua Wu, Tian Wu +432

In this report, we introduce ERNIE 5.0, a natively autoregressive foundation model desinged for unified multimodal understanding and generation across text, image, video, and audio…

cs.CL2026

BayesRAG: Probabilistic Mutual Evidence Corroboration for Multimodal Retrieval-Augmented Generation

Xuan Li, Yining Wang, Haocai Luo +6

Retrieval-Augmented Generation (RAG) has become a pivotal paradigm for Large Language Models (LLMs), yet current approaches struggle with visually rich documents by treating text a…

cs.CV2026

Semi-Supervised Facial Expression Recognition based on Dynamic Threshold and Negative Learning

Zhongpeng Cai, Jun Yu, Wei Xu +3

Facial expression recognition is a key task in human-computer interaction and affective computing. However, acquiring a large amount of labeled facial expression data is often cost…

cs.CV2026

Multimodal Sentiment Analysis based on Multi-channel and Symmetric Mutual Promotion Feature Fusion

Wangyuan Zhu, Jun Yu

Multimodal sentiment analysis is a key technology in the fields of human-computer interaction and affective computing. Accurately recognizing human emotional states is crucial for…

cs.CV2026

MIAR: Modality Interaction and Alignment Representation Fuison for Multimodal Emotion

Jichao Zhu, Jun Yu

Multimodal Emotion Recognition (MER) aims to perceive human emotions through three modes: language, vision, and audio. Previous methods primarily focused on modal fusion without ad…

cs.CV2025

Frequency-Aware Vision-Language Multimodality Generalization Network for Remote Sensing Image Classification

Junjie Zhang, Feng Zhao, Hanqiang Liu +1

The booming remote sensing (RS) technology is giving rise to a novel multimodality generalization task, which requires the model to overcome data heterogeneity while possessing pow…