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researcher

Ruibo Fu

CASIA

43 papers hereh-index 171.5k citations101 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author4
  • middle author35
  • last author1

Across the 40 of 43 papers where every author was matched, so the position is known.

fields
  • cs.SD25
  • eess.AS8
  • cs.MM4
  • cs.CL3
  • cs.CV2
  • cs.LG1
affiliations
  • CASIA
ORCID 0000-0001-9598-1881
same name
  • Ruibo Fu — 8 papers, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
most citedDetect All-Type Deepfake Audio: Wavelet Prompt Tuning for Enhanced Auditory Perception

1 citations · 1 across the 5 of their papers we have counts for

collaborators
Showing cs.MMShow all

4 papers · 1 filter

cs.MM2025

PSA-MF: Personality-Sentiment Aligned Multi-Level Fusion for Multimodal Sentiment Analysis

Heng Xie, Kang Zhu, Zhengqi Wen +4

Multimodal sentiment analysis (MSA) is a research field that recognizes human sentiments by combining textual, visual, and audio modalities. The main challenge lies in integrating…

cs.MM2025

Deconfounded Reasoning for Multimodal Fake News Detection via Causal Intervention

Moyang Liu, Kaiying Yan, Yukun Liu +4

The rapid growth of social media has led to the widespread dissemination of fake news across multiple content forms, including text, images, audio, and video. Traditional unimodal…

cs.MM2025

Exploring Modality Disruption in Multimodal Fake News Detection

Moyang Liu, Kaiying Yan, Yukun Liu +4

The rapid growth of social media has led to the widespread dissemination of fake news across multiple content forms, including text, images, audio, and video. Compared to unimodal…

cs.MM2024

Exploring the Role of Audio in Multimodal Misinformation Detection

Moyang Liu, Yukun Liu, Ruibo Fu +4

With the rapid development of deepfake technology, especially the deep audio fake technology, misinformation detection on the social media scene meets a great challenge. Social med…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.