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Xianghua Fu

4 papers hereh-index 213 citations5 works total

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

author position
  • middle author3
  • last author1

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CL1
  • cs.CR1
  • cs.CV1
  • cs.LG1
same name
  • Xianghua Fu — 2 papers
  • Xianghua Fu — 2 papers, h 6
  • Xianghua Fu — 1 paper, h 3

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 citedTopicVD: A Topic-Based Dataset of Video-Guided Multimodal Machine Translation for Documentaries

1 citations · 1 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CV2026

Video-guided Machine Translation with Global Video Context

Jian Chen, JinZe Lv, Zi Long +1

Video-guided Multimodal Translation (VMT) has advanced significantly in recent years. However, most existing methods rely on locally aligned video segments paired one-to-one with s…

cs.CL2025★ 1 cited

TopicVD: A Topic-Based Dataset of Video-Guided Multimodal Machine Translation for Documentaries

Jinze Lv, Jian Chen, Zi Long +2

Most existing multimodal machine translation (MMT) datasets are predominantly composed of static images or short video clips, lacking extensive video data across diverse domains an…

cs.CR2025

A General Pseudonymization Framework for Cloud-Based LLMs: Replacing Privacy Information in Controlled Text Generation

Shilong Hou, Ruilin Shang, Zi Long +2

An increasing number of companies have begun providing services that leverage cloud-based large language models (LLMs), such as ChatGPT. However, this development raises substantia…

cs.LG2024

Multi-intent Aware Contrastive Learning for Sequential Recommendation

Junshu Huang, Zi Long, Xianghua Fu +1

Intent is a significant latent factor influencing user-item interaction sequences. Prevalent sequence recommendation models that utilize contrastive learning predominantly rely on…

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