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Zhenyu Zhang

4 papers here

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

author position
  • first author1
  • middle author3

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

fields
  • cs.CL2
  • cs.CV2
ORCID 0000-0001-5727-9450
same name
  • Zhenyu Zhang — 16 papers
  • Zhenyu Zhang — 8 papers, h 28
  • Zhenyu Zhang — 8 papers
  • Zhenyu Zhang — 6 papers, h 43
  • Zhenyu Zhang — 5 papers
  • Zhenyu Zhang — 5 papers

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

most citedLayout-Aware Information Extraction for Document-Grounded Dialogue: Dataset, Method and Demonstration

3 citations · 3 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CL2024

Data Uncertainty-Aware Learning for Multimodal Aspect-based Sentiment Analysis

Hao Yang, Zhenyu Zhang, Yanyan Zhao +1

As a fine-grained task, multimodal aspect-based sentiment analysis (MABSA) mainly focuses on identifying aspect-level sentiment information in the text-image pair. However, we obse…

cs.CV2024

ConsistentAvatar: Learning to Diffuse Fully Consistent Talking Head Avatar with Temporal Guidance

Haijie Yang, Zhenyu Zhang, Hao Tang +2

Diffusion models have shown impressive potential on talking head generation. While plausible appearance and talking effect are achieved, these methods still suffer from temporal, 3…

cs.CV2024

Tri-Perspective View Decomposition for Geometry-Aware Depth Completion

Zhiqiang Yan, Yuankai Lin, Kun Wang +5

Depth completion is a vital task for autonomous driving, as it involves reconstructing the precise 3D geometry of a scene from sparse and noisy depth measurements. However, most ex…

cs.CL2022★ 3 cited

Layout-Aware Information Extraction for Document-Grounded Dialogue: Dataset, Method and Demonstration

Zhenyu Zhang, Bowen Yu, Haiyang Yu +6

Building document-grounded dialogue systems have received growing interest as documents convey a wealth of human knowledge and commonly exist in enterprises. Wherein, how to compre…

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