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researcher

Xiaopeng Fan

10 papers hereh-index 14503 citations51 works total

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

author position
  • middle author5
  • last author3

Across the 8 of 10 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • cs.NI2
  • eess.IV2
  • cs.MM1
  • eess.SP1
same name
  • Xiaopeng Fan — 19 papers, h 6
  • Xiaopeng Fan — 14 papers, h 4
  • Xiaopeng Fan — 7 papers, h 27
  • Xiaopeng Fan — 7 papers, h 4
  • Xiaopeng Fan — 5 papers, h 6
  • Xiaopeng Fan — 3 papers, h 5

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
20232026
most citedSpiking Tucker Fusion Transformer for Audio-Visual Zero-Shot Learning

3 citations · 9 across the 7 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2026

Beyond Single Solution: Multi-Hypothesis Collaborative Deep Unfolding Network for Image Compressive Sensing

Wenxue Cui, Hualin Li, Yuhang Qin +3

Recent deep unfolding networks (DUNs) have advanced Compressive Sensing (CS) by effectively integrating iterative optimization with deep learning architectures. However, most CS ap…

cs.CV2024★ 1 cited

Deblur4DGS: 4D Gaussian Splatting from Blurry Monocular Video

Renlong Wu, Zhilu Zhang, Mingyang Chen +2

Recent 4D reconstruction methods have yielded impressive results but rely on sharp videos as supervision. However, motion blur often occurs in videos due to camera shake and object…

cs.CV2024

Multi-modal Crowd Counting via Modal Emulation

Chenhao Wang, Xiaopeng Hong, Zhiheng Ma +3

Multi-modal crowd counting is a crucial task that uses multi-modal cues to estimate the number of people in crowded scenes. To overcome the gap between different modalities, we pro…

cs.CV2024

SpikeMba: Multi-Modal Spiking Saliency Mamba for Temporal Video Grounding

Wenrui Li, Xiaopeng Hong, Ruiqin Xiong +1

Temporal video grounding (TVG) is a critical task in video content understanding, requiring precise alignment between video content and natural language instructions. Despite signi…

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