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researcher

Bin Xia

12 papers hereh-index 5321 citations14 works total

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

author position
  • middle author11

Across the 11 of 12 papers where every author was matched, so the position is known.

fields
  • cs.CV9
  • cs.AI1
  • cs.GR1
  • eess.IV1
same name
  • Bin Xia — 24 papers, h 30
  • Bin Xia — 18 papers, h 8
  • Bin Xia — 14 papers, h 14
  • Bin Xia — 2 papers, h 3
  • Bin Xia — 2 papers, h 1
  • Bin Xia — 2 papers, h 4

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 citedVmambaIR: Visual State Space Model for Image Restoration

8 citations · 9 across the 11 of their papers we have counts for

collaborators
Showing 2024 · cs.CVShow all

4 papers · 2 filters

cs.CV2024

FFAA: Multimodal Large Language Model based Explainable Open-World Face Forgery Analysis Assistant

Zhengchao Huang, Bin Xia, Zicheng Lin +3

The rapid advancement of deepfake technologies has sparked widespread public concern, particularly as face forgery poses a serious threat to public information security. However, t…

cs.CV2024

GaussianSR: High Fidelity 2D Gaussian Splatting for Arbitrary-Scale Image Super-Resolution

Jintong Hu, Bin Xia, Bin Chen +2

Implicit neural representations (INRs) have significantly advanced the field of arbitrary-scale super-resolution (ASSR) of images. Most existing INR-based ASSR networks first extra…

cs.CV2024★ 8 cited

VmambaIR: Visual State Space Model for Image Restoration

Yuan Shi, Bin Xia, Xiaoyu Jin +5

Image restoration is a critical task in low-level computer vision, aiming to restore high-quality images from degraded inputs. Various models, such as convolutional neural networks…

cs.CV2024★ 1 cited

LLMRA: Multi-modal Large Language Model based Restoration Assistant

Xiaoyu Jin, Yuan Shi, Bin Xia +1

Multi-modal Large Language Models (MLLMs) have a significant impact on various tasks, due to their extensive knowledge and powerful perception and generation capabilities. However,…

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