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Xin Jin

40 papers hereh-index 294.5k citations66 works total

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

author position
  • first author13
  • middle author26

Across the 39 of 40 papers where every author was matched, so the position is known.

fields
  • cs.CV34
  • eess.IV4
  • cs.LG1
  • cs.MM1
same name
  • Xin Jin — 20 papers, h 7
  • Xin Jin — 14 papers, h 7
  • Xin Jin — 14 papers, h 7
  • Xin Jin — 12 papers, h 7
  • Xin Jin — 11 papers, h 18
  • Xin Jin — 11 papers, h 6

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
20162026
most citedThe MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization

183 citations · 711 across the 37 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

cs.CV2023★ 7 cited

Prompt-ICM: A Unified Framework towards Image Coding for Machines with Task-driven Prompts

Ruoyu Feng, Jinming Liu, Xin Jin +3

Image coding for machines (ICM) aims to compress images to support downstream AI analysis instead of human perception. For ICM, developing a unified codec to reduce information red…

eess.IV2023★ 1 cited

Semantically Structured Image Compression via Irregular Group-Based Decoupling

Ruoyu Feng, Yixin Gao, Xin Jin +2

Image compression techniques typically focus on compressing rectangular images for human consumption, however, resulting in transmitting redundant content for downstream applicatio…

cs.CV2023★ 61 cited

Inpaint Anything: Segment Anything Meets Image Inpainting

Tao Yu, Runseng Feng, Ruoyu Feng +4

Modern image inpainting systems, despite the significant progress, often struggle with mask selection and holes filling. Based on Segment-Anything Model (SAM), we make the first at…

cs.CV2023

Stable Attribute Group Editing for Reliable Few-shot Image Generation

Guanqi Ding, Xinzhe Han, Shuhui Wang +3

Few-shot image generation aims to generate data of an unseen category based on only a few samples. Apart from basic content generation, a bunch of downstream applications hopefully…

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