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researcher

Jun Li

5 papers hereh-index 323 citations7 works total

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 5 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • cs.CR1
same name
  • Jun Li — 16 papers, h 4
  • Jun Li — 11 papers, h 17
  • Jun Li — 8 papers, h 13
  • Jun Li — 7 papers, h 1
  • Jun Li — 7 papers, h 4
  • Jun Li — 7 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

most citedOrganizing Unstructured Image Collections using Natural Language

1 citations · 1 across the 3 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2026

Beyond Text Prompts: Precise Concept Erasure through Text-Image Collaboration

Jun Li, Lizhi Xiong, Ziqiang Li +4

Text-to-image generative models have achieved impressive fidelity and diversity, but can inadvertently produce unsafe or undesirable content due to implicit biases embedded in larg…

cs.CV2026★ 1 cited

Organizing Unstructured Image Collections using Natural Language

Mingxuan Liu, Zhun Zhong, Jun Li +3

In this work, we introduce and study the novel task of Open-ended Semantic Multiple Clustering (OpenSMC). Given a large, unstructured image collection, the goal is to automatically…

cs.CV2025

Enhancing Supervised Composed Image Retrieval via Reasoning-Augmented Representation Engineering

Jun Li, Hongjian Dou, Zhenyu Zhang +3

Composed Image Retrieval (CIR) presents a significant challenge as it requires jointly understanding a reference image and a modified textual instruction to find relevant target im…

cs.CV2025

A Comprehensive Survey on Visual Concept Mining in Text-to-image Diffusion Models

Ziqiang Li, Jun Li, Lizhi Xiong +2

Text-to-image diffusion models have made significant advancements in generating high-quality, diverse images from text prompts. However, the inherent limitations of textual signals…

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