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researcher

Junmo Kim

9 papers hereh-index 345 citations9 works total

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

author position
  • middle author5
  • last author4

Across the 9 of 9 papers where every author was matched, so the position is known.

fields
  • cs.CV8
  • cs.LG1
same name
  • Junmo Kim — 63 papers, h 43
  • Junmo Kim — 15 papers, h 4
  • Junmo Kim — 11 papers, h 3
  • Junmo Kim — 9 papers, h 3
  • Junmo Kim — 5 papers, h 3
  • Junmo Kim — 4 papers, h 2

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 citedContextMix: A context-aware data augmentation method for industrial visual inspection systems

12 citations · 16 across the 7 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

cs.CV2026

IMSE: Intrinsic Mixture of Spectral Experts Fine-tuning for Test-Time Adaptation

Sunghyun Baek, Jaemyung Yu, Seunghee Koh +3

Test-time adaptation (TTA) has been widely explored to prevent performance degradation when test data differ from the training distribution. However, fully leveraging the rich repr…

cs.CV2026

Beyond Semantics: Disentangling Information Scope in Sparse Autoencoders for CLIP

Yusung Ro, Jaehyun Choi, Junmo Kim

Sparse Autoencoders (SAEs) have emerged as a powerful tool for interpreting the internal representations of CLIP vision encoders, yet existing analyses largely focus on the semanti…

cs.CV2026

ARGOS: Who, Where, and When in Agentic Multi-Camera Person Search

Myungchul Kim, Kwanyong Park, Junmo Kim +1

Existing person search methods assume access to complete visual queries or exhaustive tracking, yet real-world witness accounts are vague, partial, and spread across cameras and ti…

cs.CV2026

IWP: Token Pruning as Implicit Weight Pruning in Large Vision Language Models

Dong-Jae Lee, Sunghyun Baek, Junmo Kim

Large Vision Language Models show impressive performance across image and video understanding tasks, yet their computational cost grows rapidly with the number of visual tokens. Ex…

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