◍wovepaper
SearchResearchersInstitutions
Sign in
researcher

Jungho Lee

24 papers hereh-index 6163 citations33 works total

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

author position
  • first author5
  • middle author17

Across the 22 of 24 papers where every author was matched, so the position is known.

fields
  • cs.CV24
same name
  • Jungho Lee — 3 papers, h 3
  • Jungho Lee — 2 papers, h 9
  • Jungho Lee — 2 papers, h 1
  • Jungho Lee — 1 paper, h 13
  • Jungho Lee — 1 paper, h 3
  • Jungho Lee — 1 paper, 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
20222026
most citedGenCLIP: Generalizing CLIP Prompts for Zero-shot Anomaly Detection

8 citations · 20 across the 21 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

cs.CV2026

OTT-Vid: Optimal Transport Temporal Token Compression for Video Large Language Models

Minseok Kang, Minhyeok Lee, Jungho Lee +6

As Video Large Language Models (Video-LLMs) scale to longer and more complex videos, their inference cost grows rapidly due to the large volume of visual tokens accumulated across…

cs.CV2026

CMTM: Cross-Modal Token Modulation for Unsupervised Video Object Segmentation

Inseok Jeon, Suhwan Cho, Minhyeok Lee +6

Recent advances in unsupervised video object segmentation have highlighted the potential of two-stream architectures that integrate appearance and motion cues. However, fully lever…

cs.CV2026

MoRGS: Efficient Per-Gaussian Motion Reasoning for Streamable Dynamic 3D Scenes

Wonjoon Lee, Sungmin Woo, Donghyeong Kim +3

Online reconstruction of dynamic scenes aims to learn from streaming multi-view inputs under low-latency constraints. The fast training and real-time rendering capabilities of 3D G…

cs.CV2026

Revisiting Weakly-Supervised Video Scene Graph Generation via Pair Affinity Learning

Minseok Kang, Minhyeok Lee, Minjung Kim +5

Weakly-supervised video scene graph generation (WS-VSGG) aims to parse video content into structured relational triplets without bounding box annotations and with only sparse tempo…

◍wovepaper

Papers, researchers and institutions, woven together.

Explore
  • Search
  • Researchers
  • Institutions
Account
  • Library
  • Chat
Data
  • arXiv.org
  • Semantic Scholar
  • OpenAlex
  • Latest RSS
AboutContactPrivacyDevelopersllms.txtopenapi.json
Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.