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Sangwoo Mo

KAIST

11 papers hereh-index 172.4k citations32 works total

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

author position
  • first author4
  • middle author6

Across the 10 of 11 papers where every author was matched, so the position is known.

fields
  • cs.LG7
  • cs.CV4
affiliations
  • KAIST
Homepage
same name
  • Sangwoo Mo — 5 papers, h 2
  • Sangwoo Mo — 3 papers

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
20192025
most citedFreeze the Discriminator: a Simple Baseline for Fine-Tuning GANs

121 citations · 288 across the 10 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2025

Rethinking Prompt Design for Inference-time Scaling in Text-to-Visual Generation

Subin Kim, Sangwoo Mo, Mamshad Nayeem Rizve +4

Achieving precise alignment between user intent and generated visuals remains a central challenge in text-to-visual generation, as a single attempt often fails to produce the desir…

cs.CV2022★ 27 cited

Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks

Sihyun Yu, Jihoon Tack, Sangwoo Mo +4

In the deep learning era, long video generation of high-quality still remains challenging due to the spatio-temporal complexity and continuity of videos. Existing prior works have…

cs.CV2021★ 16 cited

Object-aware Contrastive Learning for Debiased Scene Representation

Sangwoo Mo, Hyunwoo Kang, Kihyuk Sohn +2

Contrastive self-supervised learning has shown impressive results in learning visual representations from unlabeled images by enforcing invariance against different data augmentati…

cs.CV2020★ 121 cited

Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs

Sangwoo Mo, Minsu Cho, Jinwoo Shin

Generative adversarial networks (GANs) have shown outstanding performance on a wide range of problems in computer vision, graphics, and machine learning, but often require numerous…

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