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Sunghoon Im

11 papers hereh-index 443 citations13 works total

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

author position
  • middle author1
  • last author9

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

fields
  • cs.CV10
  • cs.LG1
same name
  • Sunghoon Im — 14 papers, h 18
  • Sunghoon Im — 12 papers, h 3
  • Sunghoon Im — 7 papers, h 3
  • Sunghoon Im — 1 paper
  • Sunghoon Im — 1 paper

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 citedDepth-discriminative Metric Learning for Monocular 3D Object Detection

2 citations · 3 across the 10 of their papers we have counts for

collaborators
Showing 2024Show all

4 papers · 1 filter

cs.CV2024

Style-Editor: Text-driven object-centric style editing

Jihun Park, Jongmin Gim, Kyoungmin Lee +2

We present Text-driven object-centric style editing model named Style-Editor, a novel method that guides style editing at an object-centric level using textual inputs. The core of…

cs.CV2024

CAVIS: Context-Aware Video Instance Segmentation

Seunghun Lee, Jiwan Seo, Kiljoon Han +2

In this paper, we introduce the Context-Aware Video Instance Segmentation (CAVIS), a novel framework designed to enhance instance association by integrating contextual information…

cs.CV2024

Multi-task Learning for Real-time Autonomous Driving Leveraging Task-adaptive Attention Generator

Wonhyeok Choi, Mingyu Shin, Hyukzae Lee +3

Real-time processing is crucial in autonomous driving systems due to the imperative of instantaneous decision-making and rapid response. In real-world scenarios, autonomous vehicle…

cs.CV2024★ 2 cited

Depth-discriminative Metric Learning for Monocular 3D Object Detection

Wonhyeok Choi, Mingyu Shin, Sunghoon Im

Monocular 3D object detection poses a significant challenge due to the lack of depth information in RGB images. Many existing methods strive to enhance the object depth estimation…

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