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In-So Kweon

62 papers hereh-index 6642.5k citations394 works total

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

author position
  • middle author15
  • last author44

Across the 59 of 62 papers where every author was matched, so the position is known.

fields
  • cs.CV56
  • cs.LG3
  • cs.RO3
same name
  • In-So Kweon — 36 papers, h 28
  • In-So Kweon — 3 papers, h 4
  • In-So Kweon — 2 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
20142026
most citedCBAM: Convolutional Block Attention Module

336 citations · 854 across the 53 of their papers we have counts for

collaborators
Showing 2024Show all

4 papers · 1 filter

cs.CV2024

360 in the Wild: Dataset for Depth Prediction and View Synthesis

Kibaek Park, Francois Rameau, Jaesik Park +1

The large abundance of perspective camera datasets facilitated the emergence of novel learning-based strategies for various tasks, such as camera localization, single image depth e…

cs.CV2024

Exploring the Spectrum of Visio-Linguistic Compositionality and Recognition

Youngtaek Oh, Pyunghwan Ahn, Jinhyung Kim +4

Vision and language models (VLMs) such as CLIP have showcased remarkable zero-shot recognition abilities yet face challenges in visio-linguistic compositionality, particularly in l…

cs.CV2024

Enhancing Temporal Consistency in Video Editing by Reconstructing Videos with 3D Gaussian Splatting

Inkyu Shin, Qihang Yu, Xiaohui Shen +3

Recent advancements in zero-shot video diffusion models have shown promise for text-driven video editing, but challenges remain in achieving high temporal consistency. To address t…

cs.LG2024★ 1 cited

Towards Understanding Dual BN In Hybrid Adversarial Training

Chenshuang Zhang, Chaoning Zhang, Kang Zhang +3

There is a growing concern about applying batch normalization (BN) in adversarial training (AT), especially when the model is trained on both adversarial samples and clean samples…

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