activity
20192022
most citedImproving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Input

5 citations · 9 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV20221 cited

RainUNet for Super-Resolution Rain Movie Prediction under Spatio-temporal Shifts

Jinyoung Park, Minseok Son, Seungju Cho +2

This paper presents a solution to the Weather4cast 2022 Challenge Stage 2. The goal of the challenge is to forecast future high-resolution rainfall events obtained from ground rada…

cs.CV20225 cited

Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Input

Junyoung Byun, Seungju Cho, Myung-Joon Kwon +2

The transferability of adversarial examples allows the deception on black-box models, and transfer-based targeted attacks have attracted a lot of interest due to their practical ap…

cs.CV20221 cited

Balancing Domain Experts for Long-Tailed Camera-Trap Recognition

Byeongjun Park, Jeongsoo Kim, Seungju Cho +2

Label distributions in camera-trap images are highly imbalanced and long-tailed, resulting in neural networks tending to be biased towards head-classes that appear frequently. Alth…

cs.CV20202 cited

Applying Tensor Decomposition to image for Robustness against Adversarial Attack

Seungju Cho, Tae Joon Jun, Mingu Kang +1

Nowadays the deep learning technology is growing faster and shows dramatic performance in computer vision areas. However, it turns out a deep learning based model is highly vulnera…

cs.CV2019

DAPAS : Denoising Autoencoder to Prevent Adversarial attack in Semantic Segmentation

Seungju Cho, Tae Joon Jun, Byungsoo Oh +1

Nowadays, Deep learning techniques show dramatic performance on computer vision area, and they even outperform human. But it is also vulnerable to some small perturbation called an…