activity
20202026
most citedINSURE: An Information Theory Inspired Disentanglement and Purification Model for Domain Generalization

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

collaborators
Showing cs.CVShow all

5 papers · 1 filter

cs.CV2023

AesFA: An Aesthetic Feature-Aware Arbitrary Neural Style Transfer

Joonwoo Kwon, Sooyoung Kim, Yuewei Lin +2

Neural style transfer (NST) has evolved significantly in recent years. Yet, despite its rapid progress and advancement, existing NST methods either struggle to transfer aesthetic i…

cs.CV20231 cited

Exploring Robust Features for Improving Adversarial Robustness

Hong Wang, Yuefan Deng, Shinjae Yoo +1

While deep neural networks (DNNs) have revolutionized many fields, their fragility to carefully designed adversarial attacks impedes the usage of DNNs in safety-critical applicatio…

cs.CV20232 cited

INSURE: An Information Theory Inspired Disentanglement and Purification Model for Domain Generalization

Xi Yu, Huan-Hsin Tseng, Shinjae Yoo +2

Domain Generalization (DG) aims to learn a generalizable model on the unseen target domain by only training on the multiple observed source domains. Although a variety of DG method…

cs.CV2021

AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric Learning

Hong Wang, Yuefan Deng, Shinjae Yoo +2

While deep neural networks have shown impressive performance in many tasks, they are fragile to carefully designed adversarial attacks. We propose a novel adversarial training-base…

cs.CV2020

Transparent Object Tracking Benchmark

Heng Fan, Halady Akhilesha Miththanthaya, Harshit +5

Visual tracking has achieved considerable progress in recent years. However, current research in the field mainly focuses on tracking of opaque objects, while little attention is p…