5 citations · 9 across the 10 of their papers we have counts for
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T-VSS: Test-Time Visual Subspace Steering for Adversarial Robustness of Vision-Language Models
Jaehyuk Jang, Minseok Seo, Seungju Cho +2
Vision-language models (VLMs) achieve strong zero-shot recognition, but they remain highly vulnerable to adversarial perturbations. Recent test-time adaptations improve robustness…
Long-tailed Adversarial Training with Self-Distillation
Seungju Cho, Hongsin Lee, Changick Kim
Adversarial training significantly enhances adversarial robustness, yet superior performance is predominantly achieved on balanced datasets. Addressing adversarial robustness in th…
Enhancing Robustness in Incremental Learning with Adversarial Training
Seungju Cho, Hongsin Lee, Changick Kim
Adversarial training is one of the most effective approaches against adversarial attacks. However, adversarial training has primarily been studied in scenarios where data for all c…
Indirect Gradient Matching for Adversarial Robust Distillation
Hongsin Lee, Seungju Cho, Changick Kim
Adversarial training significantly improves adversarial robustness, but superior performance is primarily attained with large models. This substantial performance gap for smaller m…
Introducing Competition to Boost the Transferability of Targeted Adversarial Examples through Clean Feature Mixup
Junyoung Byun, Myung-Joon Kwon, Seungju Cho +2
Deep neural networks are widely known to be susceptible to adversarial examples, which can cause incorrect predictions through subtle input modifications. These adversarial example…
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…