activity
20222026
most citedFairness-aware Adversarial Perturbation Towards Bias Mitigation for Deployed Deep Models

4 citations · 6 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2026

The First Challenge on Mobile Real-World Image Super-Resolution at NTIRE 2026: Benchmark Results and Method Overview

Jiatong Li, Zheng Chen, Kai Liu +91

This paper provides a review of the NTIRE 2026 challenge on mobile real-world image super-resolution, highlighting the proposed solutions and the resulting outcomes. The challenge…

cs.CV2025

Boost Self-Supervised Dataset Distillation via Parameterization, Predefined Augmentation, and Approximation

Sheng-Feng Yu, Jia-Jiun Yao, Wei-Chen Chiu

Although larger datasets are crucial for training large deep models, the rapid growth of dataset size has brought a significant challenge in terms of considerable training costs, w…

cs.LG2024★ 1 cited

Red-Teaming Text-to-Image Models via In-Context Experience Replay and Semantic-Preserving Prompt Rewriting

Zhi-Yi Chin, Pin-Yu Chen, Wei-Chen Chiu +1

Understanding the capabilities of text-to-image (T2I) models in harmful content generation is essential to safety and compliance. However, human red-teaming is costly and inconsist…

cs.CV2024★ 1 cited

Realizing Video Summarization from the Path of Language-based Semantic Understanding

Kuan-Chen Mu, Zhi-Yi Chin, Wei-Chen Chiu

The recent development of Video-based Large Language Models (VideoLLMs), has significantly advanced video summarization by aligning video features and, in some cases, audio feature…

cs.LG2022★ 4 cited

Fairness-aware Adversarial Perturbation Towards Bias Mitigation for Deployed Deep Models

Zhibo Wang, Xiaowei Dong, Henry Xue +4

Prioritizing fairness is of central importance in artificial intelligence (AI) systems, especially for those societal applications, e.g., hiring systems should recommend applicants…