16 citations · 42 across the 13 of their papers we have counts for
7 papers · 1 filter
Solid-SQL: Enhanced Schema-linking based In-context Learning for Robust Text-to-SQL
Geling Liu, Yunzhi Tan, Ruichao Zhong +5
Recently, large language models (LLMs) have significantly improved the performance of text-to-SQL systems. Nevertheless, many state-of-the-art (SOTA) approaches have overlooked the…
Advancing Video Quality Assessment for AIGC
Xinli Yue, Jianhui Sun, Han Kong +9
In recent years, AI generative models have made remarkable progress across various domains, including text generation, image generation, and video generation. However, assessing th…
Revisiting Video Quality Assessment from the Perspective of Generalization
Xinli Yue, Jianhui Sun, Liangchao Yao +8
The increasing popularity of short video platforms such as YouTube Shorts, TikTok, and Kwai has led to a surge in User-Generated Content (UGC), which presents significant challenge…
Zero-Query Adversarial Attack on Black-box Automatic Speech Recognition Systems
Zheng Fang, Tao Wang, Lingchen Zhao +6
In recent years, extensive research has been conducted on the vulnerability of ASR systems, revealing that black-box adversarial example attacks pose significant threats to real-wo…
Boosting Adversarial Transferability with Low-Cost Optimization via Maximin Expected Flatness
Chunlin Qiu, Ang Li, Yiheng Duan +4
Transfer-based attacks craft adversarial examples on white-box surrogate models and directly deploy them against black-box target models, offering model-agnostic and query-free thr…
Revisiting Adversarial Training under Long-Tailed Distributions
Xinli Yue, Ningping Mou, Qian Wang +1
Deep neural networks are vulnerable to adversarial attacks, often leading to erroneous outputs. Adversarial training has been recognized as one of the most effective methods to cou…
Hijacking Attacks against Neural Networks by Analyzing Training Data
Yunjie Ge, Qian Wang, Huayang Huang +7
Backdoors and adversarial examples are the two primary threats currently faced by deep neural networks (DNNs). Both attacks attempt to hijack the model behaviors with unintended ou…