most citedSelf-supervised Adversarial Training of Monocular Depth Estimation against Physical-World Attacks

15 citations · 21 across the 9 of their papers we have counts for

collaborators

9 papers

eess.AS2024

Binaural Selective Attention Model for Target Speaker Extraction

Hanyu Meng, Qiquan Zhang, Xiangyu Zhang +2

The remarkable ability of humans to selectively focus on a target speaker in cocktail party scenarios is facilitated by binaural audio processing. In this paper, we present a binau…

cs.CR2024

RL-JACK: Reinforcement Learning-powered Black-box Jailbreaking Attack against LLMs

Xuan Chen, Yuzhou Nie, Lu Yan +3

Modern large language model (LLM) developers typically conduct a safety alignment to prevent an LLM from generating unethical or harmful content. Recent studies have discovered tha…

cs.SE2024

CodeScore-R: An Automated Robustness Metric for Assessing the FunctionalCorrectness of Code Synthesis

Guang Yang, Yu Zhou, Xiang Chen +1

Evaluation metrics are crucial in the field of code synthesis. Commonly used code evaluation metrics canbe classified into three types: match-based, semantic-based, and execution-b…

cs.CV202415 cited

Self-supervised Adversarial Training of Monocular Depth Estimation against Physical-World Attacks

Zhiyuan Cheng, Cheng Han, James Liang +3

Monocular Depth Estimation (MDE) plays a vital role in applications such as autonomous driving. However, various attacks target MDE models, with physical attacks posing significant…

cs.CR20236 cited

Opening A Pandora's Box: Things You Should Know in the Era of Custom GPTs

Guanhong Tao, Siyuan Cheng, Zhuo Zhang +3

The emergence of large language models (LLMs) has significantly accelerated the development of a wide range of applications across various fields. There is a growing trend in the c…

cs.IR2023

Cold & Warm Net: Addressing Cold-Start Users in Recommender Systems

Xiangyu Zhang, Zongqiang Kuang, Zehao Zhang +2

Cold-start recommendation is one of the major challenges faced by recommender systems (RS). Herein, we focus on the user cold-start problem. Recently, methods utilizing side inform…