no code implementations • 27 Feb 2024 • Yu Ran, Ao-Xiang Zhang, Mingjie Li, Weixuan Tang, Yuan-Gen Wang
Specifically, we first formulate the attack problem as maximizing the deviation between the estimated quality scores of original and perturbed images, while restricting the perturbed image distortions for visual quality preservation.
1 code implementation • NeurIPS 2023 • Ao-Xiang Zhang, Yu Ran, Weixuan Tang, Yuan-Gen Wang
In this paper, we make the first attempt to evaluate the robustness of NR-VQA models against adversarial attacks, and propose a patch-based random search method for black-box attack.
1 code implementation • Findings (ACL) 2021 • Pei Ke, Haozhe Ji, Yu Ran, Xin Cui, LiWei Wang, Linfeng Song, Xiaoyan Zhu, Minlie Huang
Existing pre-trained models for knowledge-graph-to-text (KG-to-text) generation simply fine-tune text-to-text pre-trained models such as BART or T5 on KG-to-text datasets, which largely ignore the graph structure during encoding and lack elaborate pre-training tasks to explicitly model graph-text alignments.
Ranked #1 on KG-to-Text Generation on WebQuestions