no code implementations • 26 May 2024 • Runlin Lei, Yuwei Hu, Yuchen Ren, Zhewei Wei
In this paper, we pioneer the exploration of GIAs at the text level, presenting three novel attack designs that inject textual content into the graph.
no code implementations • 11 Oct 2023 • Chaoqi Liang, Weiqiang Bai, Lifeng Qiao, Yuchen Ren, Jianle Sun, Peng Ye, Hongliang Yan, Xinzhu Ma, WangMeng Zuo, Wanli Ouyang
To address this research gap, we first conducted a series of exploratory experiments and gained several insightful observations: 1) In the fine-tuning phase of downstream tasks, when using K-mer overlapping tokenization instead of K-mer non-overlapping tokenization, both overlapping and non-overlapping pretraining weights show consistent performance improvement. 2) During the pre-training process, using K-mer overlapping tokenization quickly produces clear K-mer embeddings and reduces the loss to a very low level, while using K-mer non-overlapping tokenization results in less distinct embeddings and continuously decreases the loss.
no code implementations • 26 May 2023 • Zhangyin Feng, Yuchen Ren, Xinmiao Yu, Xiaocheng Feng, Duyu Tang, Shuming Shi, Bing Qin
Diffusion models developed on top of powerful text-to-image generation models like Stable Diffusion achieve remarkable success in visual story generation.
2 code implementations • ICCV 2023 • Hegui Zhu, Yuchen Ren, Xiaoyan Sui, Lianping Yang, Wuming Jiang
Plentiful adversarial attack researches have revealed the fragility of deep neural networks (DNNs), where the imperceptible perturbations can cause drastic changes in the output.
no code implementations • CVPR 2023 • Yuchen Ren, Zhendong Mao, Shancheng Fang, Yan Lu, Tong He, Hao Du, Yongdong Zhang, Wanli Ouyang
In this paper, we introduce a new setting called Domain Generalization for Image Captioning (DGIC), where the data from the target domain is unseen in the learning process.
no code implementations • 20 Nov 2021 • Zhaoyang Hai, Xiabi Liu, Yuchen Ren, Nouman Q. Soomro
In this paper, we propose a meta-learning approach based on randomly generated meta-learning tasks to obtain a parametric loss for classification learning based on big data.