1 code implementation • 15 Dec 2023 • Zixian Su, Jingwei Guo, Kai Yao, Xi Yang, Qiufeng Wang, Kaizhu Huang
While recent test-time adaptations exhibit efficacy by adjusting batch normalization to narrow domain disparities, their effectiveness diminishes with realistic mini-batches due to inaccurate target estimation.
no code implementations • 13 Dec 2023 • Weiguang Zhang, Qiufeng Wang, Kaizhu Huang
While Cartesian coordinates are typically leveraged by state-of-the-art approaches to learn a group of deformation control points, such representation is not efficient for dewarping model to learn the deformation information.
no code implementations • 20 Nov 2023 • Zimu Wang, Wei Wang, Qi Chen, Qiufeng Wang, Anh Nguyen
Deep learning-based natural language processing (NLP) models, particularly pre-trained language models (PLMs), have been revealed to be vulnerable to adversarial attacks.
no code implementations • 25 Oct 2023 • Yiming Lin, Xiao-Bo Jin, Qiufeng Wang, Kaizhu Huang
The current state-of-the-art methods first refine the representation of phrase by aggregating the most similar $k$ image pixels, and then match the refined text representations with the pixels of the image feature map to generate segmentation results.
no code implementations • 4 Sep 2023 • ZiHao Zhou, Qiufeng Wang, Mingyu Jin, Jie Yao, Jianan Ye, Wei Liu, Wei Wang, Xiaowei Huang, Kaizhu Huang
Instead of attacking prompts in the use of LLMs, we propose a MathAttack model to attack MWP samples which are closer to the essence of security in solving math problems.
1 code implementation • 26 Aug 2023 • Jie Yao, ZiHao Zhou, Qiufeng Wang
Firstly, We propose a problem type classifier that combines the strengths of the tree-based solver and the LLM solver.
1 code implementation • 15 Jun 2023 • ZiHao Zhou, Maizhen Ning, Qiufeng Wang, Jie Yao, Wei Wang, Xiaowei Huang, Kaizhu Huang
We then feed them to a question generator together with the scenario to obtain the corresponding diverse questions, forming a new MWP with a variety of questions and equations.
no code implementations • 3 May 2023 • Qiufeng Wang, Xu Yang, Shuxia Lin, Jing Wang, Xin Geng
(i) Accumulating: the knowledge is accumulated during the continuous learning of an ancestry model.
1 code implementation • 27 Nov 2022 • Zixian Su, Kai Yao, Xi Yang, Qiufeng Wang, Jie Sun, Kaizhu Huang
Single-source domain generalization (SDG) in medical image segmentation is a challenging yet essential task as domain shifts are quite common among clinical image datasets.
1 code implementation • 27 Oct 2022 • Zhaorui Tan, Xi Yang, Zihan Ye, Qiufeng Wang, Yuyao Yan, Anh Nguyen, Kaizhu Huang
Generating consistent and high-quality images from given texts is essential for visual-language understanding.
no code implementations • 24 May 2022 • Zixian Su, Kai Yao, Xi Yang, Qiufeng Wang, Yuyao Yan, Jie Sun, Kaizhu Huang
This combination of global and local alignment can precisely localize the crucial regions in segmentation target while preserving the overall semantic consistency.
no code implementations • 24 Jan 2022 • Wei Yuan, Hongzhi Yin, Tieke He, Tong Chen, Qiufeng Wang, Lizhen Cui
To solve the problems, we propose a model named Unified-QG based on lifelong learning techniques, which can continually learn QG tasks across different datasets and formats.
no code implementations • 29 Sep 2021 • Zhuang Qian, Shufei Zhang, Kaizhu Huang, Qiufeng Wang, Bin Gu, Huan Xiong, Xinping Yi
It is possibly due to the fact that the conventional adversarial training methods generate adversarial perturbations usually in a supervised way, so that the adversarial samples are highly biased towards the decision boundary, resulting in an inhomogeneous data distribution.
1 code implementation • 8 Jul 2021 • Zhuang Qian, Shufei Zhang, Kaizhu Huang, Qiufeng Wang, Rui Zhang, Xinping Yi
The proposed adversarial training with latent distribution (ATLD) method defends against adversarial attacks by crafting LMAEs with the latent manifold in an unsupervised manner.
1 code implementation • 12 Jun 2021 • Qiufeng Wang, Xin Geng, Shuxia Lin, Shiyu Xia, Lei Qi, Ning Xu
Moreover, the learngene, i. e., the gene for learning initialization rules of the target model, is proposed to inherit the meta-knowledge from the collective model and reconstruct a lightweight individual model on the target task.
1 code implementation • ICCV 2021 • Zhiqiang Gao, Shufei Zhang, Kaizhu Huang, Qiufeng Wang, Chaoliang Zhong
In particular, we show that the distribution discrepancy can be reduced by constraining feature gradients of two domains to have similar distributions.
no code implementations • 18 Jan 2019 • Zhuang Qian, Kai-Zhu Huang, Qiufeng Wang, Jimin Xiao, Rui Zhang
Generative Adversarial Networks (GAN) receive great attentions recently due to its excellent performance in image generation, transformation, and super-resolution.