no code implementations • 9 May 2024 • Peng-Fei Zhang, Zi Huang, Guangdong Bai
To this end, we propose a novel black-box method to generate Universal Adversarial Perturbations (UAPs), which is so called the Effective and T ransferable Universal Adversarial Attack (ETU), aiming to mislead a variety of existing VLP models in a range of downstream tasks.
no code implementations • 7 May 2024 • Peng-Fei Zhang, Zi Huang, Xin-Shun Xu, Guangdong Bai
We propose a hybrid adversarial training surrounding multiple potential adversarial perturbations, alongside a semi-supervised learning based on class- rebalancing sample selection to enhance the resilience of the model for dual corruption.
1 code implementation • 12 Sep 2023 • Yan Jiang, Ruihong Qiu, Yi Zhang, Peng-Fei Zhang
Furthermore, an LLMs explanation mechanism is proposed by prompting an LLM with the predicted results from BERT models.
no code implementations • 28 Oct 2022 • Yan Wang, Xin Luo, Zhen-Duo Chen, Peng-Fei Zhang, Meng Liu, Xin-Shun Xu
As the first that is explored in VMR field, the new task is defined as video moment retrieval with distributed data.
no code implementations • 22 Oct 2022 • Yang Li, Tong Chen, Peng-Fei Zhang, Zi Huang, Hongzhi Yin
In order to counteract the scarcity and incompleteness of POI check-ins, we propose a novel self-supervised learning paradigm in \ssgrec, where the trajectory representations are contrastively learned from two augmented views on geolocations and temporal transitions.
1 code implementation • 11 Jul 2022 • Zixin Wang, Yadan Luo, Peng-Fei Zhang, Sen Wang, Zi Huang
A typical multi-source domain adaptation (MSDA) approach aims to transfer knowledge learned from a set of labeled source domains, to an unlabeled target domain.
no code implementations • 25 Aug 2021 • Yang Li, Tong Chen, Peng-Fei Zhang, Hongzhi Yin
Modern deep neural networks (DNNs) have greatly facilitated the development of sequential recommender systems by achieving state-of-the-art recommendation performance on various sequential recommendation tasks.