no code implementations • 15 Nov 2023 • Yuanwei Wu, Xiang Li, Yixin Liu, Pan Zhou, Lichao Sun
This finding indicates potential exploitable security risks in MLLMs; 2) Based on the acquired system prompts, we propose a novel MLLM jailbreaking attack method termed SASP (Self-Adversarial Attack via System Prompt).
1 code implementation • 5 Apr 2021 • Yuanwei Wu, Ziming Zhang, Guanghui Wang
In this paper, we propose BPGrad, a novel approximate algorithm for deep nueral network training, based on adaptive estimates of feasible region via branch-and-bound.
no code implementations • 5 Jan 2020 • Ziming Zhang, Wenchi Ma, Yuanwei Wu, Guanghui Wang
In this paper, we investigate the empirical impact of orthogonality regularization (OR) in deep learning, either solo or collaboratively.
no code implementations • 10 Dec 2019 • Wenchi Ma, Yuanwei Wu, Feng Cen, Guanghui Wang
Compared with features produced in earlier layers, the deep features are better at expressing semantic and contextual information.
no code implementations • 4 Dec 2019 • Kaidong Li, Wenchi Ma, Usman Sajid, Yuanwei Wu, Guanghui Wang
In this chapter, we present a brief overview of the recent development in object detection using convolutional neural networks (CNN).
no code implementations • arXiv 2019 • Wenju Xu, Yuanwei Wu, Wenchi Ma, Guanghui Wang
In this paper, we address the problem of weakly supervisedobject localization (WSL), which trains a detection network on the datasetwith only image-level annotations.
no code implementations • 4 Oct 2019 • Wenju Xu, Yuanwei Wu, Wenchi Ma, Guanghui Wang
In this paper, we address the problem of weakly supervised object localization (WSL), which trains a detection network on the dataset with only image-level annotations.
no code implementations • 27 Aug 2019 • Yuanwei Wu, Ziming Zhang, Guanghui Wang
We use pre-trained convenet to extract features for both high- and low-resolution images, and then feed them into a two-layer feature transfer network for knowledge transfer.
no code implementations • 6 Sep 2018 • Wenchi Ma, Yuanwei Wu, Zongbo Wang, Guanghui Wang
To better handle these challenges, the paper proposes a novel framework, multi-scale, deep inception convolutional neural network (MDCN), which focuses on wider and broader object regions by activating feature maps produced in the deep part of the network.
no code implementations • CVPR 2018 • Ziming Zhang, Yuanwei Wu, Guanghui Wang
Understanding the global optimality in deep learning (DL) has been attracting more and more attention recently.
no code implementations • 19 Mar 2017 • Yuanwei Wu, Yao Sui, Guanghui Wang
The paper focuses on the problem of vision-based obstacle detection and tracking for unmanned aerial vehicle navigation.