no code implementations • 31 Mar 2024 • Chen Peng, Zhiqin Qian, Kunyu Wang, Qi Luo, Zhuming Bi, Wenjun Zhang
In the study reported in this paper, based on the well-known hybridization principle, we proposed a method to combine CNN and Transformer to retain the strengths of both, and we applied this method to build a system called MugenNet for colonic polyp image segmentation.
no code implementations • 24 Feb 2024 • Jiazhao Zhang, Kunyu Wang, Rongtao Xu, Gengze Zhou, Yicong Hong, Xiaomeng Fang, Qi Wu, Zhizheng Zhang, He Wang
Vision-and-language navigation (VLN) stands as a key research problem of Embodied AI, aiming at enabling agents to navigate in unseen environments following linguistic instructions.
no code implementations • 5 Dec 2023 • Xiaosen Wang, Kunyu Wang
Moreover, the generated adversarial patches can be disguised as the scrawl or logo in the physical world to fool the deep models without being detected, bringing significant threats to DNNs-enabled applications.
no code implementations • 31 Oct 2023 • Kunyu Wang, Juluan Shi, Wenxuan Wang
In this work, we present a novel approach to generate transferable targeted adversarial examples by exploiting the vulnerability of deep neural networks to perturbations on high-frequency components of images.
2 code implementations • 20 Aug 2023 • Kunyu Wang, Xuanran He, Wenxuan Wang, Xiaosen Wang
In this work, we observe that existing input transformation based attacks, one of the mainstream transfer-based attacks, result in different attention heatmaps on various models, which might limit the transferability.
no code implementations • CVPR 2023 • Chengzhi Cao, Xueyang Fu, Hongjian Liu, Yukun Huang, Kunyu Wang, Jiebo Luo, Zheng-Jun Zha
Video-based person re-identification (Re-ID) is a prominent computer vision topic due to its wide range of video surveillance applications.
Representation Learning Video-Based Person Re-Identification
no code implementations • CVPR 2023 • Kunyu Wang, Xueyang Fu, Yukun Huang, Chengzhi Cao, Gege Shi, Zheng-Jun Zha
This loss enables the network to concentrate on extracting domain-invariant spectrum and domain-specific spectrum, so as to achieve better disentangling results.