1 code implementation • 26 Nov 2023 • Sitong Liu, Zhichao Lian, Shuangquan Zhang, Liang Xiao
Notably, the residual perturbations on the purified image primarily stem from the same-position patch and similar patches of the adversarial sample.
no code implementations • 18 Nov 2023 • Tao Wang, Zijian Ying, Qianmu Li, Zhichao Lian
To address these challenges, we propose a framework called Uniform Scale and Mix Mask Method (US-MM) for adversarial example generation.
no code implementations • 28 Jun 2022 • Sartaj Ahmed Salman, Zhichao Lian, Milad Taleby Ahvanooey, Hiroki Takahashi, Yuduo Zhang
However, in practice, exploring interactions in brain functional connectivity based on operational magnetic resonance imaging data is critical for studying mental illness.
no code implementations • 31 May 2022 • Yating Ma, Zhichao Lian
We use a dot product-based approach to add the denoising module to ResNet18 and the attention mechanism to further improve the robustness of the model.
no code implementations • 15 May 2022 • Ruiqi Zha, Zhichao Lian, Qianmu Li, Siqi Gu
Essentially, the target of deepfake detection problem is to represent natural faces and fake faces at the representation space discriminatively, and it reminds us whether we could optimize the feature extraction procedure at the representation space through constraining intra-class consistence and inter-class inconsistence to bring the intra-class representations close and push the inter-class representations apart?
no code implementations • 2 May 2022 • Zijian Ying, Qianmu Li, Zhichao Lian, Jun Hou, Tong Lin, Tao Wang
To organize these excitations into final saliency maps, we introduce a double-chain backpropagation procedure.
1 code implementation • 18 Feb 2022 • Sartaj Ahmed Salman, Zhichao Lian, Marva Saleem, Yuduo Zhang
To validate our approach, fMRI data of 143 normal and 100 ADHD affected children is used for experimental purpose.
no code implementations • 8 Feb 2022 • Siqi Gu, Zhichao Lian
In this paper, a novel Unified Multi-Task Learning Framework of Real-Time Drone Supervision for Crowd Counting (MFCC) is proposed, which utilizes an image fusion network architecture to fuse images from the visible and thermal infrared image, and a crowd counting network architecture to estimate the density map.
1 code implementation • 6 Feb 2022 • Sitong Liu, Zhichao Lian, Siqi Gu, Liang Xiao
Finally, we restore the spatial layout of the blocks to capture the semantic associations among them.
no code implementations • 13 Aug 2020 • Jie Song, Liang Xiao, Mohsen Molaei, Zhichao Lian
In this way, rich image appearance models together with more contextual information are integrated by learning a series of decision tree ensembles.