1 code implementation • 5 Jun 2024 • Jun Liu, Jiantao Zhou, Jiandian Zeng, Jinyu Tian
This work investigates efficient score-based black-box adversarial attacks with a high Attack Success Rate (ASR) and good generalizability.
no code implementations • 26 Apr 2024 • Yuanman Li, Yingjie He, Changsheng chen, Li Dong, Bin Li, Jiantao Zhou, Xia Li
To address these limitations, this study proposes a novel end-to-end CMFD framework that integrates the strengths of conventional and deep learning methods.
no code implementations • 7 Jan 2024 • Rongqin Liang, Yuanman Li, Jiantao Zhou, Xia Li
Traffic anomaly detection (TAD) in driving videos is critical for ensuring the safety of autonomous driving and advanced driver assistance systems.
1 code implementation • 31 Dec 2023 • Guanyiman Fu, Fengchao Xiong, Jianfeng Lu, Jun Zhou, Jiantao Zhou, Yuntao Qian
This block consists of a spatial branch and a spectral branch.
1 code implementation • 20 Dec 2023 • Yiming Chen, Haiwei Wu, Jiantao Zhou
Extensive experiments on multiple benchmark datasets and DNN models, assessed against nine state-of-the-art backdoor attacks, demonstrate the superior performance of our PIPD method for backdoor defense.
no code implementations • 14 Dec 2023 • Shuning Xu, Binbin Song, Xiangyu Chen, Xina Liu, Jiantao Zhou
Moire patterns frequently appear when capturing screens with smartphones or cameras, potentially compromising image quality.
no code implementations • 11 Dec 2023 • Fengpeng Li, Kemou Li, Jinyu Tian, Jiantao Zhou
The deep model training procedure requires large-scale datasets of annotated data.
1 code implementation • 11 Dec 2023 • Yuzhou Huang, Liangbin Xie, Xintao Wang, Ziyang Yuan, Xiaodong Cun, Yixiao Ge, Jiantao Zhou, Chao Dong, Rui Huang, Ruimao Zhang, Ying Shan
Both quantitative and qualitative results on this evaluation dataset indicate that our SmartEdit surpasses previous methods, paving the way for the practical application of complex instruction-based image editing.
1 code implementation • 19 Oct 2023 • Jun Liu, Jiantao Zhou, Jinyu Tian, Weiwei Sun
Extensive experiments demonstrate that 1) the classification accuracy of the classifier trained in the plaintext domain remains the same in both the ciphertext and plaintext domains; 2) the encrypted images can be recovered into their original form with an average PSNR of up to 51+ dB for the SVHN dataset and 48+ dB for the VGGFace2 dataset; 3) our system exhibits satisfactory generalization capability on the encryption, decryption and classification tasks across datasets that are different from the training one; and 4) a high-level of security is achieved against three potential threat models.
1 code implementation • 19 Oct 2023 • Jun Liu, Jiantao Zhou, Haiwei Wu, Weiwei Sun, Jinyu Tian
In this work, we aim to design a new framework for generating robust AEs that can survive the OSN transmission; namely, the AEs before and after the OSN transmission both possess strong attack capabilities.
1 code implementation • 18 Oct 2023 • Xiangyu Chen, Zheyuan Li, Yuandong Pu, Yihao Liu, Jiantao Zhou, Yu Qiao, Chao Dong
Following this, we present the benchmark results and analyze the reasons behind the performance disparity of different models across various tasks.
no code implementations • 16 Oct 2023 • Yihao Liu, Xiangyu Chen, Xianzheng Ma, Xintao Wang, Jiantao Zhou, Yu Qiao, Chao Dong
To address this issue, we propose a universal model for general image processing that covers image restoration, image enhancement, image feature extraction tasks, etc.
2 code implementations • 11 Sep 2023 • Xiangyu Chen, Xintao Wang, Wenlong Zhang, Xiangtao Kong, Yu Qiao, Jiantao Zhou, Chao Dong
In the training stage, we additionally adopt a same-task pre-training strategy to further exploit the potential of the model for further improvement.
2 code implementations • 8 Sep 2023 • Xiangyu Chen, Zheyuan Li, Zhengwen Zhang, Jimmy S. Ren, Yihao Liu, Jingwen He, Yu Qiao, Jiantao Zhou, Chao Dong
However, the majority of available resources are still in standard dynamic range (SDR).
1 code implementation • 8 Sep 2023 • Shuning Xu, Xiangyu Chen, Binbin Song, Jiantao Zhou
Capturing images with incorrect exposure settings fails to deliver a satisfactory visual experience.
1 code implementation • 25 Aug 2023 • Shuning Xu, Binbin Song, Xiangyu Chen, Jiantao Zhou
In TDR, we propose a temporal-guided bilateral learning pipeline to mitigate the degradation of color and details caused by the moire patterns while preserving the restored frequency information in FDDA.
1 code implementation • 18 Aug 2023 • Haiwei Wu, Yiming Chen, Jiantao Zhou
To resolve this dilemma, we propose the FOrensic ContrAstive cLustering (FOCAL) method, a novel, simple yet very effective paradigm based on contrastive learning and unsupervised clustering for the image forgery detection.
1 code implementation • ICCV 2023 • Binbin Song, Xiangyu Chen, Shuning Xu, Jiantao Zhou
With the physical model of the scattering effect, we improve the image formation pipeline for the image synthesis to construct a realistic UDC dataset with ground truths.
no code implementations • 27 Jul 2023 • Rongqin Liang, Yuanman Li, Yingxin Yi, Jiantao Zhou, Xia Li
Different from previous approaches, our method can more accurately detect both ego-involved and non-ego accidents by simultaneously modeling appearance changes and object motions in video frames through the collaboration of optical flow reconstruction and future object localization tasks.
1 code implementation • 5 Jul 2023 • Liangbin Xie, Xintao Wang, Xiangyu Chen, Gen Li, Ying Shan, Jiantao Zhou, Chao Dong
After detecting the artifact regions, we develop a finetune procedure to improve GAN-based SR models with a few samples, so that they can deal with similar types of artifacts in more unseen real data.
no code implementations • 1 Jul 2023 • Ruijie Yang, Yuanfang Guo, Junfu Wang, Jiantao Zhou, Yunhong Wang
Specifically, to reduce the model-specific features and obtain better output distributions, we construct a multi-teacher framework, where the knowledge is distilled from different teacher architectures into one student network.
1 code implementation • 23 May 2023 • Haiwei Wu, Jiantao Zhou, Shile Zhang
In this work, we propose a simple yet very effective synthetic image detection method via a language-guided contrastive learning and a new formulation of the detection problem.
no code implementations • 21 Nov 2022 • Rongqin Liang, Yuanman Li, Jiantao Zhou, Xia Li
Different from previous approaches, our method can more precisely model the underlying data distribution by optimizing the exact log-likelihood of motion behaviors.
1 code implementation • 6 Jul 2022 • Wenjie Li, Juncheng Li, Guangwei Gao, Jiantao Zhou, Jian Yang, Guo-Jun Qi
Recently, Transformer-based methods have shown impressive performance in single image super-resolution (SISR) tasks due to the ability of global feature extraction.
1 code implementation • 30 May 2022 • Jinhui Hou, Zhiyu Zhu, Junhui Hou, Huanqiang Zeng, Jinjian Wu, Jiantao Zhou
Then, we incorporate the proposed feature embedding scheme into a source-consistent super-resolution framework that is physically-interpretable, producing lightweight PDE-Net, in which high-resolution (HR) HS images are iteratively refined from the residuals between input low-resolution (LR) HS images and pseudo-LR-HS images degenerated from reconstructed HR-HS images via probability-inspired HS embedding.
no code implementations • 25 May 2022 • Eduardo Pérez-Pellitero, Sibi Catley-Chandar, Richard Shaw, Aleš Leonardis, Radu Timofte, Zexin Zhang, Cen Liu, Yunbo Peng, Yue Lin, Gaocheng Yu, Jin Zhang, Zhe Ma, Hongbin Wang, Xiangyu Chen, Xintao Wang, Haiwei Wu, Lin Liu, Chao Dong, Jiantao Zhou, Qingsen Yan, Song Zhang, Weiye Chen, Yuhang Liu, Zhen Zhang, Yanning Zhang, Javen Qinfeng Shi, Dong Gong, Dan Zhu, Mengdi Sun, Guannan Chen, Yang Hu, Haowei Li, Baozhu Zou, Zhen Liu, Wenjie Lin, Ting Jiang, Chengzhi Jiang, Xinpeng Li, Mingyan Han, Haoqiang Fan, Jian Sun, Shuaicheng Liu, Juan Marín-Vega, Michael Sloth, Peter Schneider-Kamp, Richard Röttger, Chunyang Li, Long Bao, Gang He, Ziyao Xu, Li Xu, Gen Zhan, Ming Sun, Xing Wen, Junlin Li, Shuang Feng, Fei Lei, Rui Liu, Junxiang Ruan, Tianhong Dai, Wei Li, Zhan Lu, Hengyan Liu, Peian Huang, Guangyu Ren, Yonglin Luo, Chang Liu, Qiang Tu, Fangya Li, Ruipeng Gang, Chenghua Li, Jinjing Li, Sai Ma, Chenming Liu, Yizhen Cao, Steven Tel, Barthelemy Heyrman, Dominique Ginhac, Chul Lee, Gahyeon Kim, Seonghyun Park, An Gia Vien, Truong Thanh Nhat Mai, Howoon Yoon, Tu Vo, Alexander Holston, Sheir Zaheer, Chan Y. Park
The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).
2 code implementations • CVPR 2023 • Xiangyu Chen, Xintao Wang, Jiantao Zhou, Yu Qiao, Chao Dong
In the training stage, we additionally adopt a same-task pre-training strategy to exploit the potential of the model for further improvement.
Ranked #1 on Image Super-Resolution on Set5 - 2x upscaling
1 code implementation • 28 Apr 2022 • Jiandian Zeng, Tianyi Liu, Jiantao Zhou
Specifically, we design a tag encoding module to cover both the single modality and multiple modalities missing cases, so as to guide the network's attention to those missing modalities.
1 code implementation • 17 Mar 2022 • Zhiyuan Zha, Bihan Wen, Xin Yuan, Saiprasad Ravishankar, Jiantao Zhou, Ce Zhu
Furthermore, we present a unified framework for incorporating various GSR and LR models and discuss the relationship between GSR and LR models.
1 code implementation • 2 Mar 2022 • Shuren Qi, Yushu Zhang, Chao Wang, Jiantao Zhou, Xiaochun Cao
Image forensics is a rising topic as the trustworthy multimedia content is critical for modern society.
1 code implementation • CVPR 2022 • Haiwei Wu, Jiantao Zhou, Jinyu Tian, Jun Liu
To fight against the OSN-shared forgeries, in this work, a novel robust training scheme is proposed.
no code implementations • CVPR 2021 • Jinyu Tian, Jiantao Zhou, Jia Duan
Model protection is vital when deploying Convolutional Neural Networks (CNNs) for commercial services, due to the massive costs of training them.
no code implementations • NeurIPS 2021 • Zhaoxi Zhang, Leo Yu Zhang, Xufei Zheng, Jinyu Tian, Jiantao Zhou
To alleviate this problem, we explore how to detect adversarial examples with disentangled label/semantic features under the autoencoder structure.
1 code implementation • 27 Mar 2021 • Shuren Qi, Yushu Zhang, Chao Wang, Jiantao Zhou, Xiaochun Cao
Image representation is an important topic in computer vision and pattern recognition.
1 code implementation • 7 Mar 2021 • Jinyu Tian, Jiantao Zhou, Yuanman Li, Jia Duan
Deep neural networks (DNNs) have been shown to be vulnerable against adversarial examples (AEs), which are maliciously designed to cause dramatic model output errors.
1 code implementation • 19 Jan 2021 • Haiwei Wu, Jiantao Zhou
The proposed GIID-Net consists of three sub-blocks: the enhancement block, the extraction block and the decision block.
no code implementations • 3 Dec 2020 • Fengchao Xiong, Shuyin Tao, Jun Zhou, Jianfeng Lu, Jiantao Zhou, Yuntao Qian
This model first projects the observed HSIs into a low-dimensional orthogonal subspace, and then represents the projected image with a multidimensional dictionary.
1 code implementation • 3 Dec 2020 • Rongqin Liang, Yuanman Li, Xia Li, Yi Tang, Jiantao Zhou, Wenbin Zou
Predicting human motion behavior in a crowd is important for many applications, ranging from the natural navigation of autonomous vehicles to intelligent security systems of video surveillance.
Ranked #14 on Trajectory Prediction on ETH/UCY
1 code implementation • 2 Sep 2020 • Haiwei Wu, Jiantao Zhou, Yuanman Li
Deep learning (DL) has demonstrated its powerful capabilities in the field of image inpainting.
1 code implementation • 2 Sep 2020 • Haiwei Wu, Jiantao Zhou
It is shown that, if the adversary can only have access to the SIFT descriptors while not their coordinates, then the modest success of reconstructing the latent image can be achieved for highly-structured images (e. g., faces) and would fail in general settings.
1 code implementation • 18 Jun 2020 • Zhiyu Zhu, Junhui Hou, Jie Chen, Huanqiang Zeng, Jiantao Zhou
Specifically, PZRes-Net learns a high resolution and \textit{zero-centric} residual image, which contains high-frequency spatial details of the scene across all spectral bands, from both inputs in a progressive fashion along the spectral dimension.
Hyperspectral Image Super-Resolution Hyperspectral Unmixing +1
no code implementations • 16 May 2020 • Zhiyuan Zha, Xin Yuan, Joey Tianyi Zhou, Jiantao Zhou, Bihan Wen, Ce Zhu
In this paper, we propose a joint low-rank and deep (LRD) image model, which contains a pair of triply complementary priors, namely \textit{external} and \textit{internal}, \textit{deep} and \textit{shallow}, and \textit{local} and \textit{non-local} priors.
no code implementations • 12 Dec 2019 • Yucheng Zhu, Xiongkuo Min, Dandan Zhu, Ke Gu, Jiantao Zhou, Guangtao Zhai, Xiaokang Yang, Wenjun Zhang
The saliency annotations of head and eye movements for both original and augmented videos are collected and together constitute the ARVR dataset.
1 code implementation • 6 Jul 2018 • Zhiyuan Zha, Xin Yuan, Bihan Wen, Jiantao Zhou, Jiachao Zhang, Ce Zhu
Towards this end, we first obtain a good reference of the original image groups by using the image NSS prior, and then the rank residual of the image groups between this reference and the degraded image is minimized to achieve a better estimate to the desired image.
no code implementations • 12 Sep 2017 • Zhiyuan Zha, Xin Yuan, Bihan Wen, Jiantao Zhou, Jiachao Zhang, Ce Zhu
Sparse coding has achieved a great success in various image processing tasks.
no code implementations • 16 Aug 2016 • Zhiyuan Zha, Bihan Wen, Jiachao Zhang, Jiantao Zhou, Ce Zhu
Inspired by enhancing sparsity of the weighted L1-norm minimization in comparison with L1-norm minimization in sparse representation, we thus explain that WNNM is more effective than NMM.
no code implementations • CVPR 2015 • Xianming Liu, Xiaolin Wu, Jiantao Zhou, Debin Zhao
Arguably the most common cause of image degradation is compression.