1 code implementation • 5 Feb 2025 • Peng Huang, Shu Hu, Bo Peng, Jiashu Zhang, Hongtu Zhu, Xi Wu, Xin Wang
To address these limitations, we propose AutoMedSAM, an end-to-end framework derived from SAM, designed to enhance usability and segmentation performance.
no code implementations • 21 Jan 2025 • ShiXuan Song, Hao Chen, Shu Hu, Xin Wang, Jinrong Hu, Xi Wu
Visual anomaly detection is a highly challenging task, often categorized as a one-class classification and segmentation problem.
no code implementations • 7 Jan 2025 • Zhenghao Feng, Lu Wen, Yuanyuan Xu, Binyu Yan, Xi Wu, Jiliu Zhou, Yan Wang
Additionally, based on a mean teacher framework, we elaborately design a balanced subclass regularization to utilize the teacher predictions of SCS task to supervise the student predictions of MoS task, thus effectively transferring unbiased knowledge to the MoS subnetwork and alleviating the influence of the class-imbalance problem.
no code implementations • 31 Dec 2024 • Hang Yang, Hao Chen, Hui Guo, Yineng Chen, Ching-Sheng Lin, Shu Hu, Jinrong Hu, Xi Wu, Xin Wang
Accurate and efficient question-answering systems are essential for delivering high-quality patient care in the medical field.
no code implementations • 8 Dec 2024 • Hao Chen, Hui Guo, Baochen Hu, Shu Hu, Jinrong Hu, Siwei Lyu, Xi Wu, Xin Wang
The rapid growth of social media has resulted in an explosion of online news content, leading to a significant increase in the spread of misleading or false information.
no code implementations • 30 Aug 2024 • Yuxiang Yang, Xinyi Zeng, Pinxian Zeng, Binyu Yan, Xi Wu, Jiliu Zhou, Yan Wang
To address these limitations, unsupervised domain adaptation (UDA) methods have been used to transfer knowledge from one labeled source domain to the unlabeled target domain, yet these approaches suffer from severe domain shift issues and often ignore the potential benefits of leveraging multiple relevant sources in practical applications.
Multi-Source Unsupervised Domain Adaptation
Unsupervised Domain Adaptation
no code implementations • 26 Aug 2024 • Lu Wen, Wenxia Yin, Zhenghao Feng, Xi Wu, Deng Xiong, Yan Wang
Radiation therapy is the mainstay treatment for cervical cancer, and its ultimate goal is to ensure the planning target volume (PTV) reaches the prescribed dose while reducing dose deposition of organs-at-risk (OARs) as much as possible.
no code implementations • 12 Aug 2024 • Nicola Borri, Yukun Liu, Aleh Tsyvinski, Xi Wu
The European Union Emission Trading System is a prominent market-based mechanism to reduce emissions.
no code implementations • 8 Aug 2024 • Jingfu Yang, Peng Huang, Jing Hu, Shu Hu, Siwei Lyu, Xin Wang, Jun Guo, Xi Wu
The network is embedded with a nonlocal module to capture global information, while a 3D attention module is embedded to focus on the details of the lesion so that it can directly analyze the 3D lung CT and output the classification results.
no code implementations • 30 Jul 2024 • Jiaqi Cui, Pinxian Zeng, Yuanyuan Xu, Xi Wu, Jiliu Zhou, Yan Wang
Our S3PET involves an un-supervised pre-training stage (Stage I) to extract representations from unpaired images, and a supervised dose-aware reconstruction stage (Stage II) to achieve LPET-to-SPET reconstruction by transferring the dose-specific knowledge between paired images.
no code implementations • 23 Jul 2024 • Xi Shi, Lingli Chen, Peng Wei, Xi Wu, Tian Jiang, Yonggang Luo, Lecheng Xie
This paper introduces a novel neural rendering method termed Decoupled Hybrid Gaussian Splatting (DHGS), targeting at promoting the rendering quality of novel view synthesis for static driving scenes.
1 code implementation • 8 Jul 2024 • Yuxiang Yang, Lu Wen, Xinyi Zeng, Yuanyuan Xu, Xi Wu, Jiliu Zhou, Yan Wang
Facial Expression Recognition (FER) holds significant importance in human-computer interactions.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
no code implementations • 25 Jun 2024 • Peng Huang, Shu Hu, Bo Peng, Jiashu Zhang, Xi Wu, Xin Wang
This can lead to significant differences in recognition accuracy between classes and obvious recognition weaknesses.
no code implementations • 19 Jun 2024 • Jiaqi Cui, Xinyi Zeng, Pinxian Zeng, Bo Liu, Xi Wu, Jiliu Zhou, Yan Wang
To expedite the diffusion process, we further introduce an adversarial diffusive network with a reduced number of diffusion steps.
1 code implementation • 6 May 2024 • Zhizhao Duan, Hao Cheng, Duo Xu, Xi Wu, Xiangxie Zhang, Xi Ye, Zhen Xie
In the vast and dynamic landscape of urban settings, Traffic Safety Description and Analysis plays a pivotal role in applications ranging from insurance inspection to accident prevention.
1 code implementation • 26 Apr 2024 • Jing Hu, Honghu Zhang, Peng Zheng, Jialin Mu, Xiaomeng Huang, Xi Wu
This framework aims to facilitate the downscaling of diverse meteorological variables derived from various numerical models and spatiotemporal scales.
no code implementations • 6 Mar 2024 • Lu Wen, Zhenghao Feng, Yun Hou, Peng Wang, Xi Wu, Jiliu Zhou, Yan Wang
Semi-supervised learning is a sound measure to relieve the strict demand of abundant annotated datasets, especially for challenging multi-organ segmentation .
no code implementations • 1 Feb 2024 • Tiewen Chen, Shanmin Yang, Shu Hu, Zhenghan Fang, Ying Fu, Xi Wu, Xin Wang
this paper present we put a new insight into diffusion model-based data augmentation, and propose a Masked Conditional Diffusion Model (MCDM) for enhancing deepfake detection.
1 code implementation • 1 Feb 2024 • Jiaqi Cui, Yan Wang, Lu Wen, Pinxian Zeng, Xi Wu, Jiliu Zhou, Dinggang Shen
To obtain high-quality Positron emission tomography (PET) images while minimizing radiation exposure, numerous methods have been proposed to reconstruct standard-dose PET (SPET) images from the corresponding low-dose PET (LPET) images.
no code implementations • 31 Jan 2024 • Yicui Peng, Hao Chen, ChingSheng Lin, Guo Huang, Jinrong Hu, Hui Guo, Bin Kong, Shu Hu, Xi Wu, Xin Wang
Providing explanations within the recommendation system would boost user satisfaction and foster trust, especially by elaborating on the reasons for selecting recommended items tailored to the user.
no code implementations • 17 Jan 2024 • Chengxu Wu, Qinrui Fan, Shu Hu, Xi Wu, Xin Wang, Jing Hu
An important development direction in the Single-Image Super-Resolution (SISR) algorithms is to improve the efficiency of the algorithms.
Ranked #70 on
Image Super-Resolution
on Set14 - 4x upscaling
no code implementations • 13 Jan 2024 • Kaiqun Wu, Xiaoling Jiang, Rui Yu, Yonggang Luo, Tian Jiang, Xi Wu, Peng Wei
To capture the subtle differences, a fine-grained network is adopted to acquire multi-scale features.
no code implementations • 10 Nov 2023 • Jing Hu, Qinrui Fan, Shu Hu, Siwei Lyu, Xi Wu, Xin Wang
In the field of clinical medicine, computed tomography (CT) is an effective medical imaging modality for the diagnosis of various pathologies.
no code implementations • 6 Nov 2023 • Zhenghao Feng, Lu Wen, Jianghong Xiao, Yuanyuan Xu, Xi Wu, Jiliu Zhou, Xingchen Peng, Yan Wang
In the forward process, DiffDose transforms dose distribution maps into pure Gaussian noise by gradually adding small noise and a noise predictor is simultaneously trained to estimate the noise added at each timestep.
no code implementations • 7 Oct 2023 • Lei Zhang, Hao Chen, Shu Hu, Bin Zhu, Ching Sheng Lin, Xi Wu, Jinrong Hu, Xin Wang
Generative adversarial networks (GANs) have remarkably advanced in diverse domains, especially image generation and editing.
no code implementations • 30 Sep 2023 • Chengming Feng, Jing Hu, Xin Wang, Shu Hu, Bin Zhu, Xi Wu, Hongtu Zhu, Siwei Lyu
Controlling the degree of stylization in the Neural Style Transfer (NST) is a little tricky since it usually needs hand-engineering on hyper-parameters.
no code implementations • 30 Sep 2023 • Shanmin Yang, Hui Guo, Shu Hu, Bin Zhu, Ying Fu, Siwei Lyu, Xi Wu, Xin Wang
Deepfake technology poses a significant threat to security and social trust.
1 code implementation • 25 Sep 2023 • Jia Zhang, Bo Peng, Xi Wu
Extensive experimentation on both the multi-label classification and segmentation network stages underscores the effectiveness of the proposed graph reasoning approach for advancing WSSS.
2 code implementations • 24 Sep 2023 • Xin Wang, Ziwei Luo, Jing Hu, Chengming Feng, Shu Hu, Bin Zhu, Xi Wu, Hongtu Zhu, Xin Li, Siwei Lyu
The key feature in the RL-I2IT framework is to decompose a monolithic learning process into small steps with a lightweight model to progressively transform a source image successively to a target image.
no code implementations • 23 Sep 2023 • TingYu Zhao, Bo Peng, Yuan Sun, DaiPeng Yang, Zhenguang Zhang, Xi Wu
Recently, advancements in deep learning-based superpixel segmentation methods have brought about improvements in both the efficiency and the performance of segmentation.
1 code implementation • 23 Aug 2023 • Cheng Zhu, JiaYi Zhu, Xi Wu, Lijuan Zhang, Shuqi Yang, Ping Liang, Honghan Chen, Ying Tan
In this paper, we propose a novel edge-aware hard clustering graph pool (EHCPool), which is tailored to dominant edge features and redefines the clustering process.
no code implementations • 10 Aug 2023 • Jiaqi Cui, Pinxian Zeng, Xinyi Zeng, Peng Wang, Xi Wu, Jiliu Zhou, Yan Wang, Dinggang Shen
Specifically, the TriDo-Former consists of two cascaded networks, i. e., a sinogram enhancement transformer (SE-Former) for denoising the input LPET sinograms and a spatial-spectral reconstruction transformer (SSR-Former) for reconstructing SPET images from the denoised sinograms.
no code implementations • 19 Jul 2023 • Zhenghao Feng, Lu Wen, Peng Wang, Binyu Yan, Xi Wu, Jiliu Zhou, Yan Wang
To alleviate this limitation, we innovatively introduce a diffusion-based dose prediction (DiffDP) model for predicting the radiotherapy dose distribution of cancer patients.
1 code implementation • 28 Jun 2023 • Xi Wu, Liangwei Yang, Jibing Gong, Chao Zhou, Tianyu Lin, Xiaolong Liu, Philip S. Yu
To address this limitation, we propose Dimension Independent Mixup for Hard Negative Sampling (DINS), which is the first Area-wise sampling method for training CF-based models.
no code implementations • 27 May 2023 • Nils Palumbo, Yang Guo, Xi Wu, Jiefeng Chen, YIngyu Liang, Somesh Jha
Nevertheless, under recent strong adversarial attacks (GMSA, which has been shown to be much more effective than AutoAttack against transduction), Goldwasser et al.'s work was shown to have low performance in a practical deep-learning setting.
1 code implementation • 2 May 2023 • Jiefeng Chen, Jayaram Raghuram, Jihye Choi, Xi Wu, YIngyu Liang, Somesh Jha
We theoretically analyze the stratified rejection setting and propose a novel defense method -- Adversarial Training with Consistent Prediction-based Rejection (CPR) -- for building a robust selective classifier.
no code implementations • 19 Apr 2023 • Hao Chen, Peng Zheng, Xin Wang, Shu Hu, Bin Zhu, Jinrong Hu, Xi Wu, Siwei Lyu
As growing usage of social media websites in the recent decades, the amount of news articles spreading online rapidly, resulting in an unprecedented scale of potentially fraudulent information.
1 code implementation • 28 Feb 2023 • Zhenmei Shi, Jiefeng Chen, Kunyang Li, Jayaram Raghuram, Xi Wu, YIngyu Liang, Somesh Jha
foundation models) has recently become a prevalent learning paradigm, where one first pre-trains a representation using large-scale unlabeled data, and then learns simple predictors on top of the representation using small labeled data from the downstream tasks.
no code implementations • 26 Jan 2023 • Yunxu Xie, Shu Hu, Xin Wang, Quanyu Liao, Bin Zhu, Xi Wu, Siwei Lyu
Existing adversarial attacks on object detection focus on attacking anchor-based detectors, which may not work well for anchor-free detectors.
1 code implementation • 20 Oct 2022 • Yanfei Xiang, Xin Wang, Shu Hu, Bin Zhu, Xiaomeng Huang, Xi Wu, Siwei Lyu
Reinforcement learning is applied to solve actual complex tasks from high-dimensional, sensory inputs.
1 code implementation • 14 Dec 2021 • Ziwei Luo, Jing Hu, Xin Wang, Shu Hu, Bin Kong, Youbing Yin, Qi Song, Xi Wu, Siwei Lyu
We evaluate our method on several 2D and 3D medical image datasets, some of which contain large deformations.
1 code implementation • 14 Dec 2021 • Ziwei Luo, Jing Hu, Xin Wang, Siwei Lyu, Bin Kong, Youbing Yin, Qi Song, Xi Wu
Training a model-free deep reinforcement learning model to solve image-to-image translation is difficult since it involves high-dimensional continuous state and action spaces.
no code implementations • AAAI Workshop AdvML 2022 • Jiefeng Chen, Jayaram Raghuram, Jihye Choi, Xi Wu, YIngyu Liang, Somesh Jha
Motivated by this metric, we propose novel loss functions and a robust training method -- \textit{stratified adversarial training with rejection} (SATR) -- for a classifier with reject option, where the goal is to accept and correctly-classify small input perturbations, while allowing the rejection of larger input perturbations that cannot be correctly classified.
1 code implementation • ICLR 2022 • Jiefeng Chen, Xi Wu, Yang Guo, YIngyu Liang, Somesh Jha
There has been emerging interest in using transductive learning for adversarial robustness (Goldwasser et al., NeurIPS 2020; Wu et al., ICML 2020; Wang et al., ArXiv 2021).
1 code implementation • NeurIPS 2021 • Jiefeng Chen, Frederick Liu, Besim Avci, Xi Wu, YIngyu Liang, Somesh Jha
This observation leads to two challenging tasks: (1) unsupervised accuracy estimation, which aims to estimate the accuracy of a pre-trained classifier on a set of unlabeled test inputs; (2) error detection, which aims to identify mis-classified test inputs.
no code implementations • 15 Jun 2021 • Jiefeng Chen, Yang Guo, Xi Wu, Tianqi Li, Qicheng Lao, YIngyu Liang, Somesh Jha
Compared to traditional "test-time" defenses, these defense mechanisms "dynamically retrain" the model based on test time input via transductive learning; and theoretically, attacking these defenses boils down to bilevel optimization, which seems to raise the difficulty for adaptive attacks.
no code implementations • 3 Jun 2021 • Quanyu Liao, Xin Wang, Bin Kong, Siwei Lyu, Bin Zhu, Youbing Yin, Qi Song, Xi Wu
Deep neural networks have been demonstrated to be vulnerable to adversarial attacks: subtle perturbation can completely change prediction result.
no code implementations • 3 Jun 2021 • Quanyu Liao, Yuezun Li, Xin Wang, Bin Kong, Bin Zhu, Siwei Lyu, Youbing Yin, Qi Song, Xi Wu
Fooling people with highly realistic fake images generated with Deepfake or GANs brings a great social disturbance to our society.
no code implementations • 6 May 2021 • Yuchen Fei, Bo Zhan, Mei Hong, Xi Wu, Jiliu Zhou, Yan Wang
To take full advantage of the complementary information provided by different modalities, multi-modal MRI sequences are utilized as input.
no code implementations • 11 Feb 2021 • Tianren Fan, Xi Wu, Sai R. M. Vangapandu, Amir H. Hosseinnia, Ali A. Eftekhar, Ali Adibi
We report the first demonstration of integrated electro-optic (EO) phase shifters based on racetrack microresonators on a 3C-silicon-carbide-on-insulator (SiCOI) platform working at near-infrared (NIR) wavelengths.
Optics Applied Physics
no code implementations • 4 Jan 2021 • Xi Wu, C. X. Zhang, M. A. Zubkov
We propose the model of layered materials, in which each layer is described by the conventional Haldane model, while the inter - layer hopping parameter corresponds to the ABC stacking.
Mesoscale and Nanoscale Physics
no code implementations • 1 Jan 2021 • Xi Wu, Yang Guo, Tianqi Li, Jiefeng Chen, Qicheng Lao, YIngyu Liang, Somesh Jha
On the positive side, we show that, if one is allowed to access the training data, then Domain Adversarial Neural Networks (${\sf DANN}$), an algorithm designed for unsupervised domain adaptation, can provide nontrivial robustness in the test-time maximin threat model against strong transfer attacks and adaptive fixed point attacks.
no code implementations • 27 Oct 2020 • Quanyu Liao, Xin Wang, Bin Kong, Siwei Lyu, Youbing Yin, Qi Song, Xi Wu
The deep neural network is vulnerable to adversarial examples.
no code implementations • 16 Oct 2020 • Yuang Shi, Chen Zu, Mei Hong, Luping Zhou, Lei Wang, Xi Wu, Jiliu Zhou, Daoqiang Zhang, Yan Wang
With the increasing amounts of high-dimensional heterogeneous data to be processed, multi-modality feature selection has become an important research direction in medical image analysis.
no code implementations • 28 Sep 2020 • Jiefeng Chen, Yixuan Li, Xi Wu, YIngyu Liang, Somesh Jha
We show that, by mining informative auxiliary OOD data, one can significantly improve OOD detection performance, and somewhat surprisingly, generalize to unseen adversarial attacks.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
1 code implementation • 26 Jun 2020 • Jiefeng Chen, Yixuan Li, Xi Wu, YIngyu Liang, Somesh Jha
We show that, by mining informative auxiliary OOD data, one can significantly improve OOD detection performance, and somewhat surprisingly, generalize to unseen adversarial attacks.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
no code implementations • 11 Jun 2020 • Pin Tang, Chen Zu, Mei Hong, Rui Yan, Xingchen Peng, Jianghong Xiao, Xi Wu, Jiliu Zhou, Luping Zhou, Yan Wang
In this paper, we propose a Dense SegU-net (DSU-net) framework for automatic NPC segmentation in MRI.
no code implementations • 22 Apr 2020 • Xi Wu, Yang Guo, Jiefeng Chen, YIngyu Liang, Somesh Jha, Prasad Chalasani
Recent studies provide hints and failure examples for domain invariant representation learning, a common approach for this problem, but the explanations provided are somewhat different and do not provide a unified picture.
1 code implementation • AAAI Workshop AdvML 2022 • Jiefeng Chen, Yixuan Li, Xi Wu, YIngyu Liang, Somesh Jha
Formally, we extensively study the problem of Robust Out-of-Distribution Detection on common OOD detection approaches, and show that state-of-the-art OOD detectors can be easily fooled by adding small perturbations to the in-distribution and OOD inputs.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
no code implementations • 10 Feb 2020 • Quanyu Liao, Xin Wang, Bin Kong, Siwei Lyu, Youbing Yin, Qi Song, Xi Wu
Deep neural networks have been demonstrated to be vulnerable to adversarial attacks: subtle perturbations can completely change the classification results.
no code implementations • 29 Jan 2020 • Shanhui Sun, Jing Hu, Mingqing Yao, Jinrong Hu, Xiaodong Yang, Qi Song, Xi Wu
To this end, these two components are tackled in an end-to-end manner via reinforcement learning in this work.
no code implementations • 5 Aug 2019 • Yifei Huang, Matt Shum, Xi Wu, Jason Zezhong Xiao
With the industry trend of shifting from a traditional hierarchical approach to flatter management structure, crowdsourced performance assessment gained mainstream popularity.
no code implementations • 26 May 2019 • Varun Chandrasekaran, Brian Tang, Nicolas Papernot, Kassem Fawaz, Somesh Jha, Xi Wu
and how to design a classification paradigm that leverages these invariances to improve the robustness accuracy trade-off?
1 code implementation • NeurIPS 2019 • Jiefeng Chen, Xi Wu, Vaibhav Rastogi, YIngyu Liang, Somesh Jha
An emerging problem in trustworthy machine learning is to train models that produce robust interpretations for their predictions.
1 code implementation • ICML 2020 • Prasad Chalasani, Jiefeng Chen, Amrita Roy Chowdhury, Somesh Jha, Xi Wu
Our first contribution is a theoretical exploration of how these two properties (when using attributions based on Integrated Gradients, or IG) are related to adversarial training, for a class of 1-layer networks (which includes logistic regression models for binary and multi-class classification); for these networks we show that (a) adversarial training using an $\ell_\infty$-bounded adversary produces models with sparse attribution vectors, and (b) natural model-training while encouraging stable explanations (via an extra term in the loss function), is equivalent to adversarial training.
1 code implementation • 20 May 2018 • Jiefeng Chen, Xi Wu, Vaibhav Rastogi, YIngyu Liang, Somesh Jha
We analyze our results in a theoretical framework and offer strong evidence that pixel discretization is unlikely to work on all but the simplest of the datasets.
no code implementations • ICLR 2018 • Xi Wu, Uyeong Jang, Lingjiao Chen, Somesh Jha
Interestingly, we find that a recent objective by Madry et al. encourages training a model that satisfies well our formal version of the goodness property, but has a weak control of points that are wrong but with low confidence.
no code implementations • ICML 2018 • Xi Wu, Uyeong Jang, Jiefeng Chen, Lingjiao Chen, Somesh Jha
In this paper we study leveraging confidence information induced by adversarial training to reinforce adversarial robustness of a given adversarially trained model.
no code implementations • 22 Feb 2017 • Fengan Li, Lingjiao Chen, Yijing Zeng, Arun Kumar, Jeffrey F. Naughton, Jignesh M. Patel, Xi Wu
We fill this crucial research gap by proposing a new lossless compression scheme we call tuple-oriented compression (TOC) that is inspired by an unlikely source, the string/text compression scheme Lempel-Ziv-Welch, but tailored to MGD in a way that preserves tuple boundaries within mini-batches.
1 code implementation • 15 Jun 2016 • Xi Wu, Fengan Li, Arun Kumar, Kamalika Chaudhuri, Somesh Jha, Jeffrey F. Naughton
This paper takes a first step to remedy this disconnect and proposes a private SGD algorithm to address \emph{both} issues in an integrated manner.
no code implementations • 20 Dec 2015 • Xi Wu, Matthew Fredrikson, Wentao Wu, Somesh Jha, Jeffrey F. Naughton
Perhaps more importantly, our theory reveals that the most basic mechanism in differential privacy, output perturbation, can be used to obtain a better tradeoff for all convex-Lipschitz-bounded learning tasks.
2 code implementations • 14 Nov 2015 • Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, Ananthram Swami
In this work, we introduce a defensive mechanism called defensive distillation to reduce the effectiveness of adversarial samples on DNNs.