Search Results for author: Xiaochun Cao

Found 143 papers, 66 papers with code

Unlearning Backdoor Threats: Enhancing Backdoor Defense in Multimodal Contrastive Learning via Local Token Unlearning

no code implementations24 Mar 2024 Siyuan Liang, Kuanrong Liu, Jiajun Gong, Jiawei Liang, Yuan Xun, Ee-Chien Chang, Xiaochun Cao

In this paper, we explore the possibility of a less-cost defense from the perspective of model unlearning, that is, whether the model can be made to quickly \textbf{u}nlearn \textbf{b}ackdoor \textbf{t}hreats (UBT) by constructing a small set of poisoned samples.

backdoor defense Contrastive Learning

Object Detectors in the Open Environment: Challenges, Solutions, and Outlook

no code implementations24 Mar 2024 Siyuan Liang, Wei Wang, Ruoyu Chen, Aishan Liu, Boxi Wu, Ee-Chien Chang, Xiaochun Cao, DaCheng Tao

This paper aims to bridge this gap by conducting a comprehensive review and analysis of object detectors in open environments.

Incremental Learning Object

How Powerful Potential of Attention on Image Restoration?

no code implementations15 Mar 2024 Cong Wang, Jinshan Pan, Yeying Jin, Liyan Wang, Wei Wang, Gang Fu, Wenqi Ren, Xiaochun Cao

Our designs provide a closer look at the attention mechanism and reveal that some simple operations can significantly affect the model performance.

Image Restoration

Segmentation Guided Sparse Transformer for Under-Display Camera Image Restoration

no code implementations9 Mar 2024 Jingyun Xue, Tao Wang, Jun Wang, Kaihao Zhang, Wenhan Luo, Wenqi Ren, Zikun Liu, Hyunhee Park, Xiaochun Cao

Specifically, we utilize sparse self-attention to filter out redundant information and noise, directing the model's attention to focus on the features more relevant to the degraded regions in need of reconstruction.

Image Restoration Instance Segmentation +1

Hide in Thicket: Generating Imperceptible and Rational Adversarial Perturbations on 3D Point Clouds

1 code implementation8 Mar 2024 Tianrui Lou, Xiaojun Jia, Jindong Gu, Li Liu, Siyuan Liang, Bangyan He, Xiaochun Cao

We find that concealing deformation perturbations in areas insensitive to human eyes can achieve a better trade-off between imperceptibility and adversarial strength, specifically in parts of the object surface that are complex and exhibit drastic curvature changes.

3D Point Cloud Classification Adversarial Attack +1

Logit Standardization in Knowledge Distillation

1 code implementation3 Mar 2024 Shangquan Sun, Wenqi Ren, Jingzhi Li, Rui Wang, Xiaochun Cao

Knowledge distillation involves transferring soft labels from a teacher to a student using a shared temperature-based softmax function.

Knowledge Distillation

Hierarchical Invariance for Robust and Interpretable Vision Tasks at Larger Scales

1 code implementation23 Feb 2024 Shuren Qi, Yushu Zhang, Chao Wang, Zhihua Xia, Jian Weng, Xiaochun Cao

Developing robust and interpretable vision systems is a crucial step towards trustworthy artificial intelligence.

Neural Architecture Search

Poisoned Forgery Face: Towards Backdoor Attacks on Face Forgery Detection

1 code implementation18 Feb 2024 Jiawei Liang, Siyuan Liang, Aishan Liu, Xiaojun Jia, Junhao Kuang, Xiaochun Cao

However, this paper introduces a novel and previously unrecognized threat in face forgery detection scenarios caused by backdoor attack.

Backdoor Attack

Less is More: Fewer Interpretable Region via Submodular Subset Selection

1 code implementation14 Feb 2024 Ruoyu Chen, Hua Zhang, Siyuan Liang, Jingzhi Li, Xiaochun Cao

For incorrectly predicted samples, our method achieves gains of 81. 0% and 18. 4% compared to the HSIC-Attribution algorithm in the average highest confidence and Insertion score respectively.

Interpretability Techniques for Deep Learning

Cheating Suffix: Targeted Attack to Text-To-Image Diffusion Models with Multi-Modal Priors

1 code implementation2 Feb 2024 Dingcheng Yang, Yang Bai, Xiaojun Jia, Yang Liu, Xiaochun Cao, Wenjian Yu

The MMP-Attack shows a notable advantage over existing works with superior universality and transferability, which can effectively attack commercial text-to-image (T2I) models such as DALL-E 3.

Image Generation

Phrase Grounding-based Style Transfer for Single-Domain Generalized Object Detection

no code implementations2 Feb 2024 Hao Li, Wei Wang, Cong Wang, Zhigang Luo, Xinwang Liu, Kenli Li, Xiaochun Cao

Single-domain generalized object detection aims to enhance a model's generalizability to multiple unseen target domains using only data from a single source domain during training.

object-detection Object Detection +2

Does Few-shot Learning Suffer from Backdoor Attacks?

no code implementations31 Dec 2023 Xinwei Liu, Xiaojun Jia, Jindong Gu, Yuan Xun, Siyuan Liang, Xiaochun Cao

However, in this paper, we propose the Few-shot Learning Backdoor Attack (FLBA) to show that FSL can still be vulnerable to backdoor attacks.

Backdoor Attack Few-Shot Learning

Towards Real-World Blind Face Restoration with Generative Diffusion Prior

1 code implementation25 Dec 2023 Xiaoxu Chen, Jingfan Tan, Tao Wang, Kaihao Zhang, Wenhan Luo, Xiaochun Cao

We propose BFRffusion which is thoughtfully designed to effectively extract features from low-quality face images and could restore realistic and faithful facial details with the generative prior of the pretrained Stable Diffusion.

Blind Face Restoration Privacy Preserving

OT-Attack: Enhancing Adversarial Transferability of Vision-Language Models via Optimal Transport Optimization

no code implementations7 Dec 2023 Dongchen Han, Xiaojun Jia, Yang Bai, Jindong Gu, Yang Liu, Xiaochun Cao

Investigating the generation of high-transferability adversarial examples is crucial for uncovering VLP models' vulnerabilities in practical scenarios.

Adversarial Attack Data Augmentation +2

TranSegPGD: Improving Transferability of Adversarial Examples on Semantic Segmentation

no code implementations3 Dec 2023 Xiaojun Jia, Jindong Gu, Yihao Huang, Simeng Qin, Qing Guo, Yang Liu, Xiaochun Cao

At the second stage, the pixels are divided into different branches based on their transferable property which is dependent on Kullback-Leibler divergence.

Adversarial Attack Image Classification +2

Towards Vision Enhancing LLMs: Empowering Multimodal Knowledge Storage and Sharing in LLMs

no code implementations27 Nov 2023 Yunxin Li, Baotian Hu, Wei Wang, Xiaochun Cao, Min Zhang

These models predominantly map visual information into language representation space, leveraging the vast knowledge and powerful text generation abilities of LLMs to produce multimodal instruction-following responses.

Instruction Following multimodal generation +1

BadCLIP: Dual-Embedding Guided Backdoor Attack on Multimodal Contrastive Learning

no code implementations20 Nov 2023 Siyuan Liang, Mingli Zhu, Aishan Liu, Baoyuan Wu, Xiaochun Cao, Ee-Chien Chang

This paper reveals the threats in this practical scenario that backdoor attacks can remain effective even after defenses and introduces the \emph{\toolns} attack, which is resistant to backdoor detection and model fine-tuning defenses.

Backdoor Attack Contrastive Learning

DRAUC: An Instance-wise Distributionally Robust AUC Optimization Framework

1 code implementation NeurIPS 2023 Siran Dai, Qianqian Xu, Zhiyong Yang, Xiaochun Cao, Qingming Huang

To tackle this challenge, methodically we propose an instance-wise surrogate loss of Distributionally Robust AUC (DRAUC) and build our optimization framework on top of it.

Fast Propagation is Better: Accelerating Single-Step Adversarial Training via Sampling Subnetworks

no code implementations24 Oct 2023 Xiaojun Jia, Jianshu Li, Jindong Gu, Yang Bai, Xiaochun Cao

Besides, we provide theoretical analysis to show the model robustness can be improved by the single-step adversarial training with sampled subnetworks.

A Unified Generalization Analysis of Re-Weighting and Logit-Adjustment for Imbalanced Learning

1 code implementation NeurIPS 2023 Zitai Wang, Qianqian Xu, Zhiyong Yang, Yuan He, Xiaochun Cao, Qingming Huang

However, existing generalization analysis of such losses is still coarse-grained and fragmented, failing to explain some empirical results.

Learning node representation via Motif Coarsening

1 code implementation journal 2023 Junyu Chen, Qianqian Xu, Zhiyong Yang, Ke Ma, Xiaochun Cao, Qingming Huang

For the motif-based node representation learning process, we propose a Motif Coarsening strategy for incorporating motif structure into the graph representation learning process.

Graph Representation Learning

Exploring the Robustness of Human Parsers Towards Common Corruptions

no code implementations2 Sep 2023 Sanyi Zhang, Xiaochun Cao, Rui Wang, Guo-Jun Qi, Jie zhou

The experimental results show that the proposed method demonstrates good universality which can improve the robustness of the human parsing models and even the semantic segmentation models when facing various image common corruptions.

Data Augmentation Human Parsing +1

Revisiting AUC-oriented Adversarial Training with Loss-Agnostic Perturbations

2 code implementations TPAMI 2023 Zhiyong Yang, Qianqian Xu, Wenzheng Hou, Shilong Bao, Yuan He, Xiaochun Cao, Qingming Huang

On top of this, we can show that: 1) Under mild conditions, AdAUC can be optimized equivalently with score-based or instance-wise-loss-based perturbations, which is compatible with most of the popular adversarial example generation methods.

AUC-Oriented Domain Adaptation: From Theory to Algorithm

1 code implementation TPAMI 2023 Zhiyong Yang, Qianqian Xu, Shilong Bao, Peisong Wen, Xiaochun Cao, Qingming Huang

We propose a new result that not only addresses the interdependency issue but also brings a much sharper bound with weaker assumptions about the loss function.

Disease Prediction Fraud Detection +1

Privacy-Enhancing Face Obfuscation Guided by Semantic-Aware Attribution Maps

no code implementations IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2023 Jingzhi Li, Hua Zhang, Siyuan Liang, Pengwen Dai, Xiaochun Cao

Within this module, we introduce a pixel importance estimation model based on Shapley value to obtain a pixel-level attribution map, and then each pixel on the attribution map is aggregated into semantic facial parts, which are used to quantify the importance of different facial parts.

Face Recognition

Reversible Quantization Index Modulation for Static Deep Neural Network Watermarking

no code implementations29 May 2023 Junren Qin, Shanxiang Lyu, Fan Yang, Jiarui Deng, Zhihua Xia, Xiaochun Cao

In this paper, we propose a novel RDH-based static DNN watermarking scheme using quantization index modulation (QIM).

Quantization

Towards an Accurate and Secure Detector against Adversarial Perturbations

1 code implementation18 May 2023 Chao Wang, Shuren Qi, Zhiqiu Huang, Yushu Zhang, Rushi Lan, Xiaochun Cao

It expands the above works on two aspects: 1) the introduced Krawtchouk basis provides better spatial-frequency discriminability and thereby is more suitable for capturing adversarial patterns than the common trigonometric or wavelet basis; 2) the extensive parameters for decomposition are generated by a pseudo-random function with secret keys, hence blocking the defense-aware adversarial attack.

Adversarial Attack Blocking

FedSOV: Federated Model Secure Ownership Verification with Unforgeable Signature

no code implementations10 May 2023 Wenyuan Yang, Gongxi Zhu, Yuguo Yin, Hanlin Gu, Lixin Fan, Qiang Yang, Xiaochun Cao

Federated learning allows multiple parties to collaborate in learning a global model without revealing private data.

Federated Learning

FedZKP: Federated Model Ownership Verification with Zero-knowledge Proof

no code implementations8 May 2023 Wenyuan Yang, Yuguo Yin, Gongxi Zhu, Hanlin Gu, Lixin Fan, Xiaochun Cao, Qiang Yang

Federated learning (FL) allows multiple parties to cooperatively learn a federated model without sharing private data with each other.

Federated Learning

LSGNN: Towards General Graph Neural Network in Node Classification by Local Similarity

1 code implementation7 May 2023 Yuhan Chen, Yihong Luo, Jing Tang, Liang Yang, Siya Qiu, Chuan Wang, Xiaochun Cao

Motivated by it, we propose to use the local similarity (LocalSim) to learn node-level weighted fusion, which can also serve as a plug-and-play module.

Node Classification

Cognition Guided Human-Object Relationship Detection

no code implementations journal 2023 Zhitao Zeng, Pengwen Dai, Xuan Zhang, Lei Zhang, Xiaochun Cao

Human-object relationship detection reveals the fine-grained relationship between humans and objects, helping the comprehensive understanding of videos.

Human-Object Relationship Detection Object +1

Improving Fast Adversarial Training with Prior-Guided Knowledge

no code implementations1 Apr 2023 Xiaojun Jia, Yong Zhang, Xingxing Wei, Baoyuan Wu, Ke Ma, Jue Wang, Xiaochun Cao

This initialization is generated by using high-quality adversarial perturbations from the historical training process.

Representing Noisy Image Without Denoising

1 code implementation18 Jan 2023 Shuren Qi, Yushu Zhang, Chao Wang, Tao Xiang, Xiaochun Cao, Yong Xiang

In this paper, we explore a non-learning paradigm that aims to derive robust representation directly from noisy images, without the denoising as pre-processing.

Data Augmentation Image Denoising

Focal Network for Image Restoration

1 code implementation ICCV 2023 Yuning Cui, Wenqi Ren, Xiaochun Cao, Alois Knoll

Image restoration aims to reconstruct a sharp image from its degraded counterpart, which plays an important role in many fields.

Deblurring Image Defocus Deblurring +2

Lightweight Image Super-Resolution with Superpixel Token Interaction

1 code implementation ICCV 2023 Aiping Zhang, Wenqi Ren, Yi Liu, Xiaochun Cao

Our method employs superpixels to cluster local similar pixels to form the explicable local regions and utilizes intra-superpixel attention to enable local information interaction.

Image Super-Resolution Superpixels

Rethinking Image Restoration for Object Detection

1 code implementation NIPS 2022 Shangquan Sun, Wenqi Ren, Tao Wang, Xiaochun Cao

To address the issue, we propose a targeted adversarial attack in the restoration procedure to boost object detection performance after restoration.

Adversarial Attack Domain Adaptation +5

OpenAUC: Towards AUC-Oriented Open-Set Recognition

1 code implementation22 Oct 2022 Zitai Wang, Qianqian Xu, Zhiyong Yang, Yuan He, Xiaochun Cao, Qingming Huang

In this paper, a systematic analysis reveals that most existing metrics are essentially inconsistent with the aforementioned goal of OSR: (1) For metrics extended from close-set classification, such as Open-set F-score, Youden's index, and Normalized Accuracy, a poor open-set prediction can escape from a low performance score with a superior close-set prediction.

Novelty Detection Open Set Learning

CIR-Net: Cross-modality Interaction and Refinement for RGB-D Salient Object Detection

3 code implementations6 Oct 2022 Runmin Cong, Qinwei Lin, Chen Zhang, Chongyi Li, Xiaochun Cao, Qingming Huang, Yao Zhao

Focusing on the issue of how to effectively capture and utilize cross-modality information in RGB-D salient object detection (SOD) task, we present a convolutional neural network (CNN) model, named CIR-Net, based on the novel cross-modality interaction and refinement.

object-detection RGB-D Salient Object Detection +1

The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm

1 code implementation NeurIPS 2023 Shilong Bao, Qianqian Xu, Zhiyong Yang, Yuan He, Xiaochun Cao, Qingming Huang

Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collaborative Filtering.

Collaborative Filtering Metric Learning +1

MaxMatch: Semi-Supervised Learning with Worst-Case Consistency

no code implementations26 Sep 2022 Yangbangyan Jiang, Xiaodan Li, Yuefeng Chen, Yuan He, Qianqian Xu, Zhiyong Yang, Xiaochun Cao, Qingming Huang

In recent years, great progress has been made to incorporate unlabeled data to overcome the inefficiently supervised problem via semi-supervised learning (SSL).

Exploring Inconsistent Knowledge Distillation for Object Detection with Data Augmentation

1 code implementation20 Sep 2022 Jiawei Liang, Siyuan Liang, Aishan Liu, Ke Ma, Jingzhi Li, Xiaochun Cao

Specifically, we propose a sample-specific data augmentation to transfer the teacher model's ability in capturing distinct frequency components and suggest an adversarial feature augmentation to extract the teacher model's perceptions of non-robust features in the data.

Data Augmentation Knowledge Distillation +2

A Large-scale Multiple-objective Method for Black-box Attack against Object Detection

no code implementations16 Sep 2022 Siyuan Liang, Longkang Li, Yanbo Fan, Xiaojun Jia, Jingzhi Li, Baoyuan Wu, Xiaochun Cao

Recent studies have shown that detectors based on deep models are vulnerable to adversarial examples, even in the black-box scenario where the attacker cannot access the model information.

object-detection Object Detection

A Tale of HodgeRank and Spectral Method: Target Attack Against Rank Aggregation Is the Fixed Point of Adversarial Game

1 code implementation13 Sep 2022 Ke Ma, Qianqian Xu, Jinshan Zeng, Guorong Li, Xiaochun Cao, Qingming Huang

From the perspective of the dynamical system, the attack behavior with a target ranking list is a fixed point belonging to the composition of the adversary and the victim.

Information Retrieval Retrieval

Optimizing Partial Area Under the Top-k Curve: Theory and Practice

1 code implementation3 Sep 2022 Zitai Wang, Qianqian Xu, Zhiyong Yang, Yuan He, Xiaochun Cao, Qingming Huang

Finally, the experimental results on four benchmark datasets validate the effectiveness of our proposed framework.

MOVE: Effective and Harmless Ownership Verification via Embedded External Features

1 code implementation4 Aug 2022 Yiming Li, Linghui Zhu, Xiaojun Jia, Yang Bai, Yong Jiang, Shu-Tao Xia, Xiaochun Cao

In general, we conduct the ownership verification by verifying whether a suspicious model contains the knowledge of defender-specified external features.

Style Transfer

Prior-Guided Adversarial Initialization for Fast Adversarial Training

1 code implementation18 Jul 2022 Xiaojun Jia, Yong Zhang, Xingxing Wei, Baoyuan Wu, Ke Ma, Jue Wang, Xiaochun Cao

Based on the observation, we propose a prior-guided FGSM initialization method to avoid overfitting after investigating several initialization strategies, improving the quality of the AEs during the whole training process.

Adversarial Attack Adversarial Attack on Video Classification

Watermark Vaccine: Adversarial Attacks to Prevent Watermark Removal

1 code implementation17 Jul 2022 Xinwei Liu, Jian Liu, Yang Bai, Jindong Gu, Tao Chen, Xiaojun Jia, Xiaochun Cao

Inspired by the vulnerability of DNNs on adversarial perturbations, we propose a novel defence mechanism by adversarial machine learning for good.

AIParsing: Anchor-free Instance-level Human Parsing

no code implementations14 Jul 2022 Sanyi Zhang, Xiaochun Cao, Guo-Jun Qi, Zhanjie Song, Jie zhou

Most state-of-the-art instance-level human parsing models adopt two-stage anchor-based detectors and, therefore, cannot avoid the heuristic anchor box design and the lack of analysis on a pixel level.

Human Parsing object-detection +1

Geometry Interaction Knowledge Graph Embeddings

1 code implementation24 Jun 2022 Zongsheng Cao, Qianqian Xu, Zhiyong Yang, Xiaochun Cao, Qingming Huang

Knowledge graph (KG) embeddings have shown great power in learning representations of entities and relations for link prediction tasks.

Knowledge Graph Completion Knowledge Graph Embeddings +1

Optimizing Two-way Partial AUC with an End-to-end Framework

1 code implementation TPAMI 2022 Zhiyong Yang, Qianqian Xu, Shilong Bao, Yuan He, Xiaochun Cao, Qingming Huang

The critical challenge along this course lies in the difficulty of performing gradient-based optimization with end-to-end stochastic training, even with a proper choice of surrogate loss.

Vocal Bursts Valence Prediction

Entity-Graph Enhanced Cross-Modal Pretraining for Instance-level Product Retrieval

no code implementations17 Jun 2022 Xiao Dong, Xunlin Zhan, Yunchao Wei, XiaoYong Wei, YaoWei Wang, Minlong Lu, Xiaochun Cao, Xiaodan Liang

Our goal in this research is to study a more realistic environment in which we can conduct weakly-supervised multi-modal instance-level product retrieval for fine-grained product categories.

Retrieval

ADT-SSL: Adaptive Dual-Threshold for Semi-Supervised Learning

no code implementations21 May 2022 Zechen Liang, Yuan-Gen Wang, Wei Lu, Xiaochun Cao

Semi-Supervised Learning (SSL) has advanced classification tasks by inputting both labeled and unlabeled data to train a model jointly.

Detecting Recolored Image by Spatial Correlation

no code implementations23 Apr 2022 Yushu Zhang, Nuo Chen, Shuren Qi, Mingfu Xue, Xiaochun Cao

In this paper, we try to explore a solution from the perspective of the spatial correlation, which exhibits the generic detection capability for both conventional and deep learning-based recoloring.

Image Forensics Image Manipulation

LAS-AT: Adversarial Training with Learnable Attack Strategy

1 code implementation CVPR 2022 Xiaojun Jia, Yong Zhang, Baoyuan Wu, Ke Ma, Jue Wang, Xiaochun Cao

In this paper, we propose a novel framework for adversarial training by introducing the concept of "learnable attack strategy", dubbed LAS-AT, which learns to automatically produce attack strategies to improve the model robustness.

A Principled Design of Image Representation: Towards Forensic Tasks

1 code implementation2 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.

Image Forensics

Parallel Rectangle Flip Attack: A Query-based Black-box Attack against Object Detection

no code implementations ICCV 2021 Siyuan Liang, Baoyuan Wu, Yanbo Fan, Xingxing Wei, Xiaochun Cao

Extensive experiments demonstrate that our method can effectively and efficiently attack various popular object detectors, including anchor-based and anchor-free, and generate transferable adversarial examples.

Autonomous Driving Image Classification +2

Defending against Model Stealing via Verifying Embedded External Features

1 code implementation ICML Workshop AML 2021 Yiming Li, Linghui Zhu, Xiaojun Jia, Yong Jiang, Shu-Tao Xia, Xiaochun Cao

In this paper, we explore the defense from another angle by verifying whether a suspicious model contains the knowledge of defender-specified \emph{external features}.

Style Transfer

Diverse Message Passing for Attribute with Heterophily

no code implementations NeurIPS 2021 Liang Yang, Mengzhe Li, Liyang Liu, bingxin niu, Chuan Wang, Xiaochun Cao, Yuanfang Guo

Based on this attribute homophily rate, we propose a Diverse Message Passing (DMP) framework, which specifies every attribute propagation weight on each edge.

Attribute

Boosting Fast Adversarial Training with Learnable Adversarial Initialization

no code implementations11 Oct 2021 Xiaojun Jia, Yong Zhang, Baoyuan Wu, Jue Wang, Xiaochun Cao

Adversarial training (AT) has been demonstrated to be effective in improving model robustness by leveraging adversarial examples for training.

An Effective and Robust Detector for Logo Detection

2 code implementations1 Aug 2021 Xiaojun Jia, Huanqian Yan, Yonglin Wu, Xingxing Wei, Xiaochun Cao, Yong Zhang

Moreover, we have applied the proposed methods to competition ACM MM2021 Robust Logo Detection that is organized by Alibaba on the Tianchi platform and won top 2 in 36489 teams.

Data Augmentation

Comprehensive Studies for Arbitrary-shape Scene Text Detection

no code implementations25 Jul 2021 Pengwen Dai, Xiaochun Cao

In this paper, we carefully examine and analyze the inconsistent settings, and propose a unified framework for the bottom-up based scene text detection methods.

Scene Text Detection Text Detection

When All We Need is a Piece of the Pie: A Generic Framework for Optimizing Two-way Partial AUC.

1 code implementation ICML 2021 Zhiyong Yang, Qianqian Xu, Shilong Bao, Yuan He, Xiaochun Cao, Qingming Huang

The critical challenge along this course lies in the difficulty of performing gradient-based optimization with end-to-end stochastic training, even with a proper choice of surrogate loss.

Poisoning Attack against Estimating from Pairwise Comparisons

1 code implementation5 Jul 2021 Ke Ma, Qianqian Xu, Jinshan Zeng, Xiaochun Cao, Qingming Huang

In this paper, to the best of our knowledge, we initiate the first systematic investigation of data poisoning attacks on pairwise ranking algorithms, which can be formalized as the dynamic and static games between the ranker and the attacker and can be modeled as certain kinds of integer programming problems.

Data Poisoning

Ultra-High-Definition Image Dehazing via Multi-Guided Bilateral Learning

1 code implementation CVPR 2021 Zhuoran Zheng, Wenqi Ren, Xiaochun Cao, Xiaobin Hu, Tao Wang, Fenglong Song, Xiuyi Jia

To address the problem, we propose a novel network capable of real-time dehazing of 4K images on a single GPU, which consists of three deep CNNs.

Image Dehazing Single Image Dehazing +1

Generate More Imperceptible Adversarial Examples for Object Detection

no code implementations ICML Workshop AML 2021 Siyuan Liang, Xingxing Wei, Xiaochun Cao

The existing attack methods have the following problems: 1) the training generator takes a long time and is difficult to extend to a large dataset; 2) the excessive destruction of the image features does not improve the black-box attack effect(the generated adversarial examples have poor transferability) and brings about visible perturbations.

Object object-detection +1

SIGAN: A Novel Image Generation Method for Solar Cell Defect Segmentation and Augmentation

no code implementations11 Apr 2021 Binyi Su, Zhong Zhou, Haiyong Chen, Xiaochun Cao

Moreover, we release a new solar cell EL image dataset named as EL-2019, which includes three types of images: crack, finger interruption and defect-free.

Defect Detection Generative Adversarial Network +2

Multi-Scale Separable Network for Ultra-High-Definition Video Deblurring

1 code implementation ICCV 2021 Senyou Deng, Wenqi Ren, Yanyang Yan, Tao Wang, Fenglong Song, Xiaochun Cao

Although recent research has witnessed a significant progress on the video deblurring task, these methods struggle to reconcile inference efficiency and visual quality simultaneously, especially on ultra-high-definition (UHD) videos (e. g., 4K resolution).

Deblurring Vocal Bursts Intensity Prediction

Dense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images

3 code implementations26 Nov 2020 Qijian Zhang, Runmin Cong, Chongyi Li, Ming-Ming Cheng, Yuming Fang, Xiaochun Cao, Yao Zhao, Sam Kwong

Despite the remarkable advances in visual saliency analysis for natural scene images (NSIs), salient object detection (SOD) for optical remote sensing images (RSIs) still remains an open and challenging problem.

object-detection Object Detection +1

Efficient Adversarial Attacks for Visual Object Tracking

no code implementations ECCV 2020 Siyuan Liang, Xingxing Wei, Siyuan Yao, Xiaochun Cao

In this paper, we analyze the weakness of object trackers based on the Siamese network and then extend adversarial examples to visual object tracking.

Object Visual Object Tracking +1

Face Super-Resolution Guided by 3D Facial Priors

1 code implementation ECCV 2020 Xiaobin Hu, Wenqi Ren, John LaMaster, Xiaochun Cao, Xiaoming Li, Zechao Li, Bjoern Menze, Wei Liu

State-of-the-art face super-resolution methods employ deep convolutional neural networks to learn a mapping between low- and high- resolution facial patterns by exploring local appearance knowledge.

Super-Resolution

Fast Stochastic Ordinal Embedding with Variance Reduction and Adaptive Step Size

no code implementations1 Dec 2019 Ke Ma, Jinshan Zeng, Qianqian Xu, Xiaochun Cao, Wei Liu, Yuan YAO

Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years.

Generalized Block-Diagonal Structure Pursuit: Learning Soft Latent Task Assignment against Negative Transfer

1 code implementation NeurIPS 2019 Zhiyong Yang, Qianqian Xu, Yangbangyan Jiang, Xiaochun Cao, Qingming Huang

Different from most of the previous work, pursuing the Block-Diagonal structure of LTAM (assigning latent tasks to output tasks) alleviates negative transfer via collaboratively grouping latent tasks and output tasks such that inter-group knowledge transfer and sharing is suppressed.

Attribute Multi-Task Learning

DM2C: Deep Mixed-Modal Clustering

1 code implementation NeurIPS 2019 Yangbangyan Jiang, Qianqian Xu, Zhiyong Yang, Xiaochun Cao, Qingming Huang

Instead of transforming all the samples into a joint modality-independent space, our framework learns the mappings across individual modal spaces by virtue of cycle-consistency.

Clustering

Collaborative Preference Embedding against Sparse Labels

1 code implementation ACM MM 2019 Shilong Bao, Qianqian Xu, Ke Ma, Zhiyong Yang, Xiaochun Cao, Qingming Huang

From the margin theory point-of-view, we then propose a generalization enhancement scheme for sparse and insufficient labels via optimizing the margin distribution.

Collaborative Filtering Decision Making +3

iSplit LBI: Individualized Partial Ranking with Ties via Split LBI

1 code implementation NeurIPS 2019 Qianqian Xu, Xinwei Sun, Zhiyong Yang, Xiaochun Cao, Qingming Huang, Yuan YAO

In this paper, instead of learning a global ranking which is agreed with the consensus, we pursue the tie-aware partial ranking from an individualized perspective.

Identifying and Resisting Adversarial Videos Using Temporal Consistency

no code implementations11 Sep 2019 Xiaojun Jia, Xingxing Wei, Xiaochun Cao

We propose the temporal defense, which reconstructs the polluted frames with their temporally neighbor clean frames, to deal with the adversarial videos with sparse polluted frames.

Video Classification

Learning Personalized Attribute Preference via Multi-task AUC Optimization

no code implementations18 Jun 2019 Zhiyong Yang, Qianqian Xu, Xiaochun Cao, Qingming Huang

Traditionally, most of the existing attribute learning methods are trained based on the consensus of annotations aggregated from a limited number of annotators.

Attribute

Single Image Deraining: A Comprehensive Benchmark Analysis

1 code implementation CVPR 2019 Siyuan Li, Iago Breno Araujo, Wenqi Ren, Zhangyang Wang, Eric K. Tokuda, Roberto Hirata Junior, Roberto Cesar-Junior, Jiawan Zhang, Xiaojie Guo, Xiaochun Cao

We present a comprehensive study and evaluation of existing single image deraining algorithms, using a new large-scale benchmark consisting of both synthetic and real-world rainy images. This dataset highlights diverse data sources and image contents, and is divided into three subsets (rain streak, rain drop, rain and mist), each serving different training or evaluation purposes.

Single Image Deraining

Deep Robust Subjective Visual Property Prediction in Crowdsourcing

no code implementations CVPR 2019 Qianqian Xu, Zhiyong Yang, Yangbangyan Jiang, Xiaochun Cao, Qingming Huang, Yuan YAO

The problem of estimating subjective visual properties (SVP) of images (e. g., Shoes A is more comfortable than B) is gaining rising attention.

Property Prediction

Robust Ordinal Embedding from Contaminated Relative Comparisons

1 code implementation5 Dec 2018 Ke Ma, Qianqian Xu, Xiaochun Cao

Existing ordinal embedding methods usually follow a two-stage routine: outlier detection is first employed to pick out the inconsistent comparisons; then an embedding is learned from the clean data.

Outlier Detection

Less but Better: Generalization Enhancement of Ordinal Embedding via Distributional Margin

1 code implementation5 Dec 2018 Ke Ma, Qianqian Xu, Zhiyong Yang, Xiaochun Cao

To address the issue of insufficient training samples, we propose a margin distribution learning paradigm for ordinal embedding, entitled Distributional Margin based Ordinal Embedding (\textit{DMOE}).

Deep Non-Blind Deconvolution via Generalized Low-Rank Approximation

no code implementations NeurIPS 2018 Wenqi Ren, Jiawei Zhang, Lin Ma, Jinshan Pan, Xiaochun Cao, WangMeng Zuo, Wei Liu, Ming-Hsuan Yang

In this paper, we present a deep convolutional neural network to capture the inherent properties of image degradation, which can handle different kernels and saturated pixels in a unified framework.

Deblurring

Transferable Adversarial Attacks for Image and Video Object Detection

2 code implementations30 Nov 2018 Xingxing Wei, Siyuan Liang, Ning Chen, Xiaochun Cao

Adversarial examples have been demonstrated to threaten many computer vision tasks including object detection.

Generative Adversarial Network Object +2

ComDefend: An Efficient Image Compression Model to Defend Adversarial Examples

1 code implementation CVPR 2019 Xiaojun Jia, Xingxing Wei, Xiaochun Cao, Hassan Foroosh

In other words, ComDefend can transform the adversarial image to its clean version, which is then fed to the trained classifier.

Image Compression

HSCS: Hierarchical Sparsity Based Co-saliency Detection for RGBD Images

no code implementations16 Nov 2018 Runmin Cong, Jianjun Lei, Huazhu Fu, Qingming Huang, Xiaochun Cao, Nam Ling

In this paper, we propose a novel co-saliency detection method for RGBD images based on hierarchical sparsity reconstruction and energy function refinement.

Co-Salient Object Detection

A Margin-based MLE for Crowdsourced Partial Ranking

no code implementations29 Jul 2018 Qianqian Xu, Jiechao Xiong, Xinwei Sun, Zhiyong Yang, Xiaochun Cao, Qingming Huang, Yuan YAO

A preference order or ranking aggregated from pairwise comparison data is commonly understood as a strict total order.

Disc-aware Ensemble Network for Glaucoma Screening from Fundus Image

3 code implementations19 May 2018 Huazhu Fu, Jun Cheng, Yanwu Xu, Changqing Zhang, Damon Wing Kee Wong, Jiang Liu, Xiaochun Cao

Specifically, a novel Disc-aware Ensemble Network (DENet) for automatic glaucoma screening is proposed, which integrates the deep hierarchical context of the global fundus image and the local optic disc region.

From Social to Individuals: a Parsimonious Path of Multi-level Models for Crowdsourced Preference Aggregation

no code implementations8 Mar 2018 Qianqian Xu, Jiechao Xiong, Xiaochun Cao, Qingming Huang, Yuan YAO

In crowdsourced preference aggregation, it is often assumed that all the annotators are subject to a common preference or social utility function which generates their comparison behaviors in experiments.

Fake Colorized Image Detection

no code implementations9 Jan 2018 Yuanfang Guo, Xiaochun Cao, Wei zhang, Rui Wang

Based on our observations, i. e., potential traces in the hue, saturation, dark and bright channels, we propose two simple yet effective detection methods for fake colorized images: Histogram based Fake Colorized Image Detection (FCID-HIST) and Feature Encoding based Fake Colorized Image Detection (FCID-FE).

Multimedia

Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation

3 code implementations3 Jan 2018 Huazhu Fu, Jun Cheng, Yanwu Xu, Damon Wing Kee Wong, Jiang Liu, Xiaochun Cao

The proposed M-Net mainly consists of multi-scale input layer, U-shape convolutional network, side-output layer, and multi-label loss function.

Segmentation

From Common to Special: When Multi-Attribute Learning Meets Personalized Opinions

no code implementations18 Nov 2017 Zhiyong Yang, Qianqian Xu, Xiaochun Cao, Qingming Huang

However, both categories ignore the joint effect of the two mentioned factors: the personal diversity with respect to the global consensus; and the intrinsic correlation among multiple attributes.

Attribute feature selection

Stochastic Non-convex Ordinal Embedding with Stabilized Barzilai-Borwein Step Size

1 code implementation17 Nov 2017 Ke Ma, Jinshan Zeng, Jiechao Xiong, Qianqian Xu, Xiaochun Cao, Wei Liu, Yuan YAO

Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years.

An Iterative Co-Saliency Framework for RGBD Images

no code implementations4 Nov 2017 Runmin Cong, Jianjun Lei, Huazhu Fu, Weisi Lin, Qingming Huang, Xiaochun Cao, Chunping Hou

In this paper, we propose an iterative RGBD co-saliency framework, which utilizes the existing single saliency maps as the initialization, and generates the final RGBD cosaliency map by using a refinement-cycle model.

Co-Salient Object Detection

Co-saliency Detection for RGBD Images Based on Multi-constraint Feature Matching and Cross Label Propagation

no code implementations14 Oct 2017 Runmin Cong, Jianjun Lei, Huazhu Fu, Qingming Huang, Xiaochun Cao, Chunping Hou

Different from the most existing co-saliency methods focusing on RGB images, this paper proposes a novel co-saliency detection model for RGBD images, which utilizes the depth information to enhance identification of co-saliency.

Co-Salient Object Detection

Video Deblurring via Semantic Segmentation and Pixel-Wise Non-Linear Kernel

no code implementations ICCV 2017 Wenqi Ren, Jinshan Pan, Xiaochun Cao, Ming-Hsuan Yang

We analyze the relationship between motion blur trajectory and optical flow, and present a novel pixel-wise non-linear kernel model to account for motion blur.

Deblurring Optical Flow Estimation +1

Binarized Mode Seeking for Scalable Visual Pattern Discovery

no code implementations CVPR 2017 Wei Zhang, Xiaochun Cao, Rui Wang, Yuanfang Guo, Zhineng Chen

Second, we further extend bMS to a more general form, namely contrastive binary mean shift (cbMS), which maximizes the contrastive density in binary space, for finding informative patterns that are both frequent and discriminative for the dataset.

Latent Multi-View Subspace Clustering

no code implementations CVPR 2017 Changqing Zhang, QinGhua Hu, Huazhu Fu, Pengfei Zhu, Xiaochun Cao

In this paper, we propose a novel Latent Multi-view Subspace Clustering (LMSC) method, which clusters data points with latent representation and simultaneously explores underlying complementary information from multiple views.

Clustering Multi-view Subspace Clustering

Cascade one-vs-rest detection network for fine-grained recognition without part annotations

no code implementations28 Feb 2017 Long Chen, Junyu Dong, Shengke Wang, Kin-Man Lam, Muwei Jian, Hua Zhang, Xiaochun Cao

To bridge this gap, we introduce a cascaded structure to eliminate background and exploit a one-vs-rest loss to capture more minute variances among different subordinate categories.

Object

Parsimonious Mixed-Effects HodgeRank for Crowdsourced Preference Aggregation

no code implementations12 Jul 2016 Qianqian Xu, Jiechao Xiong, Xiaochun Cao, Yuan YAO

In crowdsourced preference aggregation, it is often assumed that all the annotators are subject to a common preference or utility function which generates their comparison behaviors in experiments.

CUNet: A Compact Unsupervised Network for Image Classification

no code implementations6 Jul 2016 Le Dong, Ling He, Gaipeng Kong, Qianni Zhang, Xiaochun Cao, Ebroul Izquierdo

In this paper, we propose a compact network called CUNet (compact unsupervised network) to counter the image classification challenge.

Classification General Classification +1

A Hierarchical Distributed Processing Framework for Big Image Data

no code implementations3 Jul 2016 Le Dong, Zhiyu Lin, Yan Liang, Ling He, Ning Zhang, Qi Chen, Xiaochun Cao, Ebroul lzquierdo

The proposed ICP framework consists of two mechanisms, i. e. SICP (Static ICP) and DICP (Dynamic ICP).

SketchNet: Sketch Classification With Web Images

no code implementations CVPR 2016 Hua Zhang, Si Liu, Changqing Zhang, Wenqi Ren, Rui Wang, Xiaochun Cao

In this study, we present a weakly supervised approach that discovers the discriminative structures of sketch images, given pairs of sketch images and web images.

Classification General Classification

False Discovery Rate Control and Statistical Quality Assessment of Annotators in Crowdsourced Ranking

no code implementations19 May 2016 Qianqian Xu, Jiechao Xiong, Xiaochun Cao, Yuan YAO

With the rapid growth of crowdsourcing platforms it has become easy and relatively inexpensive to collect a dataset labeled by multiple annotators in a short time.

Position Sociology

Makeup like a superstar: Deep Localized Makeup Transfer Network

no code implementations25 Apr 2016 Si Liu, Xinyu Ou, Ruihe Qian, Wei Wang, Xiaochun Cao

In this paper, we propose a novel Deep Localized Makeup Transfer Network to automatically recommend the most suitable makeup for a female and synthesis the makeup on her face.

Quadratic Projection Based Feature Extraction with Its Application to Biometric Recognition

no code implementations25 Mar 2016 Yan Yan, Hanzi Wang, Si Chen, Xiaochun Cao, David Zhang

This paper presents a novel quadratic projection based feature extraction framework, where a set of quadratic matrices is learned to distinguish each class from all other classes.

Low-Rank Tensor Constrained Multiview Subspace Clustering

no code implementations ICCV 2015 Changqing Zhang, Huazhu Fu, Si Liu, Guangcan Liu, Xiaochun Cao

We introduce a low-rank tensor constraint to explore the complementary information from multiple views and, accordingly, establish a novel method called Low-rank Tensor constrained Multiview Subspace Clustering (LT-MSC).

Clustering

Diversity-Induced Multi-View Subspace Clustering

no code implementations CVPR 2015 Xiaochun Cao, Changqing Zhang, Huazhu Fu, Si Liu, Hua Zhang

In this paper, we focus on how to boost the multi-view clustering by exploring the complementary information among multi-view features.

Clustering Face Clustering +1

Matching-CNN Meets KNN: Quasi-Parametric Human Parsing

no code implementations CVPR 2015 Si Liu, Xiaodan Liang, Luoqi Liu, Xiaohui Shen, Jianchao Yang, Changsheng Xu, Liang Lin, Xiaochun Cao, Shuicheng Yan

Under the classic K Nearest Neighbor (KNN)-based nonparametric framework, the parametric Matching Convolutional Neural Network (M-CNN) is proposed to predict the matching confidence and displacements of the best matched region in the testing image for a particular semantic region in one KNN image.

Human Parsing

Evaluating Visual Properties via Robust HodgeRank

no code implementations15 Aug 2014 Qianqian Xu, Jiechao Xiong, Xiaochun Cao, Qingming Huang, Yuan YAO

In this paper we study the problem of how to estimate such visual properties from a ranking perspective with the help of the annotators from online crowdsourcing platforms.

Graph Sampling Outlier Detection

Sparse Dictionary Learning for Edit Propagation of High-Resolution Images

no code implementations CVPR 2014 Xiaowu Chen, Dongqing Zou, Jianwei Li, Xiaochun Cao, Qinping Zhao, Hao Zhang

Previous approaches for edit propagation typically employ a global optimization over the whole set of image pixels, incurring a prohibitively high memory and time consumption for high-resolution images.

Dictionary Learning Vocal Bursts Intensity Prediction

Robust Separation of Reflection from Multiple Images

no code implementations CVPR 2014 Xiaojie Guo, Xiaochun Cao, Yi Ma

When one records a video/image sequence through a transparent medium (e. g. glass), the image is often a superposition of a transmitted layer (scene behind the medium) and a reflected layer.

Video Editing with Temporal, Spatial and Appearance Consistency

no code implementations CVPR 2013 Xiaojie Guo, Xiaochun Cao, Xiaowu Chen, Yi Ma

Given an area of interest in a video sequence, one may want to manipulate or edit the area, e. g. remove occlusions from or replace with an advertisement on it.

Image Matting Video Editing

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