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Ming Shao

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10 papers

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AAAI Conference 2025 Conference Paper

Supportive Negatives Spectral Augmentation for Source-Free Cross-Domain Segmentation

Kexin Zheng
Haifeng Xia
Siyu Xia
Ming Shao
Zhengming Ding

Source-free domain adaptation (SFDA) aims to transfer knowledge from the well-trained source model and optimize it to adapt target data distribution. SFDA methods are suitable for medical image segmentation task due to its data-privacy protection and achieve promising performances. However, cross-domain distribution shift makes it difficult for the adapted model to provide accurate decisions on several hard instances and negatively affects model generalization. To overcome this limitation, a novel method `supportive negatives spectral augmentation' (SNSA) is presented in this work. Concretely, SNSA includes the instance selection mechanism to automatically discover a few hard samples for which source model produces incorrect predictions. And, active learning strategy is adopted to re-calibrate their predictive masks. Moreover, SNSA deploys the spectral augmentation between hard instances and others to encourage source model to gradually capture and adapt the attributions of target distribution. Considerable experimental studies demonstrate that annotating merely 4%~5% of negative instances from the target domain significantly improves segmentation performance over previous methods.

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AAAI Conference 2020 Conference Paper

Adversary for Social Good: Protecting Familial Privacy through Joint Adversarial Attacks

Chetan Kumar
Riazat Ryan
Ming Shao

Social media has been widely used among billions of people with dramatical participation of new users every day. Among them, social networks maintain the basic social characters and host huge amount of personal data. While protecting user sensitive data is obvious and demanding, information leakage due to adversarial attacks is somehow unavoidable, yet hard to detect. For example, implicit social relation such as family information may be simply exposed by network structure and hosted face images through off-the-shelf graph neural networks (GNN), which will be empirically proved in this paper. To address this issue, in this paper, we propose a novel adversarial attack algorithm for social good. First, we start from conventional visual family understanding problem, and demonstrate that familial information can easily be exposed to attackers by connecting sneak shots to social networks. Second, to protect family privacy on social networks, we propose a novel adversarial attack algorithm that produces both adversarial features and graph under a given budget. Specifically, both features on the node and edges between nodes will be perturbed gradually such that the probe images and its family information can not be identiﬁed correctly through conventional GNN. Extensive experiments on a popular visual social dataset have demonstrated that our defense strategy can signiﬁcantly mitigate the impacts of family information leakage.

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IJCAI Conference 2018 Conference Paper

Robust Multi-view Representation: A Unified Perspective from Multi-view Learning to Domain Adaption

Zhengming Ding
Ming Shao
Yun Fu

Multi-view data are extensively accessible nowadays thanks to various types of features, different view-points and sensors which tend to facilitate better representation in many key applications. This survey covers the topic of robust multi-view data representation, centered around several major visual applications. First of all, we formulate a unified learning framework which is able to model most existing multi-view learning and domain adaptation in this line. Following this, we conduct a comprehensive discussion across these two problems by reviewing the algorithms along these two topics, including multi-view clustering, multi-view classification, zero-shot learning, and domain adaption. We further present more practical challenges in multi-view data analysis. Finally, we discuss future research including incomplete, unbalance, large-scale multi-view learning. This would benefit AI community from literature review to future direction.

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AAAI Conference 2016 Conference Paper

Consensus Guided Unsupervised Feature Selection

Hongfu Liu
Ming Shao
Yun Fu

Feature selection has been widely recognized as one of the key problems in data mining and machine learning community, especially for high-dimensional data with redundant information, partial noises and outliers. Recently, unsupervised feature selection attracts substantial research attentions since data acquisition is rather cheap today but labeling work is still expensive and time consuming. This is speciﬁcally useful for effective feature selection of clustering tasks. Recent works using sparse projection with pre-learned pseudo labels achieve appealing results; however, they generate pseudo labels with all features so that noisy and ineffective features degrade the cluster structure and further harm the performance of feature selection; besides, these methods suffer from complex composition of multiple constraints and computational inefﬁciency, e. g. , eigen-decomposition. Differently, in this work we introduce consensus clustering for pseudo labeling, which gets rid of expensive eigen-decomposition and provides better clustering accuracy with high robustness. In addition, complex constraints such as non-negative are removed due to the crisp indicators of consensus clustering. Speciﬁcally, we propose one efﬁcient formulation for our unsupervised feature selection by using the utility function and provide theoretical analysis on optimization rules and model convergence. Extensive experiments on several realworld data sets demonstrate that our methods are superior to the most recent state-of-the-art works in terms of NMI.

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AAAI Conference 2016 Conference Paper

Consensus Style Centralizing Auto-Encoder for Weak Style Classification

Shuhui Jiang
Ming Shao
Chengcheng Jia
Yun Fu

Style classiﬁcation (e. g. , architectural, music, fashion) attracts an increasing attention in both research and industrial ﬁelds. Most existing works focused on low-level visual features composition for style representation. However, little effort has been devoted to automatic mid-level or high-level style features learning by reorganizing low-level descriptors. Moreover, styles are usually spread out and not easy to differentiate from one to another. In this paper, we call these less representative images as weak style images. To address these issues, we propose a consensus style centralizing autoencoder (CSCAE) to extract robust style features to facilitate weak style classiﬁcation. CSCAE is the ensemble of several style centralizing auto-encoders (SCAEs) with consensus constraint. Each SCAE centralizes each feature of certain category in a progressive way. We apply our method in fashion style classiﬁcation and manga style classiﬁcation as two example applications. In addition, we collect a new dataset, Online Shopping, for fashion style classiﬁcation evaluation, which will be publicly available for vision based fashion style research. Experiments demonstrate the effectiveness of SCAE and CSCAE on both public and newly collected datasets when compared with the most recent state-of-the-art works.

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AAAI Conference 2016 Conference Paper

Spectral Bisection Tree Guided Deep Adaptive Exemplar Autoencoder for Unsupervised Domain Adaptation

Ming Shao
Zhengming Ding
Handong Zhao
Yun Fu

Learning with limited labeled data is always a challenge in AI problems, and one of promising ways is transferring wellestablished source domain knowledge to the target domain, i. e. , domain adaptation. In this paper, we extend the deep representation learning to domain adaptation scenario, and propose a novel deep model called “Deep Adaptive Exemplar AutoEncoder (DAE2 )”. Different from conventional denoising autoencoders using corrupted inputs, we assign semantics to the input-output pairs of the autoencoders, which allow us to gradually extract discriminant features layer by layer. To this end, ﬁrst, we build a spectral bisection tree to generate source-target data compositions as the training pairs fed to autoencoders. Second, a low-rank coding regularizer is imposed to ensure the transferability of the learned hidden layer. Finally, a supervised layer is added on top to transform learned representations into discriminant features. The problem above can be solved iteratively in an EM fashion of learning. Extensive experiments on domain adaptation tasks including object, handwritten digits, and text data classiﬁcations demonstrate the effectiveness of the proposed method.

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IJCAI Conference 2015 Conference Paper

Cross-View Projective Dictionary Learning for Person Re-Identification

Sheng Li
Ming Shao
Yun Fu

Person re-identification plays an important role in many safety-critical applications. Existing works mainly focus on extracting patch-level features or learning distance metrics. However, the representation power of extracted features might be limited, due to the various viewing conditions of pedestrian images in reality. To improve the representation power of features, we learn discriminative and robust representations via dictionary learning in this paper. First, we propose a cross-view projective dictionary learning (CPDL) approach, which learns effective features for persons across different views. CPDL is a general framework for multiview dictionary learning. Secondly, by utilizing the CPDL framework, we design two objectives to learn low-dimensional representations for each pedestrian in the patch-level and the image-level, respectively. The proposed objectives can capture the intrinsic relationships of different representation coefficients in various settings. We devise efficient optimization algorithms to solve the objectives. Finally, a fusion strategy is utilized to generate the similarity scores. Experiments on the public VIPeR and CUHK Campus datasets show that our approach achieves the state-of-the-art performance.

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IJCAI Conference 2015 Conference Paper

Deep Linear Coding for Fast Graph Clustering

Ming Shao
Sheng Li
Zhengming Ding
Yun Fu

Clustering has been one of the most critical unsupervised learning techniques that has been widely applied in data mining problems. As one of its branches, graph clustering enjoys its popularity due to its appealing performance and strong theoretical supports. However, the eigen-decomposition problems involved are computationally expensive. In this paper, we propose a deep structure with a linear coder as the building block for fast graph clustering, called Deep Linear Coding (DLC). Different from conventional coding schemes, we jointly learn the feature transform function and discriminative codings, and guarantee that the learned codes are robust in spite of local distortions. In addition, we use the proposed linear coders as the building blocks to formulate a deep structure to further refine features in a layerwise fashion. Extensive experiments on clustering tasks demonstrate that our method performs well in terms of both time complexity and clustering accuracy. On a large-scale benchmark dataset (580K), our method runs 1500 times faster than the original spectral clustering.

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IJCAI Conference 2015 Conference Paper

Deep Low-Rank Coding for Transfer Learning

Zhengming Ding
Ming Shao
Yun Fu

Recent researches on transfer learning exploit deep structures for discriminative feature representation to tackle cross-domain disparity. However, few of them are able to joint feature learning and knowledge transfer in a unified deep framework. In this paper, we develop a novel approach, called Deep Low-Rank Coding (DLRC), for transfer learning. Specifically, discriminative low-rank coding is achieved in the guidance of an iterative supervised structure term for each single layer. In this way, both marginal and conditional distributions between two domains intend to be mitigated. In addition, a marginalized denoising feature transformation is employed to guarantee the learned singlelayer low-rank coding to be robust despite of corruptions or noises. Finally, by stacking multiple layers of low-rank codings, we manage to learn robust cross-domain features from coarse to fine. Experimental results on several benchmarks have demonstrated the effectiveness of our proposed algorithm on facilitating the recognition performance for the target domain.

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IJCAI Conference 2011 Conference Paper

Kinship Verification through Transfer Learning

Siyu Xia
Ming Shao
Yun Fu

Because of the inevitable impact factors such as pose, expression, lighting and aging on faces, identity verification through faces is still an unsolved problem. Research on biometrics raises an even challenging problem - is it possible to determine the kinship merely based on face images? A critical observation that faces of parents captured while they were young are more alike their children's compared with images captured when they are old has been revealed by genetics studies. This enlightens us the following research. First, a new kinship database named UB KinFace composed of child, young parent and old parent face images is collected from Internet. Second, an extended transfer subspace learning method is proposed aiming at mitigating the enormous divergence of distributions between children and old parents. The key idea is to utilize an intermediate distribution close to both the source and target distributions to bridge them and reduce the divergence. Naturally the young parent set is suitable for this task. Through this learning process, the large gap between distributions can be significantly reduced and kinship verification problem becomes more discriminative. Experimental results show that our hypothesis on the role of young parents is valid and transfer learning is effective to enhance the verification accuracy.

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