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Yangbangyan Jiang

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6 papers

1 author row

AAAI Conference 2024 Conference Paper

ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly Detection

Junwei He
Qianqian Xu
Yangbangyan Jiang
Zitai Wang
Qingming Huang

Graph anomaly detection is crucial for identifying nodes that deviate from regular behavior within graphs, benefiting various domains such as fraud detection and social network. Although existing reconstruction-based methods have achieved considerable success, they may face the Anomaly Overfitting and Homophily Trap problems caused by the abnormal patterns in the graph, breaking the assumption that normal nodes are often better reconstructed than abnormal ones. Our observations indicate that models trained on graphs with fewer anomalies exhibit higher detection performance. Based on this insight, we introduce a novel two-stage framework called Anomaly-Denoised Autoencoders for Graph Anomaly Detection (ADA-GAD). In the first stage, we design a learning-free anomaly-denoised augmentation method to generate graphs with reduced anomaly levels. We pretrain graph autoencoders on these augmented graphs at multiple levels, which enables the graph autoencoders to capture normal patterns. In the next stage, the decoders are retrained for detection on the original graph, benefiting from the multi-level representations learned in the previous stage. Meanwhile, we propose the node anomaly distribution regularization to further alleviate Anomaly Overfitting. We validate the effectiveness of our approach through extensive experiments on both synthetic and real-world datasets.

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NeurIPS Conference 2024 Conference Paper

AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentation

Boyu Han
Qianqian Xu
Zhiyong Yang
Shilong Bao
Peisong Wen
Yangbangyan Jiang
Qingming Huang

The Area Under the ROC Curve (AUC) is a well-known metric for evaluating instance-level long-tail learning problems. In the past two decades, many AUC optimization methods have been proposed to improve model performance under long-tail distributions. In this paper, we explore AUC optimization methods in the context of pixel-level long-tail semantic segmentation, a much more complicated scenario. This task introduces two major challenges for AUC optimization techniques. On one hand, AUC optimization in a pixel-level task involves complex coupling across loss terms, with structured inner-image and pairwise inter-image dependencies, complicating theoretical analysis. On the other hand, we find that mini-batch estimation of AUC loss in this case requires a larger batch size, resulting in an unaffordable space complexity. To address these issues, we develop a pixel-level AUC loss function and conduct a dependency-graph-based theoretical analysis of the algorithm's generalization ability. Additionally, we design a Tail-Classes Memory Bank (T-Memory Bank) to manage the significant memory demand. Finally, comprehensive experiments across various benchmarks confirm the effectiveness of our proposed AUCSeg method. The code is available at https: //github. com/boyuh/AUCSeg.

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AAAI Conference 2021 Conference Paper

Deep Partial Rank Aggregation for Personalized Attributes

Qianqian Xu
Zhiyong Yang
Zuyao Chen
Yangbangyan Jiang
Xiaochun Cao
Yuan Yao
Qingming Huang

In this paper, we study the problem of how to aggregate pairwise personalized attributes (PA) annotations (e. g. , Shoes A is more comfortable than B) from different annotators on the crowdsourcing platforms, which is an emerging topic gaining increasing attention in recent years. Given the crowdsourced annotations, the majority of the traditional literature assumes that all the pairs in the collected dataset are distinguishable. However, this assumption is incompatible with how humans perceive attributes since indistinguishable pairs are ubiquitous for the annotators due to the limitation of human perception. To attack this problem, we propose a novel deep prediction model that could simultaneously detect the indistinguishable pairs and aggregate ranking results for distinguishable pairs. First of all, we represent the pairwise annotations as a multi-graph. Based on such data structure, we propose an end-to-end partial ranking model which consists of a deep backbone architecture and a probabilistic model that captures the generative process of the partial rank annotations. Specifically, to recognize the indistinguishable pairs, the probabilistic model we proposed is equipped with an adaptive perception threshold, where indistinguishable pairs could be automatically detected when the absolute value of the score difference is below the learned threshold. In our empirical studies, we perform a series of experiments on three real-world datasets: LFW-10, Shoes, and Sun. The corresponding results consistently show the superiority of our proposed model.

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AAAI Conference 2021 Conference Paper

What to Select: Pursuing Consistent Motion Segmentation from Multiple Geometric Models

Yangbangyan Jiang
Qianqian Xu
Ke Ma
Zhiyong Yang
Xiaochun Cao
Qingming Huang

Motion segmentation aims at separating motions of different moving objects in a video sequence. Facing the complicated real-world scenes, recent studies reveal that combining multiple geometric models would be a more effective way than just employing a single one. This motivates a new wave of model-fusion based motion segmentation methods. However, the vast majority of models of this kind merely seek consensus in spectral embeddings. We argue that a simple consensus might be insufficient to filter out the harmful information which is either unreliable or semantically unrelated to the segmentation task. Therefore, how to automatically select valuable patterns across multiple models should be regarded as a key challenge here. In this paper, we present a novel geometric-model-fusion framework for motion segmentation, which targets at constructing a consistent affinity matrix across all the geometric models. Specifically, it incorporates the structural information shared by affinity matrices to select those semantically consistent entries. Meanwhile, a multiplicative decomposition scheme is adopted to ensure structural consistency among multiple affinities. To solve this problem, an alternative optimization scheme is proposed, together with a proof of its global convergence. Experiments on four real-world benchmarks show the superiority of the proposed method.

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NeurIPS Conference 2019 Conference Paper

DM2C: Deep Mixed-Modal Clustering

Yangbangyan Jiang
Qianqian Xu
Zhiyong Yang
Xiaochun Cao
Qingming Huang

Data exhibited with multiple modalities are ubiquitous in real-world clustering tasks. Most existing methods, however, pose a strong assumption that the pairing information for modalities is available for all instances. In this paper, we consider a more challenging task where each instance is represented in only one modality, which we call mixed-modal data. Without any extra pairing supervision across modalities, it is difficult to find a universal semantic space for all of them. To tackle this problem, we present an adversarial learning framework for clustering with mixed-modal data. 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. Through these mappings, we could easily unify all the samples into a single modal space and perform the clustering. Evaluations on several real-world mixed-modal datasets could demonstrate the superiority of our proposed framework.

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NeurIPS Conference 2019 Conference Paper

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

Zhiyong Yang
Qianqian Xu
Yangbangyan Jiang
Xiaochun Cao
Qingming Huang

In multi-task learning, a major challenge springs from a notorious issue known as negative transfer, which refers to the phenomenon that sharing the knowledge with dissimilar and hard tasks often results in a worsened performance. To circumvent this issue, we propose a novel multi-task learning method, which simultaneously learns latent task representations and a block-diagonal Latent Task Assignment Matrix (LTAM). 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. This goal is challenging, since 1) our notion of Block-Diagonal Property extends the traditional notion for square matrices where the $i$-th column and the $i$-th column represents the same concept; 2) marginal constraints on rows and columns are also required for avoiding isolated latent/output tasks. Facing such challenges, we propose a novel regularizer by means of an equivalent spectral condition realizing this generalized block-diagonal property. Practically, we provide a relaxation scheme which improves the flexibility of the model. With the objective function given, we then propose an alternating optimization method, which not only tells how negative transfer is alleviated in our method but also reveals an interesting connection between our method and the optimal transport problem. Finally, the method is demonstrated on a simulation dataset, three real-world benchmark datasets and further applied to personalized attribute predictions.

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