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Haochao Ying

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8 papers

2 author rows

IJCAI Conference 2025 Conference Paper

Dual-level Fuzzy Learning with Patch Guidance for Image Ordinal Regression

Chunlai Dong
Haochao Ying
Qibo Qiu
Jinhong Wang
Danny Chen
Jian Wu

Ordinal regression bridges regression and classification by assigning objects to ordered classes. While human experts rely on discriminative patch-level features for decisions, current approaches are limited by the availability of only image-level ordinal labels, overlooking fine-grained patch-level characteristics. In this paper, we propose a Dual-level Fuzzy Learning with Patch Guidance framework, named DFPG that learns precise feature-based grading boundaries from ambiguous ordinal labels, with patch-level supervision. Specifically, we propose patch-labeling and filtering strategies to enable the model to focus on patch-level features exclusively with only image-level ordinal labels available. We further design a dual-level fuzzy learning module, which leverages fuzzy logic to quantitatively capture and handle label ambiguity from both patch-wise and channel-wise perspectives. Extensive experiments on various image ordinal regression datasets demonstrate the superiority of our proposed method, further confirming its ability in distinguishing samples from difficult-to-classify categories. The code is available at https: //github. com/ZJUMAI/DFPG-ord.

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AAAI Conference 2024 Conference Paper

Arithmetic Feature Interaction Is Necessary for Deep Tabular Learning

Yi Cheng
Renjun Hu
Haochao Ying
Xing Shi
Jian Wu
Wei Lin

Until recently, the question of the effective inductive bias of deep models on tabular data has remained unanswered. This paper investigates the hypothesis that arithmetic feature interaction is necessary for deep tabular learning. To test this point, we create a synthetic tabular dataset with a mild feature interaction assumption and examine a modified transformer architecture enabling arithmetical feature interactions, referred to as AMFormer. Results show that AMFormer outperforms strong counterparts in fine-grained tabular data modeling, data efficiency in training, and generalization. This is attributed to its parallel additive and multiplicative attention operators and prompt-based optimization, which facilitate the separation of tabular samples in an extended space with arithmetically-engineered features. Our extensive experiments on real-world data also validate the consistent effectiveness, efficiency, and rationale of AMFormer, suggesting it has established a strong inductive bias for deep learning on tabular data. Code is available at https://github.com/aigc-apps/AMFormer.

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NeurIPS Conference 2024 Conference Paper

EMVP: Embracing Visual Foundation Model for Visual Place Recognition with Centroid-Free Probing

Qibo Qiu
Shun Zhang
Haiming Gao
Honghui Yang
Haochao Ying
Wenxiao Wang
Xiaofei He

Visual Place Recognition (VPR) is essential for mobile robots as it enables them to retrieve images from a database closest to their current location. The progress of Visual Foundation Models (VFMs) has significantly advanced VPR by capturing representative descriptors in images. However, existing fine-tuning efforts for VFMs often overlook the crucial role of probing in effectively adapting these descriptors for improved image representation. In this paper, we propose the Centroid-Free Probing (CFP) stage, making novel use of second-order features for more effective use of descriptors from VFMs. Moreover, to control the preservation of task-specific information adaptively based on the context of the VPR, we introduce the Dynamic Power Normalization (DPN) module in both the recalibration and CFP stages, forming a novel Parameter Efficiency Fine-Tuning (PEFT) pipeline (EMVP) tailored for the VPR task. Extensive experiments demonstrate the superiority of the proposed CFP over existing probing methods. Moreover, the EMVP pipeline can further enhance fine-tuning performance in terms of accuracy and efficiency. Specifically, it achieves 93. 9\%, 96. 5\%, and 94. 6\% Recall@1 on the MSLS Validation, Pitts250k-test, and SPED datasets, respectively, while saving 64. 3\% of trainable parameters compared with the existing SOTA PEFT method.

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IJCAI Conference 2024 Conference Paper

Personalized Heart Disease Detection via ECG Digital Twin Generation

Yaojun Hu
Jintai Chen
Lianting Hu
Dantong Li
Jiahuan Yan
Haochao Ying
Huiying Liang
Jian Wu

Heart diseases rank among the leading causes of global mortality, demonstrating a crucial need for early diagnosis and intervention. Most traditional electrocardiogram (ECG) based automated diagnosis methods are trained at population level, neglecting the customization of personalized ECGs to enhance individual healthcare management. A potential solution to address this limitation is to employ digital twins to simulate symptoms of diseases in real patients. In this paper, we present an innovative prospective learning approach for personalized heart disease detection, which generates digital twins of healthy individuals' anomalous ECGs and enhances the model sensitivity to the personalized symptoms. In our approach, a vector quantized feature separator is proposed to locate and isolate the disease symptom and normal segments in ECG signals with ECG report guidance. Thus, the ECG digital twins can simulate specific heart diseases used to train a personalized heart disease detection model. Experiments demonstrate that our approach not only excels in generating high-fidelity ECG signals but also improves personalized heart disease detection. Moreover, our approach ensures robust privacy protection, safeguarding patient data in model development. The code can be found at https: //github. com/huyjj/LAVQ-Editor.

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IJCAI Conference 2023 Conference Paper

Robust Image Ordinal Regression with Controllable Image Generation

Yi Cheng
Haochao Ying
Renjun Hu
Jinhong Wang
Wenhao Zheng
Xiao Zhang
Danny Chen
Jian Wu

Image ordinal regression has been mainly studied along the line of exploiting the order of categories. However, the issues of class imbalance and category overlap that are very common in ordinal regression were largely overlooked. As a result, the performance on minority categories is often unsatisfactory. In this paper, we propose a novel framework called CIG based on controllable image generation to directly tackle these two issues. Our main idea is to generate extra training samples with specific labels near category boundaries, and the sample generation is biased toward the less-represented categories. To achieve controllable image generation, we seek to separate structural and categorical information of images based on structural similarity, categorical similarity, and reconstruction constraints. We evaluate the effectiveness of our new CIG approach in three different image ordinal regression scenarios. The results demonstrate that CIG can be flexibly integrated with off-the-shelf image encoders or ordinal regression models to achieve improvement, and further, the improvement is more significant for minority categories.

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JBHI Journal 2022 Journal Article

Discriminative Cervical Lesion Detection in Colposcopic Images With Global Class Activation and Local Bin Excitation

Tingting Chen
Xuechen Liu
Ruiwei Feng
Wenzhe Wang
Chunnv Yuan
Weiguo Lu
Haizhen He
Honghao Gao

Accurate cervical lesion detection (CLD) methods using colposcopic images are highly demanded in computer-aided diagnosis (CAD) for automatic diagnosis of High-grade Squamous Intraepithelial Lesions (HSIL). However, compared to natural scene images, the specific characteristics of colposcopic images, such as low contrast, visual similarity, and ambiguous lesion boundaries, pose difficulties to accurately locating HSIL regions and also significantly impede the performance improvement of existing CLD approaches. To tackle these difficulties and better capture cervical lesions, we develop novel feature enhancing mechanisms from both global and local perspectives, and propose a new discriminative CLD framework, called CervixNet, with a Global Class Activation (GCA) module and a Local Bin Excitation (LBE) module. Specifically, the GCA module learns discriminative features by introducing an auxiliary classifier, and guides our model to focus on HSIL regions while ignoring noisy regions. It globally facilitates the feature extraction process and helps boost feature discriminability. Further, our LBE module excites lesion features in a local manner, and allows the lesion regions to be more fine-grained enhanced by explicitly modelling the inter-dependencies among bins of proposal feature. Extensive experiments on a number of 9888 clinical colposcopic images verify the superiority of our method (AP $_{. 75}$ = 20. 45) over state-of-the-art models on four widely used metrics.

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ICML Conference 2022 Conference Paper

ME-GAN: Learning Panoptic Electrocardio Representations for Multi-view ECG Synthesis Conditioned on Heart Diseases

Jintai Chen
Kuanlun Liao
Kun Wei
Haochao Ying
Danny Z. Chen
Jian Wu 0001

Electrocardiogram (ECG) is a widely used non-invasive diagnostic tool for heart diseases. Many studies have devised ECG analysis models (e. g. , classifiers) to assist diagnosis. As an upstream task, researches have built generative models to synthesize ECG data, which are beneficial to providing training samples, privacy protection, and annotation reduction. However, previous generative methods for ECG often neither synthesized multi-view data, nor dealt with heart disease conditions. In this paper, we propose a novel disease-aware generative adversarial network for multi-view ECG synthesis called ME-GAN, which attains panoptic electrocardio representations conditioned on heart diseases and projects the representations onto multiple standard views to yield ECG signals. Since ECG manifestations of heart diseases are often localized in specific waveforms, we propose a new "mixup normalization" to inject disease information precisely into suitable locations. In addition, we propose a "view discriminator" to revert disordered ECG views into a pre-determined order, supervising the generator to obtain ECG representing correct view characteristics. Besides, a new metric, rFID, is presented to assess the quality of the synthesized ECG signals. Comprehensive experiments verify that our ME-GAN performs well on multi-view ECG signal synthesis with trusty morbid manifestations.

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IJCAI Conference 2018 Conference Paper

Sequential Recommender System based on Hierarchical Attention Networks

Haochao Ying
Fuzhen Zhuang
Fuzheng Zhang
Yanchi Liu
Guandong Xu
Xing Xie
Hui Xiong
Jian Wu

With a large amount of user activity data accumulated, it is crucial to exploit user sequential behavior for sequential recommendations. Conventionally, user general taste and recent demand are combined to promote recommendation performances. However, existing methods often neglect that user long-term preference keep evolving over time, and building a static representation for user general taste may not adequately reflect the dynamic characters. Moreover, they integrate user-item or item-item interactions through a linear way which limits the capability of model. To this end, in this paper, we propose a novel two-layer hierarchical attention network, which takes the above properties into account, to recommend the next item user might be interested. Specifically, the first attention layer learns user long-term preferences based on the historical purchased item representation, while the second one outputs final user representation through coupling user long-term and short-term preferences. The experimental study demonstrates the superiority of our method compared with other state-of-the-art ones.

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