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Haiyang Yang

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4 papers

2 author rows

IJCAI Conference 2025 Conference Paper

APIMig: A Project-Level Cross-Multi-Version API Migration Framework Based on Evolution Knowledge Graph

Li Kuang
Qi Xie
Haiyang Yang
Yang Yang
Xiang Wei
HaoYue Kang
Yingjie Xia

API migration is essential for software maintenance due to the rapid evolution of third-party libraries where API elements may change continuously through updates. There are two main challenges for API migration at the project level, especially across multiple versions: 1) lack of specific library evolution knowledge across multi-version; 2) difficulty in identifying the chain of changes at the project level. This paper proposes a project-level cross-multi-version API migration framework APIMig. We first construct an API evolution knowledge graph (KG) to capture changes between adjacent library versions and then derive coherent cross-version API evolution knowledge by KG reasoning. Second, we design a chain exploration algorithm to track the chain of changes and aggregate the affected code segments. Finally, a large language model is employed in completing API migration by providing the API evolution knowledge and the chain of changes. We construct an evolution KG for the Lucene library from version 4. 0. 0 to 10. 1. 0 and evaluate our approach through project migration pairs that depend on different major versions. Our framework shows improvements over the baseline in migrating projects across 7 major versions, achieving average increases of 16. 52% in CodeBLEU scores and 28. 49% in VCEU scores in GPT-4o.

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TMLR Journal 2023 Journal Article

Bidirectional View based Consistency Regularization for Semi-Supervised Domain Adaptation

Yuntao Du
娟江
Hongtao Luo
Haiyang Yang
MingCai Chen
Chongjun Wang

Distinguished from unsupervised domain adaptation (UDA), semi-supervised domain adaptation (SSDA) could access a few labeled target samples during learning additionally. Although achieving remarkable progress, target supervised information is easily overwhelmed by massive source supervised information, as there are many more labeled source samples than those in the target domain. In this work, we propose a novel method BVCR that better utilizes the supervised information by three schemes, i.e., modeling, exploration, and interaction. In the modeling scheme, BVCR models the source supervision and target supervision separately to avoid target supervised information being overwhelmed by source supervised information and better utilize the target supervision. Besides, as both supervised information naturally offer distinct views for the target domain, the exploration scheme performs intra-domain consistency regularization to better explore target information with bidirectional views. Moreover, as both views are complementary to each other, the interaction scheme introduces inter-domain consistency regularization to activate information interaction bidirectionally. Thus, the proposed method is elegantly symmetrical by design and easy to implement. Extensive experiments are conducted, and the results show the effectiveness of the proposed method.

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ICLR Conference 2023 Conference Paper

Cycle-consistent Masked AutoEncoder for Unsupervised Domain Generalization

Haiyang Yang
Xiaotong Li
Shixiang Tang
Feng Zhu 0006
Yizhou Wang 0007
Meilin Chen
Lei Bai 0001
Rui Zhao 0001

Self-supervised learning methods undergo undesirable performance drops when there exists a significant domain gap between training and testing scenarios. Therefore, unsupervised domain generalization (UDG) is proposed to tackle the problem, which requires the model to be trained on several different domains without supervision and generalize well on unseen test domains. Existing methods either rely on a cross-domain and semantically consistent image pair in contrastive methods or the reconstruction pair in generative methods, while the precious image pairs are not available without semantic labels. In this paper, we propose a cycle cross-domain reconstruction task for unsupervised domain generalization in the absence of paired images. The cycle cross-domain reconstruction task converts a masked image from one domain to another domain and then reconstructs the original image from the converted images. To preserve the divergent domain knowledge of decoders in the cycle reconstruction task, we propose a novel domain-contrastive loss to regularize the domain information in reconstructed images encoded with the desirable domain style. Qualitative results on extensive datasets illustrate our method improves the state-of-the-art unsupervised domain generalization methods by average $\textbf{+5.59\%}, \textbf{+4.52\%}, \textbf{+4.22\%}, \textbf{+7.02\%}$ on $1\%, 5\%, 10\%, 100\%$ PACS, and $\textbf{+5.08\%}, \textbf{+6.49\%}, \textbf{+1.79\%}, \textbf{+0.53\%}$ on $1\%, 5\%, 10\%, 100\%$ DomainNet, respectively.

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NeurIPS Conference 2022 Conference Paper

Unsupervised Object Detection Pretraining with Joint Object Priors Generation and Detector Learning

Yizhou Wang
Meilin Chen
Shixiang Tang
Feng Zhu
Haiyang Yang
Lei Bai
Rui Zhao
Yunfeng Yan

Unsupervised pretraining methods for object detection aim to learn object discrimination and localization ability from large amounts of images. Typically, recent works design pretext tasks that supervise the detector to predict the defined object priors. They normally leverage heuristic methods to produce object priors, \emph{e. g. ,} selective search, which separates the prior generation and detector learning and leads to sub-optimal solutions. In this work, we propose a novel object detection pretraining framework that could generate object priors and learn detectors jointly by generating accurate object priors from the model itself. Specifically, region priors are extracted by attention maps from the encoder, which highlights foregrounds. Instance priors are the selected high-quality output bounding boxes of the detection decoder. By assuming objects as instances in the foreground, we can generate object priors with both region and instance priors. Moreover, our object priors are jointly refined along with the detector optimization. With better object priors as supervision, the model could achieve better detection capability, which in turn promotes the object priors generation. Our method improves the competitive approaches by \textbf{+1. 3 AP}, \textbf{+1. 7 AP} in 1\% and 10\% COCO low-data regimes object detection.

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