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Zetian Jiang

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6 papers

2 author rows

ICLR Conference 2025 Conference Paper

Learning Structured Universe Graph with Outlier OOD Detection for Partial Matching

Zetian Jiang
Jiaxin Lu 0001
Haizhao Fan
Tianzhe Wang
Junchi Yan

Partial matching is a kind of graph matching where only part of two graphs can be aligned. This problem is particularly important in computer vision applications, where challenges like point occlusion or annotation errors often occur when labeling key points. Previous work has often conflated point occlusion and annotation errors, despite their distinct underlying causes. We propose two components to address these challenges: (1) a structured universe graph is learned to connect two input graphs $X_{ij} = X_{iu} X_{ju}^\top$, effectively resolving the issue of point occlusion; (2) an energy-based out-of-distribution detection is designed to remove annotation errors from the input graphs before matching. We evaluated our method on the Pascal VOC and Willow Object datasets, focusing on scenarios involving point occlusion and random outliers. The experimental results demonstrate that our approach consistently outperforms state-of-the-art methods across all tested scenarios, highlighting the accuracy and robustness of our method.

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ICML Conference 2024 Conference Paper

Graph Out-of-Distribution Detection Goes Neighborhood Shaping

Tianyi Bao
Qitian Wu
Zetian Jiang
Yiting Chen 0003
Jiawei Sun 0001
Junchi Yan

Despite the rich line of research works on out-of-distribution (OOD) detection on images, the literature on OOD detection for interdependent data, e. g. , graphs, is still relatively limited. To fill this gap, we introduce TopoOOD as a principled approach that accommodates graph topology and neighborhood context for detecting OOD node instances on graphs. Meanwhile, we enrich the experiment settings by splitting in-distribution (ID) and OOD data based on distinct topological distributions, which presents new benchmarks for a more comprehensive analysis of graph-based OOD detection. The latter is designed to thoroughly assess the performance of these discriminators under distribution shifts involving structural information, providing a rigorous evaluation of methods in the emerging area of OOD detection on graphs. Our experimental results show the competitiveness of the proposed model across multiple datasets, as evidenced by up to a 15% increase in the AUROC and a 50% decrease in the FPR compared to existing state-of-the-art methods.

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ICLR Conference 2024 Conference Paper

M3C: A Framework towards Convergent, Flexible, and Unsupervised Learning of Mixture Graph Matching and Clustering

Jiaxin Lu 0001
Zetian Jiang
Tianzhe Wang
Junchi Yan

Existing graph matching methods typically assume that there are similar structures between graphs and they are matchable. This work addresses a more realistic scenario where graphs exhibit diverse modes, requiring graph grouping before or along with matching, a task termed mixture graph matching and clustering. Specifically, we introduce Minorize-Maximization Matching and Clustering (M3C), a learning-free algorithm that guarantees theoretical convergence through the Minorize-Maximization framework and offers enhanced flexibility via relaxed clustering. Building on M3C, we further develop UM3C, an unsupervised model that incorporates novel edge-wise affinity learning and pseudo label selection. Extensive experimental results on public benchmarks demonstrate that our method outperforms state-of-the-art graph matching and mixture graph matching and clustering approaches in both accuracy and efficiency.

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JMLR Journal 2024 Journal Article

Pygmtools: A Python Graph Matching Toolkit

Runzhong Wang
Ziao Guo
Wenzheng Pan
Jiale Ma
Yikai Zhang
Nan Yang
Qi Liu
Longxuan Wei

Graph matching aims to find node-to-node matching among multiple graphs, which is a fundamental yet challenging problem. To facilitate graph matching in scientific research and industrial applications, pygmtools is released, which is a Python graph matching toolkit that implements a comprehensive collection of two-graph matching and multi-graph matching solvers, covering both learning-free solvers as well as learning-based neural graph matching solvers. Our implementation supports numerical backends including Numpy, PyTorch, Jittor, Paddle, runs on Windows, MacOS and Linux, and is friendly to install and configure. Comprehensive documentations covering beginner's guide, API reference and examples are available online. pygmtools is open-sourced under Mulan PSL v2 license. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2024. ( edit, beta )

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ICLR Conference 2023 Conference Paper

Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching

Chang Liu 0021
Zetian Jiang
Runzhong Wang
Lingxiao Huang
Pinyan Lu
Junchi Yan

Graph matching (GM) has been a building block in various areas including computer vision and pattern recognition. Despite recent impressive progress, existing deep GM methods often have obvious difficulty in handling outliers, which are ubiquitous in practice. We propose a deep reinforcement learning based approach RGM, whose sequential node matching scheme naturally fits the strategy for selective inlier matching against outliers. A revocable action framework is devised to improve the agent's flexibility against the complex constrained GM. Moreover, we propose a quadratic approximation technique to regularize the affinity score, in the presence of outliers. As such, the agent can finish inlier matching timely when the affinity score stops growing, for which otherwise an additional parameter i.e. the number of inliers is needed to avoid matching outliers. In this paper, we focus on learning the back-end solver under the most general form of GM: the Lawler's QAP, whose input is the affinity matrix. Especially, our approach can also boost existing GM methods that use such input. Experiments on multiple real-world datasets demonstrate its performance regarding both accuracy and robustness.

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AAAI Conference 2020 Conference Paper

Multiple Graph Matching and Clustering via Decayed Pairwise Matching Composition

Tianzhe Wang
Zetian Jiang
Junchi Yan

Jointly matching of multiple graphs is challenging and recently has been an active topic in machine learning and computer vision. State-of-the-art methods have been devised, however, to our best knowledge there is no effective mechanism that can explicitly deal with the matching of a mixture of graphs belonging to multiple clusters, e. g. , a collection of bikes and bottles. Seeing its practical importance, we propose a novel approach for multiple graph matching and clustering. Firstly, for the traditional multi-graph matching setting, we devise a composition scheme based on a tree structure, which can be seen as in the between of two strong multigraph matching solvers, i. e. , MatchOpt (Yan et al. 2015a) and CAO (Yan et al. 2016a). In particular, it can be more robust than MatchOpt against a set of diverse graphs and more ef- ﬁcient than CAO. Then we further extend the algorithm to the multiple graph matching and clustering setting, by adopting a decaying technique along the composition path, to discount the meaningless matching between graphs in different clusters. Experimental results show the proposed methods achieve excellent trade-off on the traditional multi-graph matching case, and outperform in both matching and clustering accuracy, as well as time efﬁciency.

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