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Mingxuan Xia

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5 papers

2 author rows

AAAI Conference 2025 Conference Paper

Multi-Instance Multi-Label Classification from Crowdsourced Labels

Ziquan Wang
Mingxuan Xia
Xiangyu Ren
Jiaqing Zhou
Gengyu Lyu
Tianlei Hu
Haobo Wang

Multi-instance multi-label classification (MIML) is a fundamental task in machine learning, where each data sample comprises a bag containing several instances and multiple binary labels. Despite its wide applications, the data collection process involves matching multiple instances and labels, typically resulting in high annotation costs. In this paper, we study a novel yet practical crowdsourced multi-instance multi-label classification (CMIML) setup, where labels are collected from multiple crowd sources. To address this problem, we first propose a novel data generation process for CMIML, i.e., cross-label transition, where cross-label annotation error is more likely to appear rather than previous single-label transition assumption, due to the inherent similarity of localized instances from different classes. Then, we formally define the cross-label transition by cross-label transition matrices which are dependent across classes. Subsequently, we establish the first unbiased risk estimator for CMIML and further improve it through aggregation techniques, along with a rigorous generalization error bound. We also provide a practical implementation of cross-label transition matrix estimation. Comprehensive experiments on six benchmark datasets under various scenarios demonstrate that our algorithm outperforms the baselines by a large margin, validating its effectiveness in handling the CMIML problem.

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IJCAI Conference 2025 Conference Paper

Towards Robust Incremental Learning Under Ambiguous Supervision

Rui Wang
Mingxuan Xia
Haobo Wang
Lei Feng
Junbo Zhao
Gang Chen
Chang Yao

Traditional Incremental Learning (IL) targets to handle sequential fully-supervised learning problems where novel classes emerge from time to time. However, due to inherent annotation uncertainty and ambiguity, collecting high-quality annotated data in a dynamic learning system can be extremely expensive. To mitigate this problem, we propose a novel weakly-supervised learning paradigm called Incremental Partial Label Learning (IPLL), where the sequentially arrived data relate to a set of candidate labels rather than the ground truth. Technically, we develop the Prototype-Guided Disambiguation and Replay Algorithm (PGDR) which leverages the class prototypes as a proxy to mitigate two intertwined challenges in IPLL, i. e. , label ambiguity and catastrophic forgetting. To handle the former, PGDR encapsulates a momentum-based pseudo-labeling algorithm along with prototype-guided initialization, resulting in a balanced perception of classes. To alleviate forgetting, we develop a memory replay technique that collects well-disambiguated samples while maintaining representativeness and diversity. By jointly distilling knowledge from curated memory data, our framework exhibits a great disambiguation ability for samples of new tasks and achieves less forgetting of knowledge. Extensive experiments demonstrate that PGDR achieves superior performance over the baselines in the IPLL task.

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AAAI Conference 2024 Conference Paper

A Separation and Alignment Framework for Black-Box Domain Adaptation

Mingxuan Xia
Junbo Zhao
Gengyu Lyu
Zenan Huang
Tianlei Hu
Gang Chen
Haobo Wang

Black-box domain adaptation (BDA) targets to learn a classifier on an unsupervised target domain while assuming only access to black-box predictors trained from unseen source data. Although a few BDA approaches have demonstrated promise by manipulating the transferred labels, they largely overlook the rich underlying structure in the target domain. To address this problem, we introduce a novel separation and alignment framework for BDA. Firstly, we locate those well-adapted samples via loss ranking and a flexible confidence-thresholding procedure. Then, we introduce a novel graph contrastive learning objective that aligns under-adapted samples to their local neighbors and well-adapted samples. Lastly, the adaptation is finally achieved by a nearest-centroid-augmented objective that exploits the clustering effect in the feature space. Extensive experiments demonstrate that our proposed method outperforms best baselines on benchmark datasets, e.g. improving the averaged per-class accuracy by 4.1% on the VisDA dataset. The source code is available at: https://github.com/MingxuanXia/SEAL.

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ICML Conference 2024 Conference Paper

Unbiased Multi-Label Learning from Crowdsourced Annotations

Mingxuan Xia
Zenan Huang
Runze Wu 0001
Gengyu Lyu
Junbo Zhao 0002
Gang Chen 0001
Haobo Wang 0001

This work studies the novel Crowdsourced Multi-Label Learning (CMLL) problem, where each instance is related to multiple true labels but the model only receives unreliable labels from different annotators. Although a few Crowdsourced Multi-Label Inference (CMLI) methods have been developed, they require both the training and testing sets to be assigned crowdsourced labels and focus on true label inferring rather than prediction, making them less practical. In this paper, by excavating the generation process of crowdsourced labels, we establish the first unbiased risk estimator for CMLL based on the crowdsourced transition matrices. To facilitate transition matrix estimation, we upgrade our unbiased risk estimator by aggregating crowdsourced labels and transition matrices from all annotators while guaranteeing its theoretical characteristics. Integrating with the unbiased risk estimator, we further propose a decoupled autoencoder framework to exploit label correlations and boost performance. We also provide a generalization error bound to ensure the convergence of the empirical risk estimator. Experiments on various CMLL scenarios demonstrate the effectiveness of our proposed method. The source code is available at https: //github. com/MingxuanXia/CLEAR.

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NeurIPS Conference 2022 Conference Paper

SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning

Haobo Wang
Mingxuan Xia
Yixuan Li
Yuren Mao
Lei Feng
Gang Chen
Junbo Zhao

Partial-label learning (PLL) is a peculiar weakly-supervised learning task where the training samples are generally associated with a set of candidate labels instead of single ground truth. While a variety of label disambiguation methods have been proposed in this domain, they normally assume a class-balanced scenario that may not hold in many real-world applications. Empirically, we observe degenerated performance of the prior methods when facing the combinatorial challenge from the long-tailed distribution and partial-labeling. In this work, we first identify the major reasons that the prior work failed. We subsequently propose SoLar, a novel Optimal Transport-based framework that allows to refine the disambiguated labels towards matching the marginal class prior distribution. SoLar additionally incorporates a new and systematic mechanism for estimating the long-tailed class prior distribution under the PLL setup. Through extensive experiments, SoLar exhibits substantially superior results on standardized benchmarks compared to the previous state-of-the-art PLL methods. Code and data are available at: https: //github. com/hbzju/SoLar.

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