Author name cluster

Xiaomin Song

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

10 papers

2 author rows

AAAI Conference 2026 Conference Paper

Robust Semi-paired Multimodal Learning for Cross-modal Retrieval

Yang Qin
Yuan Sun
Xi Peng
Dezhong Peng
Joey Tianyi Zhou
Xiaomin Song
Peng Hu

Cross-modal retrieval is a fundamental application of multi-modal learning that has achieved remarkable success with large-scale well-paired data. However, in practice, it is costly to collect large-scale well-paired data. To alleviate the dependence on the amount of paired data, in this paper, we study a practical learning paradigm: semi-paired cross-modal learning (SPL), which utilizes both a small amount of paired data and a large amount of unpaired data to enhance cross-modal learning directly and is more accessible in practice. To achieve this, we take image-text retrieval as an example and propose a novel Robust Cross-modal Semi-paired Learning method (RCSL) by addressing two challenges. To be specific, i) to overcome the under-optimization issue caused by too little paired data, we present Semi-paired Discriminative Learning (SDL) to fully learn visual-semantic associations from a small amount of image-text pairs by preserving the alignment and uniformity of modality representations. ii) To mine visual-semantic correspondences from unpaired data, RCSL first constructs pseudo-paired correlations across different modalities by nearest neighbor association. However, this may introduce noisy correspondences (NCs) due to inaccurate pseudo signals, which could degrade the model's performance. To tackle NCs, we devise Robust Cross-correlation Mining (RCM) based on the risk minimization criterion to robustly and explicitly learn visual-semantic associations from pseudo-paired data, thus boosting cross-modal learning. Finally, we conduct extensive experiments on four datasets, i.e., three widely used benchmark datasets of Flickr30K, MS-COCO, CC152K, and a newly constructed real-world dataset Drone-SP, to demonstrate the effectiveness of RCSL under semi-paired and noisy settings.

PDF Details DOI

NeurIPS Conference 2025 Conference Paper

Robust Cross-modal Alignment Learning for Cross-Scene Spatial Reasoning and Grounding

Yanglin Feng
Hongyuan Zhu
Dezhong Peng
Xi Peng
Xiaomin Song
Peng Hu

Grounding target objects in 3D environments via natural language is a fundamental capability for autonomous agents to successfully fulfill user requests. Almost all existing works typically assume that the target object lies within a known scene and focus solely on in-scene localization. In practice, however, agents often encounter unknown or previously visited environments and need to search across a large archive of scenes to ground the described object, thereby invalidating this assumption. To address this, we reveal a novel task called Cross-Scene Spatial Reasoning and Grounding (CSSRG), which aims to locate a described object anywhere across an entire collection of 3D scenes rather than predetermined scenes. Due to the difference from existing 3D visual grounding, CSSRG poses two challenges: the prohibitive cost of exhaustively traversing all scenes and more complex cross-modal spatial alignment. To address the challenges, we propose a Cross-Scene 3D Object Reasoning Framework (CoRe), which adopts a matching-then-grounding pipeline to reduce computational overhead. Specifically, CoRe consists of i) a Robust Text-Scene Aligning (RTSA) module that learns global scene representations for robust alignment between object descriptions and the corresponding 3D scenes, enabling efficient retrieval of candidate scenes; and ii) a Tailored Word-Object Associating (TWOA) module that establishes fine-grained alignment between words and target objects to filter out redundant context, supporting precise object-level reasoning and alignment. Additionally, to benchmark CSSRG, we construct a new CrossScene-RETR dataset and evaluation protocol tailored for cross-scene grounding. Extensive experiments across four multimodal datasets demonstrate that CoRe dramatically reduces computational overhead while showing superiority in both scene retrieval and object grounding. Code is available at https: //github. com/Yangl1nFeng/CoRe.

PDF Details

AAAI Conference 2025 Conference Paper

Robust Self-Paced Hashing for Cross-Modal Retrieval with Noisy Labels

Ruitao Pu
Yuan Sun
Yang Qin
Zhenwen Ren
Xiaomin Song
Huiming Zheng
Dezhong Peng

Cross-modal hashing (CMH) has appeared as a popular technique for cross-modal retrieval due to its low storage cost and high computational efficiency in large-scale data. Most existing methods implicitly assume that multi-modal data is correctly labeled, which is expensive and even unattainable due to the inevitable imperfect annotations (i.e., noisy labels) in real-world scenarios. Inspired by human cognitive learning, a few methods introduce self-paced learning to gradually train the model from easy to hard samples, which is often used to mitigate the effects of feature noise or outliers. It is a less-touched problem that how to utilize SPL to alleviate the misleading of noisy labels on the hash model. To tackle this problem, we propose a new cognitive cross-modal retrieval method called Robust Self-paced Hashing with Noisy Labels (RSHNL), which can mimic the human cognitive process to identify the noise while embracing robustness against noisy labels. Specifically, we first propose a contrastive hashing learning (CHL) scheme to improve multi-modal consistency, thereby reducing the inherent semantic gap. Afterward, we propose center aggregation learning (CAL) to mitigate the intra-class variations. Finally, we propose Noise-tolerance Self-paced Hashing (NSH) that dynamically estimates the learning difficulty for each instance and distinguishes noisy labels through the difficulty level. For all estimated clean pairs, we further adopt a self-paced regularizer to gradually learn hash codes from easy to hard. Extensive experiments demonstrate that the proposed RSHNL performs remarkably well over the state-of-the-art CMH methods.

PDF Details DOI

AAAI Conference 2025 Conference Paper

RoDA: Robust Domain Alignment for Cross-Domain Retrieval Against Label Noise

Ziniu Yin
Yanglin Feng
Ming Yan
Xiaomin Song
Dezhong Peng
Xu Wang

This paper studies the complex challenge of cross-domain image retrieval under the condition of noisy labels (NCIR), a scenario that not only includes the inherent obstacles of traditional cross-domain image retrieval (CIR) but also requires alleviating the adverse effects of label noise. To address this challenge, this paper introduces a novel Robust Domain Alignment framework (RoDA), specifically designed for the NCIR task. At the heart of RoDA is the Selective Division and Adaptive Learning mechanism (SDAL), a key component crafted to shield the model from overfitting the noisy labels. SDAL effectively learns discriminative knowledge by dividing the dataset into clean and noisy parts, subsequently rectifying the labels for the latter based on information drawn from the clean one. This process involves adaptively weighting the relabeled samples and leveraging both the clean and relabeled data to bootstrap model training. Moreover, to bridge the domain gap further, we introduce the Accumulative Class Center Alignment (ACCA), a novel approach that fosters domain alignment through an accumulative domain loss mechanism.Thanks to SDAL and ACCA, our RoDA demonstrates its superiority in overcoming label noise and domain discrepancies within the NCIR paradigm. The effectiveness and robustness of our RoDA framework are comprehensively validated through extensive experiments across three multi-domain benchmarks.

PDF Details DOI

ICML Conference 2025 Conference Paper

SHE: Streaming-media Hashing Retrieval

Ruitao Pu
Yang Qin
Xiaomin Song
Dezhong Peng
Zhenwen Ren
Yuan Sun 0016

Recently, numerous cross-modal hashing (CMH) methods have been proposed, yielding remarkable progress. As a static learning paradigm, existing CMH methods often implicitly assume that all modalities are prepared before processing. However, in practice applications (such as multi-modal medical diagnosis), it is very challenging to collect paired multi-modal data simultaneously. Specifically, they are collected chronologically, forming streaming-media data (SMA). To handle this, all previous CMH methods require retraining on data from all modalities, which inevitably limits the scalability and flexibility of the model. In this paper, we propose a novel CMH paradigm named Streaming-media Hashing rEtrieval (SHE) that enables parallel training of each modality. Specifically, we first propose a knowledge library mining module (KLM) that extracts a prototype knowledge library for each modality, thereby revealing the commonality distribution of the instances from each modality. Then, we propose a knowledge library transfer module (KLT) that updates and aligns the new knowledge by utilizing the historical knowledge library, ensuring semantic consistency. Finally, to enhance intra-class semantic relevance and inter-class semantic disparity, we develop a discriminative hashing learning module (DHL). Comprehensive experiments on four benchmark datasets demonstrate the superiority of our SHE compared to 14 competitors.

Details

YNICL Journal 2023 Journal Article

Morphometric similarity network alterations in COVID-19 survivors correlate with behavioral features and transcriptional signatures

Jia Long
Jiao Li
Bing Xie
Zhuomin Jiao
Guoqiang Shen
Wei Liao
Xiaomin Song
Hongbo Le

OBJECTIVES: To explore the differences in the cortical morphometric similarity network (MSN) between COVID-19 survivors and healthy controls, and the correlation between these differences and behavioralfeatures and transcriptional signatures. MATERIALS & METHODS: 39 COVID-19 survivors and 39 age-, sex- and education years-matched healthy controls (HCs) were included. All participants underwent MRI and behavioral assessments (PCL-17, GAD-7, PHQ-9). MSN analysis was used to compute COVID-19 survivors vs. HCs differences across brain regions. Correlation analysis was used to determine the associations between regional MSN differences and behavioral assessments, and determine the spatial similarities between regional MSN differences and risk genes transcriptional activity. RESULTS: COVID-19 survivors exhibited decreased regional MSN in insula, precuneus, transverse temporal, entorhinal, para-hippocampal, rostral middle frontal and supramarginal cortices, and increased regional MSN in pars triangularis, lateral orbitofrontal, superior frontal, superior parietal, postcentral, and inferior temporal cortices. Regional MSN value of lateral orbitofrontal cortex was positively associated with GAD-7 and PHQ-9 scores, and rostral middle frontal was negatively related to PHQ-9 scores. The analysis of spatial similarities showed that seven risk genes (MFGE8, MOB2, NUP62, PMPCA, SDSL, TMEM178B, and ZBTB11) were related to regional MSN values. CONCLUSION: The MSN differences were associated with behavioral and transcriptional signatures, early psychological counseling or intervention may be required to COVID-19 survivors. Our study provided a new insight into understanding the altered coordination of structure in COVID-19 and may offer a new endophenotype to further investigate the brain substrate.

Details DOI

IJCAI Conference 2021 Conference Paper

Time Series Data Augmentation for Deep Learning: A Survey

Qingsong Wen
Liang Sun
Fan Yang
Xiaomin Song
Jingkun Gao
Xue Wang
Huan Xu

Deep learning performs remarkably well on many time series analysis tasks recently. The superior performance of deep neural networks relies heavily on a large number of training data to avoid overfitting. However, the labeled data of many real-world time series applications may be limited such as classification in medical time series and anomaly detection in AIOps. As an effective way to enhance the size and quality of the training data, data augmentation is crucial to the successful application of deep learning models on time series data. In this paper, we systematically review different data augmentation methods for time series. We propose a taxonomy for the reviewed methods, and then provide a structured review for these methods by highlighting their strengths and limitations. We also empirically compare different data augmentation methods for different tasks including time series classification, anomaly detection, and forecasting. Finally, we discuss and highlight five future directions to provide useful research guidance.

PDF Details DOI

AAAI Conference 2019 Conference Paper

RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series

Qingsong Wen
Jingkun Gao
Xiaomin Song
Liang Sun
Huan Xu
Shenghuo Zhu

Decomposing complex time series into trend, seasonality, and remainder components is an important task to facilitate time series anomaly detection and forecasting. Although numerous methods have been proposed, there are still many time series characteristics exhibiting in real-world data which are not addressed properly, including 1) ability to handle seasonality fluctuation and shift, and abrupt change in trend and reminder; 2) robustness on data with anomalies; 3) applicability on time series with long seasonality period. In the paper, we propose a novel and generic time series decomposition algorithm to address these challenges. Specifically, we extract the trend component robustly by solving a regression problem using the least absolute deviations loss with sparse regularization. Based on the extracted trend, we apply the the non-local seasonal filtering to extract the seasonality component. This process is repeated until accurate decomposition is obtained. Experiments on different synthetic and real-world time series datasets demonstrate that our method outperforms existing solutions.

PDF Details

IJCAI Conference 2019 Conference Paper

RobustTrend: A Huber Loss with a Combined First and Second Order Difference Regularization for Time Series Trend Filtering

Qingsong Wen
Jingkun Gao
Xiaomin Song
Liang Sun
Jian Tan

Extracting the underlying trend signal is a crucial step to facilitate time series analysis like forecasting and anomaly detection. Besides noise signal, time series can contain not only outliers but also abrupt trend changes in real-world scenarios. To deal with these challenges, we propose a robust trend filtering algorithm based on robust statistics and sparse learning. Specifically, we adopt the Huber loss to suppress outliers, and utilize a combination of the first order and second order difference on the trend component as regularization to capture both slow and abrupt trend changes. Furthermore, an efficient method is designed to solve the proposed robust trend filtering based on majorization minimization (MM) and alternative direction method of multipliers (ADMM). We compared our proposed robust trend filter with other nine state-of-the-art trend filtering algorithms on both synthetic and real-world datasets. The experiments demonstrate that our algorithm outperforms existing methods.

PDF Details

AAAI Conference 2019 Conference Paper

Which Factorization Machine Modeling Is Better: A Theoretical Answer with Optimal Guarantee

Ming Lin
Shuang Qiu
Jieping Ye
Xiaomin Song
Qi Qian
Liang Sun
Shenghuo Zhu
Rong Jin

Factorization machine (FM) is a popular machine learning model to capture the second order feature interactions. The optimal learning guarantee of FM and its generalized version is not yet developed. For a rank k generalized FM of d dimensional input, the previous best known sampling complexity is O[k3 d · polylog(kd)] under Gaussian distribution. This bound is sub-optimal comparing to the information theoretical lower bound O(kd). In this work, we aim to tighten this bound towards optimal and generalize the analysis to sub-gaussian distribution. We prove that when the input data satisfies the so-called τ-Moment Invertible Property, the sampling complexity of generalized FM can be improved to O[k2 d · polylog(kd)/τ2 ]. When the second order self-interaction terms are excluded in the generalized FM, the bound can be improved to the optimal O[kd · polylog(kd)] up to the logarithmic factors. Our analysis also suggests that the positive semi-definite constraint in the conventional FM is redundant as it does not improve the sampling complexity while making the model difficult to optimize. We evaluate our improved FM model in real-time high precision GPS signal calibration task to validate its superiority.

PDF Details