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Tao Han

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15 papers

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EAAI Journal 2026 Journal Article

A new mineral quantification method via experiment-enhanced transfer learning of linear mixed mid-infrared spectra data

Tao Han
Tengfei Yu
Peng Lin
Wen Ma
Zhenhao Xu

Accurate quantitative mineral analysis is fundamental for understanding geological evolution, predicting rock mechanical behavior, and delineating exploration targets. Mid-infrared spectroscopy offers rapid, non-destructive acquisition of mineralogical information. However, the application of mid-infrared spectroscopy in mineral quantification is still hindered by strong spectral nonlinearity caused by intimate mineral mixtures and the challenges of spectral testing in complex geological conditions. To address these limitations, we proposed a new mineral quantification method via experiment-enhanced transfer learning of linear mixed mid-infrared spectra data (MQM-ExpTL). The proposed method integrates submodel internal transfer and comprehensive fine-tuning strategies to construct a deep learning model capable of robustly quantifying protolith and clay minerals while capturing and interpreting non-linear mixing effects. On the independent test set, our method significantly outperformed support vector regression (SVR), partial least squares regression (PLSR), competitive adaptive reweighted sampling-support vector regression (CARS-SVR), competitive adaptive reweighted sampling-partial least squares regression (CARS-PLSR), the standard transfer learning model, and the model without transfer learning across coefficient of determination (R2), mean squared error (MSE), and mean absolute error (MAE). Specifically, our method achieves an overall prediction accuracy of R2 = 0. 98, yielding an improvement of 0. 9% to 32% over the comparative methods. Furthermore, we elucidated the rationale for adopting the validation loss for hyperparameter optimization and examined the differences in model selection when employing various globally optimal model selection criteria. The method provides an efficient quantitative approach to support the application of mid-infrared spectroscopy in remote sensing interpretation, geological identification, and mineral exploration.

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AAAI Conference 2026 Conference Paper

Predicting the Future by Retrieving the Past

Dazhao Du
Tao Han
Song Guo

Deep learning models such as MLP, Transformer, and TCN have achieved remarkable success in univariate time series forecasting, typically relying on sliding window samples from historical data for training. However, while these models implicitly compress historical information into their parameters during training, they are unable to explicitly and dynamically access this global knowledge during inference, relying only on the local context within the lookback window. This results in an underutilization of rich patterns from the global history. To bridge this gap, we propose Predicting the Future by Retrieving the Past (PFRP), a novel approach that explicitly integrates global historical data to enhance forecasting accuracy. Specifically, we construct a Global Memory Bank (GMB) to effectively store and manage global historical patterns. A retrieval mechanism is then employed to extract similar patterns from the GMB, enabling the generation of global predictions. By adaptively combining these global predictions with the outputs of any local prediction model, PFRP produces more accurate and interpretable forecasts. Extensive experiments conducted on seven real-world datasets demonstrate that PFRP enhances the average performance of advanced univariate forecasting models by 8.4%.

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NeurIPS Conference 2025 Conference Paper

Mesh-RFT: Enhancing Mesh Generation via Fine-grained Reinforcement Fine-Tuning

Jian Liu
Jing Xu
Song Guo
Jing Li
jingfeng Guo
Jiaao Yu
Haohan Weng
Biwen Lei

Existing pretrained models for 3D mesh generation often suffer from data biases and produce low-quality results, while global reinforcement learning (RL) methods rely on object-level rewards that struggle to capture local structure details. To address these challenges, we present $\textbf{Mesh-RFT}$, a novel fine-grained reinforcement fine-tuning framework that employs Masked Direct Preference Optimization (M-DPO) to enable localized refinement via quality-aware face masking. To facilitate efficient quality evaluation, we introduce an objective topology-aware scoring system to evaluate geometric integrity and topological regularity at both object and face levels through two metrics: Boundary Edge Ratio (BER) and Topology Score (TS). By integrating these metrics into a fine-grained RL strategy, Mesh-RFT becomes the first method to optimize mesh quality at the granularity of individual faces, resolving localized errors while preserving global coherence. Experiment results show that our M-DPO approach reduces Hausdorff Distance (HD) by 24. 6\% and improves Topology Score (TS) by 3. 8\% over pre-trained models, while outperforming global DPO methods with a 17. 4\% HD reduction and 4. 9\% TS gain. These results demonstrate Mesh-RFT’s ability to improve geometric integrity and topological regularity, achieving new state-of-the-art performance in production-ready mesh generation.

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AAAI Conference 2025 Conference Paper

Mixture of Experts Based Multi-Task Supervise Learning from Crowds

Tao Han
Huaixuan Shi
Xinyi Ding
Xi-Ao Ma
Huamao Gu
Yili Fang

Existing learning-from-crowds methods aim to design proper aggregation strategies to infer the unknown true labels from noisy labels provided by crowdsourcing. They treat the ground truth as hidden variables and use statistical or deep learning based worker behavior models to infer the ground truth. However, worker behavior models that rely on ground truth hidden variables overlook workers' behavior at the item feature level, leading to imprecise characterizations and negatively impacting the quality of learning-from-crowds. This paper proposes a new paradigm of multi-task supervised learning-from-crowds, which eliminates the need for modeling of items's ground truth in worker behavior models. Within this paradigm, we propose a worker behavior model at the item feature level called Mixture of Experts based Multi-task Supervised Learning-from-Crowds (MMLC), then, two aggregation strategies are proposed within MMLC. The first strategy, named MMLC-owf, utilizes clustering methods in the worker spectral space to identify the projection vector of the oracle worker. Subsequently, the labels generated based on this vector are regarded as the items's ground truth The second strategy, called MMLC-df, employs the MMLC model to fill the crowdsourced data, which can enhance the effectiveness of existing aggregation strategies. Experimental results demonstrate that MMLC-owf outperforms state-of-the-art methods and MMLC-df enhances the quality of existing learning-from-crowds methods.

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AAAI Conference 2025 Conference Paper

SRDC: Semantics-based Ransomware Detection and Classification with LLM-assisted Pre-training

Ce Zhou
Yilun Liu
Weibin Meng
Shimin Tao
Weinan Tian
Feiyu Yao
Xiaochun Li
Tao Han

In recent years, ransomware has emerged as a formidable data security threat, causing significant data privacy breaches that inflict substantial financial, reputational, and operational damages on society. Many studies employ dynamic feature analysis for ransomware detection. However, these methods utilize neither the internal semantic information (semantic information inherent in the features), nor external semantics (the wealth of existing knowledge and expert experience with regard to ransomware detection). Moreover, conventional methods rely on training data from known ransomware families, while zero-day ransomware often has unknown data distribution patterns, posing detection challenges. In this paper, we propose a Semantics-based Ransomware Detection and family Classification (SRDC) framework that can utilize both internal and external semantics of software. To bolster semantic analysis in zero-day attacks, we also design a procedure called LLM-assisted task-adaptive pre-training (LATAP). In LATAP, ransomware semantics from human experts and LLMs are employed to pre-train the detection model (GPT-2). By fully utilizing semantics, the proposed SRDC framework outperforms the SOTA methods by 12.15% for ransomware family classification tasks, and by 4.03% for zero-day ransomware detection tasks. SRDC also exhibits excellent data efficiency, requiring only two ransom families for training, which is only 35% of the data required by existing methods, to achieve a 90%+ accuracy of zero-day ransomware detection in nine unseen ransom families.

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AAAI Conference 2025 Conference Paper

VQLTI: Long-Term Tropical Cyclone Intensity Forecasting with Physical Constraints

Xinyu Wang
Lei Liu
Kang Chen
Tao Han
Bin Li
Lei Bai

Tropical cyclone (TC) intensity forecasting is crucial for early disaster warning and emergency decision-making. Numerous researchers have explored deep-learning methods to address computational and post-processing issues in operational forecasting. Regrettably, they exhibit subpar long-term forecasting capabilities. We use two strategies to enhance long-term forecasting. (1) By enhancing the matching between TC intensity and spatial information, we can improve long-term forecasting performance. (2) Incorporating physical knowledge and physical constraints can help mitigate the accumulation of forecasting errors. To achieve the above strategies, we propose the VQLTI framework. VQLTI transfers the TC intensity information to a discrete latent space while retaining the spatial information differences, using large-scale spatial meteorological data as conditions. Furthermore, we leverage the forecast from the weather prediction model FengWu to provide additional physical knowledge for VQLTI. Additionally, we calculate the potential intensity (PI) to impose physical constraints on the latent variables. In the global long-term TC intensity forecasting, VQLTI achieves state-of-the-art results for the 24h to 120h, with the MSW (Maximum Sustained Wind) forecast error reduced by 35.65%-42.51% compared to ECMWF-IFS.

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JBHI Journal 2024 Journal Article

Cross-Anatomy Transfer Learning via Shape-Aware Adaptive Fine-Tuning for 3D Vessel Segmentation

Tao Han
Danni Ai
Jingfan Fan
Hong Song
Deqiang Xiao
Yining Wang
Jian Yang

Deep learning methods have recently achieved remarkable performance in vessel segmentation applications, yet require numerous labor-intensive labeled data. To alleviate the requirement of manual annotation, transfer learning methods can potentially be used to acquire the related knowledge of tubular structures from public large-scale labeled vessel datasets for target vessel segmentation in other anatomic sites of the human body. However, the cross-anatomy domain shift is a challenging task due to the formidable discrepancy among various vessel structures in different anatomies, resulting in the limited performance of transfer learning. Therefore, we propose a cross-anatomy transfer learning framework for 3D vessel segmentation, which first generates a pre-trained model on a public hepatic vessel dataset and then adaptively fine-tunes our target segmentation network initialized from the model for segmentation of other anatomic vessels. In the framework, the adaptive fine-tuning strategy is presented to dynamically decide on the frozen or fine-tuned filters of the target network for each input sample with a proxy network. Moreover, we develop a Gaussian-based signed distance map that explicitly encodes vessel-specific shape context. The prediction of the map is added as an auxiliary task in the segmentation network to capture geometry-aware knowledge in the fine-tuning. We demonstrate the effectiveness of our method through extensive experiments on two small-scale datasets of coronary artery and brain vessel. The results indicate the proposed method effectively overcomes the discrepancy of cross-anatomy domain shift to achieve accurate vessel segmentation for these two datasets.

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NeurIPS Conference 2024 Conference Paper

FNP: Fourier Neural Processes for Arbitrary-Resolution Data Assimilation

Kun Chen
Peng Ye
Hao Chen
Kang Chen
Tao Han
Wanli Ouyang
Tao Chen
Lei Bai

Data assimilation is a vital component in modern global medium-range weather forecasting systems to obtain the best estimation of the atmospheric state by combining the short-term forecast and observations. Recently, AI-based data assimilation approaches have attracted increasing attention for their significant advantages over traditional techniques in terms of computational consumption. However, existing AI-based data assimilation methods can only handle observations with a specific resolution, lacking the compatibility and generalization ability to assimilate observations with other resolutions. Considering that complex real-world observations often have different resolutions, we propose the Fourier Neural Processes (FNP) for arbitrary-resolution data assimilation in this paper. Leveraging the efficiency of the designed modules and flexible structure of neural processes, FNP achieves state-of-the-art results in assimilating observations with varying resolutions, and also exhibits increasing advantages over the counterparts as the resolution and the amount of observations increase. Moreover, our FNP trained on a fixed resolution can directly handle the assimilation of observations with out-of-distribution resolutions and the observational information reconstruction task without additional fine-tuning, demonstrating its excellent generalization ability across data resolutions as well as across tasks. Code is available at https: //github. com/OpenEarthLab/FNP.

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NeurIPS Conference 2024 Conference Paper

Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling

Wanghan Xu
Fenghua Ling
Wenlong Zhang
Tao Han
Hao Chen
Wanli Ouyang
Lei Bai

Data-driven artificial intelligence (AI) models have made significant advancements in weather forecasting, particularly in medium-range and nowcasting. However, most data-driven weather forecasting models are black-box systems that focus on learning data mapping rather than fine-grained physical evolution in the time dimension. Consequently, the limitations in the temporal scale of datasets prevent these models from forecasting at finer time scales. This paper proposes a physics-AI hybrid model (i. e. , WeatherGFT) which generalizes weather forecasts to finer-grained temporal scales beyond training dataset. Specifically, we employ a carefully designed PDE kernel to simulate physical evolution on a small time scale (e. g. , 300 seconds) and use a parallel neural networks with a learnable router for bias correction. Furthermore, we introduce a lead time-aware training framework to promote the generalization of the model at different lead times. The weight analysis of physics-AI modules indicates that physics conducts major evolution while AI performs corrections adaptively. Extensive experiments show that WeatherGFT trained on an hourly dataset, effectively generalizes forecasts across multiple time scales, including 30-minute, which is even smaller than the dataset's temporal resolution.

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IJCAI Conference 2023 Conference Paper

TDG4Crowd: Test Data Generation for Evaluation of Aggregation Algorithms in Crowdsourcing

Yili Fang
Chaojie Shen
Huamao Gu
Tao Han
Xinyi Ding

In crowdsourcing, existing efforts mainly use real datasets collected from crowdsourcing as test datasets to evaluate the effectiveness of aggregation algorithms. However, these work ignore the fact that the datasets obtained by crowdsourcing are usually sparse and imbalanced due to limited budget. As a result, applying the same aggregation algorithm on different datasets often show contradicting conclusions. For example, on the RTE dataset, Dawid and Skene model performs significantly better than Majority Voting, while on the LableMe dataset, the experiments give the opposite conclusion. It is challenging to obtain comprehensive and balanced datasets at a low cost. To our best knowledge, little effort have been made to the fair evaluation of aggregation algorithms. To fill in this gap, we propose a novel method named TDG4Crowd that can automatically generate comprehensive and balanced datasets. Using Kullback Leibler divergence and Kolmogorov–Smirnov test, the experiment results show the superior of our method compared with others. Aggregation algorithms also perform more consistently on the synthetic datasets generated using our method.

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AAAI Conference 2021 Conference Paper

Explanation Consistency Training: Facilitating Consistency-Based Semi-Supervised Learning with Interpretability

Tao Han
Wei-Wei Tu
Yu-Feng Li

Unlabeled data exploitation and interpretability are usually both required in reality. They, however, are conducted independently, and very few works try to connect the two. For unlabeled data exploitation, state-of-the-art semi-supervised learning (SSL) results have been achieved via encouraging the consistency of model output on data perturbation, that is, consistency assumption. However, it remains hard for users to understand how particular decisions are made by state-ofthe-art SSL models. To this end, in this paper we first disclose that the consistency assumption is closely related to causality invariance, where causality invariance lies in the main reason why the consistency assumption is valid. We then propose ECT (Explanation Consistency Training) which encourages a consistent reason of model decision under data perturbation. ECT employs model explanation as a surrogate of the causality of model output, which is able to bridge state-of-the-art interpretability to SSL models and alleviate the high complexity of causality. We realize ECT-SM for vision and ECT- ATT for NLP tasks. Experimental results on real-world data sets validate the highly competitive performance and better explanation of the proposed algorithms.

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AIIM Journal 2020 Journal Article

An effective approach for CT lung segmentation using mask region-based convolutional neural networks

Qinhua Hu
Luís Fabrício de F. Souza
Gabriel Bandeira Holanda
Shara S.A. Alves
Francisco Hércules dos S. Silva
Tao Han
Pedro P. Rebouças Filho

Computer vision systems have numerous tools to assist in various medical fields, notably in image diagnosis. Computed tomography (CT) is the principal imaging method used to assist in the diagnosis of diseases such as bone fractures, lung cancer, heart disease, and emphysema, among others. Lung cancer is one of the four main causes of death in the world. The lung regions in the CT images are marked manually by a specialist as this initial step is a significant challenge for computer vision techniques. Once defined, the lung regions are segmented for clinical diagnoses. This work proposes an automatic segmentation of the lungs in CT images, using the Convolutional Neural Network (CNN) Mask R-CNN, to specialize the model for lung region mapping, combined with supervised and unsupervised machine learning methods (Bayes, Support Vectors Machine (SVM), K-means and Gaussian Mixture Models (GMMs)). Our approach using Mask R-CNN with the K-means kernel produced the best results for lung segmentation reaching an accuracy of 97. 68 ± 3. 42% and an average runtime of 11. 2 s. We compared our results against other works for validation purposes, and our approach had the highest accuracy and was faster than some state-of-the-art methods.

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IJCAI Conference 2020 Conference Paper

Structured Probabilistic End-to-End Learning from Crowds

Zhijun Chen
Huimin Wang
Hailong Sun
Pengpeng Chen
Tao Han
Xudong Liu
Jie Yang

End-to-end learning from crowds has recently been introduced as an EM-free approach to training deep neural networks directly from noisy crowdsourced annotations. It models the relationship between true labels and annotations with a specific type of neural layer, termed as the crowd layer, which can be trained using pure backpropagation. Parameters of the crowd layer, however, can hardly be interpreted as annotator reliability, as compared with the more principled probabilistic approach. The lack of probabilistic interpretation further prevents extensions of the approach to account for important factors of annotation processes, e. g. , instance difficulty. This paper presents SpeeLFC, a structured probabilistic model that incorporates the constraints of probability axioms for parameters of the crowd layer, which allows to explicitly model annotator reliability while benefiting from the end-to-end training of neural networks. Moreover, we propose SpeeLFC-D, which further takes into account instance difficulty. Extensive validation on real-world datasets shows that our methods improve the state-of-the-art.

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NeurIPS Conference 2020 Conference Paper

Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning

Tao Han
Junyu Gao
Yuan Yuan
Qi Wang

Unlabeled data learning has attracted considerable attention recently. However, it is still elusive to extract the expected high-level semantic feature with mere unsupervised learning. In the meantime, semi-supervised learning (SSL) demonstrates a promising future in leveraging few samples. In this paper, we combine both to propose an Unsupervised Semantic Aggregation and Deformable Template Matching (USADTM) framework for SSL, which strives to improve the classification performance with few labeled data and then reduce the cost in data annotating. Specifically, unsupervised semantic aggregation based on Triplet Mutual Information (T-MI) loss is explored to generate semantic labels for unlabeled data. Then the semantic labels are aligned to the actual class by the supervision of labeled data. Furthermore, a feature pool that stores the labeled samples is dynamically updated to assign proxy labels for unlabeled data, which are used as targets for cross-entropy minimization. Extensive experiments and analysis across four standard semi-supervised learning benchmarks validate that USADTM achieves top performance (e. g. , 90. 46% accuracy on CIFAR-10 with 40 labels and 95. 20% accuracy with 250 labels). The code is released at https: //github. com/taohan10200/USADTM.

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IJCAI Conference 2016 Conference Paper

Incorporating External Knowledge into Crowd Intelligence for More Specific Knowledge Acquisition

Tao Han
Hailong Sun
Yangqiu Song
Yili Fang
Xudong Liu

Crowdsourcing has been a helpful mechanism to leverage human intelligence to acquire useful knowledge for well defined tasks. However, when aggregating the crowd knowledge based on the currently developed voting algorithms, it often results in common knowledge that may not be expected. In this paper, we consider the problem of collecting as specific as possible knowledge via crowdsourcing. With the help of using external knowledge base such as WordNet, we incorporate the semantic relations between the alternative answers into a probabilistic model to determine which answer is more specific. We formulate the probabilistic model considering both worker's ability and task's difficulty, and solve it by expectation-maximization (EM) algorithm. Experimental results show that our approach achieved 35. 88% improvement over majority voting when more specific answers are expected.

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