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Tong Wei

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17

AAAI Conference 2026 Conference Paper

TSPE-GS: Probabilistic Depth Extraction for Semi-Transparent Surface Reconstruction via 3D Gaussian Splatting

  • Zhiyuan Xu
  • Min Nan
  • Yuhang Guo
  • Tong Wei

3D Gaussian Splatting-based geometry reconstruction is regarded as an excellent paradigm due to its favorable trade-off between speed and reconstruction quality. However, such 3D Gaussian-based reconstruction pipelines often face challenges when reconstructing semi-transparent surfaces, hindering their broader application in real-world scenes. The primary reason is the assumption in mainstream methods that each pixel corresponds to one specific depth—an assumption that fails under semi-transparent conditions where multiple surfaces are visible, leading to depth ambiguity and ineffective recovery of geometric structures. To address these challenges, we propose TSPE-GS (Transparent Surface Probabilistic Extraction for Gaussian Splatting), a novel probabilistic depth extraction approach that uniformly samples transmittance to model the multi-modal distribution of opacity and depth per pixel, replacing the previous single-peak distribution that caused depth confusion across surfaces. By progressively fusing truncated signed distance functions, TSPE-GS separately reconstructs distinct external and internal surfaces in a unified framework. Our method can be easily generalized to other Gaussian-based reconstruction pipelines, effectively extracting semi-transparent surfaces without requiring additional training overhead. Extensive experiments on both public and self-collected semi-transparent datasets, as well as opaque object datasets, demonstrate that TSPE-GS significantly enhances reconstruction accuracy for semi-transparent surfaces while maintaining reconstruction quality in opaque scenes.

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NeurIPS Conference 2025 Conference Paper

Adaptive Divergence Regularized Policy Optimization for Fine-tuning Generative Models

  • Jiajun Fan
  • Tong Wei
  • Chaoran Cheng
  • Yuxin Chen
  • Ge Liu

Balancing exploration and exploitation during reinforcement learning fine-tuning of generative models presents a critical challenge, as existing approaches rely on fixed divergence regularization that creates an inherent dilemma: strong regularization preserves model capabilities but limits reward optimization, while weak regularization enables greater alignment but risks instability or reward hacking. We introduce Adaptive Divergence Regularized Policy Optimization (ADRPO), which automatically adjusts regularization strength based on advantage estimates—reducing regularization for high-value samples while applying stronger regularization to poor samples, enabling policies to navigate between exploration and aggressive exploitation according to data quality. Our implementation with Wasserstein-2 regularization for flow matching generative models achieves remarkable results on text-to-image generation, achieving better semantic alignment and diversity than offline methods like DPO and online methods with fixed regularization like ORW-CFM-W2. ADRPO enables a 2B parameter SD3 model to surpass much larger models with 4. 8B and 12B parameters in attribute binding, semantic consistency, artistic style transfer, and compositional control while maintaining generation diversity. ADRPO generalizes to KL-regularized fine-tuning of both text-only LLMs and multi-modal reasoning models, enhancing existing online RL methods like GRPO while requiring no additional networks or complex architectural changes. In LLM fine-tuning, ADRPO demonstrates an emergent ability to escape local optima through active exploration, while in multi-modal audio reasoning, it outperforms GRPO through superior step-by-step reasoning, enabling a 7B model to outperform substantially larger commercial models including Gemini 2. 5 Pro and GPT-4o Audio, offering an effective plug-and-play solution to the exploration-exploitation challenge across diverse generative architectures and modalities.

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JMLR Journal 2025 Journal Article

Optimal and Efficient Algorithms for Decentralized Online Convex Optimization

  • Yuanyu Wan
  • Tong Wei
  • Bo Xue
  • Mingli Song
  • Lijun Zhang

We investigate decentralized online convex optimization (D-OCO), in which a set of local learners are required to minimize a sequence of global loss functions using only local computations and communications. Previous studies have established $O(n^{5/4}\rho^{-1/2}\sqrt{T})$ and ${O}(n^{3/2}\rho^{-1}\log T)$ regret bounds for convex and strongly convex functions respectively, where $n$ is the number of local learners, $\rho [abs] [ pdf ][ bib ] &copy JMLR 2025. ( edit, beta )

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NeurIPS Conference 2025 Conference Paper

X-Mahalanobis: Transformer Feature Mixing for Reliable OOD Detection

  • Tong Wei
  • Bolin Wang
  • Jiang-Xin Shi
  • Yu-Feng Li
  • Min-Ling Zhang

Recognizing out-of-distribution (OOD) samples is essential for deploying robust machine learning systems in open-world environments. While conventional OOD detection approaches rely on feature representations from the penultimate layer of neural networks, they often overlook informative signals embedded in intermediate layers. In this paper, we present a straightforward feature mixing approach for pre-trained Transformers, which combines multi-layer representations via calculated importance weights, and identifies OOD samples using Mahalanobis distance in the blended feature space. When in-distribution samples are accessible, we show that parameter-efficient fine-tuning strategies effectively balance classification accuracy and OOD detection performance. We conduct extensive empirical analyses to validate the superiority of our proposed method under zero-shot, and fine-tuning settings using both class-balanced and long-tailed datasets. The source code is available at https: //github. com/SEUML/X-Maha.

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IJCAI Conference 2024 Conference Paper

Bridging the Gap: Learning Pace Synchronization for Open-World Semi-Supervised Learning

  • Bo Ye
  • Kai Gan
  • Tong Wei
  • Min-Ling Zhang

In open-world semi-supervised learning, a machine learning model is tasked with uncovering novel categories from unlabeled data while maintaining performance on seen categories from labeled data. The central challenge is the substantial learning gap between seen and novel categories, as the model learns the former faster due to accurate supervisory information. Moreover, capturing the semantics of unlabeled novel category samples is also challenging due to the missing label information. To address the above issues, we introduce 1) the adaptive synchronizing marginal loss which imposes class-specific negative margins to alleviate the model bias towards seen classes, and 2) the pseudo-label contrastive clustering which exploits pseudo-labels predicted by the model to group unlabeled data from the same category together in the output space. Extensive experiments on benchmark datasets demonstrate that previous approaches may significantly hinder novel class learning, whereas our method strikingly balances the learning pace between seen and novel classes, achieving a remarkable 3% average accuracy increase on the ImageNet dataset. Importantly, we find that fine-tuning the self-supervised pre-trained model significantly boosts the performance, which is overlooked in prior literature. Our code is available at https: //github. com/yebo0216best/LPS-main.

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NeurIPS Conference 2024 Conference Paper

Continuous Contrastive Learning for Long-Tailed Semi-Supervised Recognition

  • Zi-Hao Zhou
  • Siyuan Fang
  • Zi-Jing Zhou
  • Tong Wei
  • Yuanyu Wan
  • Min-Ling Zhang

Long-tailed semi-supervised learning poses a significant challenge in training models with limited labeled data exhibiting a long-tailed label distribution. Current state-of-the-art LTSSL approaches heavily rely on high-quality pseudo-labels for large-scale unlabeled data. However, these methods often neglect the impact of representations learned by the neural network and struggle with real-world unlabeled data, which typically follows a different distribution than labeled data. This paper introduces a novel probabilistic framework that unifies various recent proposals in long-tail learning. Our framework derives the class-balanced contrastive loss through Gaussian kernel density estimation. We introduce a continuous contrastive learning method, CCL, extending our framework to unlabeled data using reliable and smoothed pseudo-labels. By progressively estimating the underlying label distribution and optimizing its alignment with model predictions, we tackle the diverse distribution of unlabeled data in real-world scenarios. Extensive experiments across multiple datasets with varying unlabeled data distributions demonstrate that CCL consistently outperforms prior state-of-the-art methods, achieving over 4% improvement on the ImageNet-127 dataset. The supplementary material includes the source code for reproducibility.

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NeurIPS Conference 2024 Conference Paper

Dual Critic Reinforcement Learning under Partial Observability

  • Jinqiu Li
  • Enmin Zhao
  • Tong Wei
  • Junliang Xing
  • Shiming Xiang

Partial observability in environments poses significant challenges that impede the formation of effective policies in reinforcement learning. Prior research has shown that borrowing the complete state information can enhance sample efficiency. This strategy, however, frequently encounters unstable learning with high variance in practical applications due to the over-reliance on complete information. This paper introduces DCRL, a Dual Critic Reinforcement Learning framework designed to adaptively harness full-state information during training to reduce variance for optimized online performance. In particular, DCRL incorporates two distinct critics: an oracle critic with access to complete state information and a standard critic functioning within the partially observable context. It innovates a synergistic strategy to meld the strengths of the oracle critic for efficiency improvement and the standard critic for variance reduction, featuring a novel mechanism for seamless transition and weighting between them. We theoretically prove that DCRL mitigates the learning variance while maintaining unbiasedness. Extensive experimental analyses across the Box2D and Box3D environments have verified DCRL's superior performance. The source code is available in the supplementary.

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AAAI Conference 2024 Conference Paper

EAT: Towards Long-Tailed Out-of-Distribution Detection

  • Tong Wei
  • Bo-Lin Wang
  • Min-Ling Zhang

Despite recent advancements in out-of-distribution (OOD) detection, most current studies assume a class-balanced in-distribution training dataset, which is rarely the case in real-world scenarios. This paper addresses the challenging task of long-tailed OOD detection, where the in-distribution data follows a long-tailed class distribution. The main difficulty lies in distinguishing OOD data from samples belonging to the tail classes, as the ability of a classifier to detect OOD instances is not strongly correlated with its accuracy on the in-distribution classes. To overcome this issue, we propose two simple ideas: (1) Expanding the in-distribution class space by introducing multiple abstention classes. This approach allows us to build a detector with clear decision boundaries by training on OOD data using virtual labels. (2) Augmenting the context-limited tail classes by overlaying images onto the context-rich OOD data. This technique encourages the model to pay more attention to the discriminative features of the tail classes. We provide a clue for separating in-distribution and OOD data by analyzing gradient noise. Through extensive experiments, we demonstrate that our method outperforms the current state-of-the-art on various benchmark datasets. Moreover, our method can be used as an add-on for existing long-tail learning approaches, significantly enhancing their OOD detection performance. Code is available at: https://github.com/Stomach-ache/Long-Tailed-OOD-Detection.

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TMLR Journal 2024 Journal Article

Revisiting Discrete Soft Actor-Critic

  • haibin zhou
  • Tong Wei
  • Zichuan Lin
  • Junyou Li
  • Junliang Xing
  • Yuanchun Shi
  • Li Shen
  • Chao Yu

We study the adaption of Soft Actor-Critic (SAC), which is considered as a state-of-the-art reinforcement learning (RL) algorithm, from continuous action space to discrete action space. We revisit vanilla discrete SAC and provide an in-depth understanding of its Q value underestimation and performance instability issues when applied to discrete settings. We thereby propose Stable Discrete SAC (SDSAC), an algorithm that leverages entropy-penalty and double average Q-learning with Q-clip to address these issues. Extensive experiments on typical benchmarks with discrete action space, including Atari games and a large-scale MOBA game, show the efficacy of our proposed method. Our code is at: https://github.com/coldsummerday/SD-SAC.git.

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NeurIPS Conference 2024 Conference Paper

Vision-Language Models are Strong Noisy Label Detectors

  • Tong Wei
  • Hao-Tian Li
  • Chun-Shu Li
  • Jiang-Xin Shi
  • Yu-Feng Li
  • Min-Ling Zhang

Recent research on fine-tuning vision-language models has demonstrated impressive performance in various downstream tasks. However, the challenge of obtaining accurately labeled data in real-world applications poses a significant obstacle during the fine-tuning process. To address this challenge, this paper presents a Denoising Fine-Tuning framework, called DeFT, for adapting vision-language models. DeFT utilizes the robust alignment of textual and visual features pre-trained on millions of auxiliary image-text pairs to sieve out noisy labels. The proposed framework establishes a noisy label detector by learning positive and negative textual prompts for each class. The positive prompt seeks to reveal distinctive features of the class, while the negative prompt serves as a learnable threshold for separating clean and noisy samples. We employ parameter-efficient fine-tuning for the adaptation of a pre-trained visual encoder to promote its alignment with the learned textual prompts. As a general framework, DeFT can seamlessly fine-tune many pre-trained models to downstream tasks by utilizing carefully selected clean samples. Experimental results on seven synthetic and real-world noisy datasets validate the effectiveness of DeFT in both noisy label detection and image classification. Our source code can be found in the supplementary material.

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AAAI Conference 2023 Conference Paper

Can Label-Specific Features Help Partial-Label Learning?

  • Ruo-Jing Dong
  • Jun-Yi Hang
  • Tong Wei
  • Min-Ling Zhang

Partial label learning (PLL) aims to learn from inexact data annotations where each training example is associated with a coarse candidate label set. Due to its practicability, many PLL algorithms have been proposed in recent literature. Most prior PLL works attempt to identify the ground-truth labels from candidate sets and the classifier is trained afterward by fitting the features of examples and their exact ground-truth labels. From a different perspective, we propose to enrich the feature space and raise the question ``Can label-specific features help PLL?'' rather than learning from examples with identical features for all classes. Despite its benefits, previous label-specific feature approaches rely on ground-truth labels to split positive and negative examples of each class and then conduct clustering analysis, which is not directly applicable in PLL. To remedy this problem, we propose an uncertainty-aware confidence region to accommodate false positive labels. We first employ graph-based label enhancement to yield smooth pseudo-labels and facilitate the confidence region split. After acquiring label-specific features, a family of binary classifiers is induced. Extensive experiments on both synthesized and real-world datasets are conducted and the results show that our method consistently outperforms eight baselines. Our code is released at https://github.com/meteoseeker/UCL

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NeurIPS Conference 2023 Conference Paper

How Re-sampling Helps for Long-Tail Learning?

  • Jiang-Xin Shi
  • Tong Wei
  • Yuke Xiang
  • Yu-Feng Li

Long-tail learning has received significant attention in recent years due to the challenge it poses with extremely imbalanced datasets. In these datasets, only a few classes (known as the head classes) have an adequate number of training samples, while the rest of the classes (known as the tail classes) are infrequent in the training data. Re-sampling is a classical and widely used approach for addressing class imbalance issues. Unfortunately, recent studies claim that re-sampling brings negligible performance improvements in modern long-tail learning tasks. This paper aims to investigate this phenomenon systematically. Our research shows that re-sampling can considerably improve generalization when the training images do not contain semantically irrelevant contexts. In other scenarios, however, it can learn unexpected spurious correlations between irrelevant contexts and target labels. We design experiments on two homogeneous datasets, one containing irrelevant context and the other not, to confirm our findings. To prevent the learning of spurious correlations, we propose a new context shift augmentation module that generates diverse training images for the tail class by maintaining a context bank extracted from the head-class images. Experiments demonstrate that our proposed module can boost the generalization and outperform other approaches, including class-balanced re-sampling, decoupled classifier re-training, and data augmentation methods. The source code is available at https: //www. lamda. nju. edu. cn/code_CSA. ashx.

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IJCAI Conference 2023 Conference Paper

Stochastic Feature Averaging for Learning with Long-Tailed Noisy Labels

  • Hao-Tian Li
  • Tong Wei
  • Hao Yang
  • Kun Hu
  • Chong Peng
  • Li-Bo Sun
  • Xun-Liang Cai
  • Min-Ling Zhang

Deep neural networks have shown promising results on a wide variety of tasks using large-scale and well-annotated training datasets. However, data collected from real-world applications can suffer from two prevalent biases, i. e. , long-tailed class distribution and label noise. Previous efforts on long-tailed learning and label-noise learning can only address a single type of data bias, leading to a severe deterioration of their performance. In this paper, we propose a distance-based sample selection algorithm called Stochastic Feature Averaging (SFA), which fits a Gaussian using the exponential running average of class centroids to capture uncertainty in representation space due to label noise and data scarcity. With SFA, we detect noisy samples based on their distances to class centroids sampled from this Gaussian distribution. Based on the identified clean samples, we then propose to train an auxiliary balanced classifier to improve the generalization for the minority class and facilitate the update of Gaussian parameters. Extensive experimental results show that SFA can enhance the performance of existing methods on both simulated and real-world datasets. Further, we propose to combine SFA with the sample-selection approach, distribution-robust, and noise-robust loss functions, resulting in significant improvement in performance over the baselines. Our code is available at https: //github. com/HotanLee/SFA

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AAAI Conference 2019 Conference Paper

Learning Compact Model for Large-Scale Multi-Label Data

  • Tong Wei
  • Yu-Feng Li

Large-scale multi-label learning (LMLL) aims to annotate relevant labels from a large number of candidates for unseen data. Due to the high dimensionality in both feature and label spaces in LMLL, the storage overheads of LMLL models are often costly. This paper proposes a POP (joint label and feature Parameter OPtimization) method. It tries to filter out redundant model parameters to facilitate compact models. Our key insights are as follows. First, we investigate labels that have little impact on the commonly used LMLL performance metrics and only preserve a small number of dominant parameters for these labels. Second, for the remaining influential labels, we reduce spurious feature parameters that have little contribution to the generalization capability of models, and preserve parameters for only discriminative features. The overall problem is formulated as a constrained optimization problem pursuing minimal model size. In order to solve the resultant difficult optimization, we show that a relaxation of the optimization can be efficiently solved using binary search and greedy strategies. Experiments verify that the proposed method clearly reduces the model size compared to state-of-the-art LMLL approaches, in addition, achieves highly competitive performance.

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IJCAI Conference 2019 Conference Paper

Learning for Tail Label Data: A Label-Specific Feature Approach

  • Tong Wei
  • Wei-Wei Tu
  • Yu-Feng Li

Tail label data (TLD) is prevalent in real-world tasks, and large-scale multi-label learning (LMLL) is its major learning scheme. Previous LMLL studies typically need to additionally take into account extensive head label data (HLD), and thus fail to guide the learning behavior of TLD. In many applications such as recommender systems, however, the prediction of tail label is very necessary, since it provides very important supplementary information. We call this kind of problem as \emph{tail label learning}. In this paper, we propose a novel method for the tail label learning problem. Based on the observation that the raw feature representation in LMLL data usually benefits HLD, which may not be suitable for TLD, we construct effective and rich label-specific features through exploring labeled data distribution and leveraging label correlations. Specifically, we employ clustering analysis to explore discriminative features for each tail label replacing the original high-dimensional and sparse features. In addition, due to the scarcity of positive examples of TLD, we encode knowledge from HLD by exploiting label correlations to enhance the label-specific features. Experimental results verify the superiority of the proposed method in terms of performance on TLD.

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AAAI Conference 2019 Conference Paper

Towards Automated Semi-Supervised Learning

  • Yu-Feng Li
  • Hai Wang
  • Tong Wei
  • Wei-Wei Tu

Automated Machine Learning (AutoML) aims to build an appropriate machine learning model for any unseen dataset automatically, i. e. , without human intervention. Great efforts have been devoted on AutoML while they typically focus on supervised learning. In many applications, however, semisupervised learning (SSL) are widespread and current AutoML systems could not well address SSL problems. In this paper, we propose to present an automated learning system for SSL (AUTO-SSL). First, meta-learning with enhanced meta-features is employed to quickly suggest some instantiations of the SSL techniques which are likely to perform quite well. Second, a large margin separation method is proposed to fine-tune the hyperparameters and more importantly, alleviate performance deterioration. The basic idea is that, if a certain hyperparameter owns a high quality, its predictive results on unlabeled data may have a large margin separation. Extensive empirical results over 200 cases demonstrate that our proposal on one side achieves highly competitive or better performance compared to the state-of-the-art AutoML system AUTO-SKLEARN and classical SSL techniques, on the other side unlike classical SSL techniques which often significantly degenerate performance, our proposal seldom suffers from such deficiency.

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IJCAI Conference 2018 Conference Paper

Does Tail Label Help for Large-Scale Multi-Label Learning

  • Tong Wei
  • Yu-Feng Li

Large-scale multi-label learning annotates relevant labels for unseen data from a huge number of candidate labels. It is well known that in large-scale multi-label learning, labels exhibit a long tail distribution in which a significant fraction of labels are tail labels. Nonetheless, how tail labels make impact on the performance metrics in large-scale multi-label learning was not explicitly quantified. In this paper, we disclose that whatever labels are randomly missing or misclassified, tail labels impact much less than common labels in terms of commonly used performance metrics (Top-$k$ precision and nDCG@$k$). With the observation above, we develop a low-complexity large-scale multi-label learning algorithm with the goal of facilitating fast prediction and compact models by trimming tail labels adaptively. Experiments clearly verify that both the prediction time and the model size are significantly reduced without sacrificing much predictive performance for state-of-the-art approaches.

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