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Tao Xu

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11 papers

2 author rows

AAAI Conference 2026 Conference Paper

Refine3D: Scene-Adaptive Reference Point Refinement for Sparse 3D Object Detection

Fan Li
Jing Lu
Yunlu Xu
Changhong Wu
Tao Xu
Zhaoyi Xiang
Yi Niu

Sparse query-based detectors have emerged as the dominant paradigm in camera-only 3D object detection, owing to their exceptional performance and computational efficiency. A central component of these approaches is the use of reference points, which serve as learnable spatial anchors to guide queries in localizing target objects. However, existing methods typically employ a unified set of reference points across all scenes, a design we find suboptimal for handling complex scenarios with highly imbalanced object distributions, such as road intersections or occluded environments. In this paper, we investigate the adaptability of reference points and propose Refine3D, an adaptive refinement mechanism that achieves scene-level alignment between the distribution of reference points and ground-truth objects. In particular, we introduce a novel Reference Point Distribution Loss (RPD-Loss) to ensure reference points converge globally toward object positions, and a Scene-Adaptive Refinement head (SAR-Head) that predicts dynamic offsets for each reference point. Both components can be seamlessly integrated into mainstream sparse detectors. Extensive experiments on two challenging autonomous driving datasets demonstrate that Refine3D outperforms the state-of-the-art with improved detection accuracy and robustness.

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AIIM Journal 2025 Journal Article

DRExplainer: Quantifiable interpretability in drug response prediction with directed graph convolutional network

Haoyuan Shi
Tao Xu
Xiaodi Li
Qian Gao
Zhiwei Xiong
Junfeng Xia
Zhenyu Yue

Predicting the response of a cancer cell line to a therapeutic drug is pivotal for personalized medicine. Despite numerous deep learning methods that have been developed for drug response prediction, integrating diverse information about biological entities and predicting the directional response remain major challenges. Here, we propose a novel interpretable predictive model, DRExplainer, which leverages a directed graph convolutional network to enhance the prediction in a directed bipartite network framework. DRExplainer constructs a directed bipartite network integrating multi-omics profiles of cell lines, the chemical structure of drugs and known drug response to achieve directed prediction. Then, DRExplainer identifies the most relevant subgraph to each prediction in this directed bipartite network by learning a mask, facilitating critical medical decision-making. Additionally, we introduce a quantifiable method for model interpretability that leverages a ground truth benchmark dataset curated from biological features. In computational experiments, DRExplainer outperforms state-of-the-art predictive methods and another graph-based explanation method under the same experimental setting. Finally, the case studies further validate the interpretability and the effectiveness of DRExplainer in predictive novel drug response. Our code is available at: https: //github. com/vshy-dream/DRExplainer.

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JBHI Journal 2025 Journal Article

Interpretable Dynamic Directed Graph Convolutional Network for Multi-Relational Prediction of Missense Mutation and Drug Response

Qian Gao
Tao Xu
Xiaodi Li
Wanling Gao
Haoyuan Shi
Youhua Zhang
Jie Chen
Zhenyu Yue

Tumor heterogeneity presents a significant challenge in predicting drug responses, especially as missense mutations within the same gene can lead to varied outcomes such as drug resistance, enhanced sensitivity, or therapeutic ineffectiveness. These complex relationships highlight the need for advanced analytical approaches in oncology. Due to their powerful ability to handle heterogeneous data, graph convolutional networks (GCNs) represent a promising approach for predicting drug responses. However, simple bipartite graphs cannot accurately capture the complex relationships involved in missense mutation and drug response. Furthermore, Deep learning models for drug response are often considered “black boxes”, and their interpretability remains a widely discussed issue. To address these challenges, we propose an Interpretable Dynamic Directed Graph Convolutional Network (IDDGCN) framework, which incorporates four key features: 1) the use of directed graphs to differentiate between sensitivity and resistance relationships, 2) the dynamic updating of node weights based on node-specific interactions, 3) the exploration of associations between different mutations within the same gene and drug response, and 4) the enhancement of interpretability models through the integration of a weighted mechanism that accounts for the biological significance, alongside a ground truth construction method to evaluate prediction transparency. The experimental results demonstrate that IDDGCN outperforms existing state-of-the-art models, exhibiting excellent predictive power. Both qualitative and quantitative evaluations of its interpretability further highlight its ability to explain predictions, offering a fresh perspective for precision oncology and targeted drug development.

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ICML Conference 2025 Conference Paper

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation

Philippe Hansen-Estruch
David Yan
Ching-Yao Chuang
Orr Zohar
Jialiang Wang 0001
Tingbo Hou
Tao Xu
Sriram Vishwanath

Visual tokenization via auto-encoding empowers state-of-the-art image and video generative models by compressing pixels into a latent space. However, questions remain about how auto-encoder design impacts reconstruction and downstream generative performance. This work explores scaling in auto-encoders for reconstruction and generation by replacing the convolutional backbone with an enhanced Vision Transformer for Tokenization (ViTok). We find scaling the auto-encoder bottleneck correlates with reconstruction but exhibits a nuanced relationship with generation. Separately, encoder scaling yields no gains, while decoder scaling improves reconstruction with minimal impact on generation. As a result, we determine that scaling the current paradigm of auto-encoders is not effective for improving generation performance. Coupled with Diffusion Transformers, ViTok achieves competitive image reconstruction and generation performance on 256p and 512p ImageNet-1K. In videos, ViTok achieves SOTA reconstruction and generation performance on 16-frame 128p UCF-101.

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JBHI Journal 2024 Journal Article

Driving Fatigue Detection Based on Hybrid Electroencephalography and Eye Tracking

Zequan Lian
Tao Xu
Zhen Yuan
Junhua Li
Nitish Thakor
Hongtao Wang

EEG-based unimodal method has demonstrated significant success in the detection of driving fatigue. Nonetheless, data from a single modality might be not sufficient to optimize fatigue detection due to incomplete information. To address this limitation and enhance the performance of driving fatigue detection, a novel multimodal architecture combining hybrid electroencephalograph (EEG) and eye tracking data was proposed in this work. Specifically, the EEG and eye tracking data were separately input into encoders, generating two one-dimensional (1D) features. Subsequently, these 1D features were fed into a cross-modal predictive alignment module to improve fusion efficiency and two 1D attention modules to enhance feature representation. Furthermore, the fused features were recognized by a linear classifier. To evaluate the effectiveness of the proposed multimodal method, comprehensive validation tasks were conducted, including intra-session, cross-session, and cross-subject evaluations. In the intra-session task, the proposed architecture achieves an exceptional average accuracy of 99. 93%. Moreover, in the cross-session task, our method demonstrates an average accuracy of 88. 67%, surpassing the performance of EEG-only approach by 8. 52%, eye tracking-only method by 5. 92%, multimodal deep canonical correlation analysis (DCCA) technique by 0. 42%, and multimodal deep generalized canonical correlation analysis (DGCCA) approach by 0. 84%. Similarly, in the cross-subject task, the proposed approach achieves an average accuracy of 78. 19%, outperforming EEG-only method by 5. 87%, eye tracking-only approach by 4. 21%, DCCA method by 0. 55%, and DGCCA approach by 0. 44%. The experimental results conclusively illustrate the superior effectiveness of the proposed method compared to both single modality approaches and canonical correlation analysis-based multimodal methods.

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YNIMG Journal 2024 Journal Article

Meso-scale reorganization of local–global brain networks under mild sedation of propofol anesthesia

Kangli Dong
Lu Zhang
Yuming Zhong
Tao Xu
Yue Zhao
Siya Chen
Seedahmed S. Mahmoud
Qiang Fang

The fragmentation of the functional brain network has been identified through the functional connectivity (FC) analysis in studies investigating anesthesia-induced loss of consciousness (LOC). However, it remains unclear whether mild sedation of anesthesia can cause similar effects. This paper aims to explore the changes in local-global brain network topology during mild anesthesia, to better understand the macroscopic neural mechanism underlying anesthesia sedation. We analyzed high-density EEG from 20 participants undergoing mild and moderate sedation of propofol anesthesia. By employing a local-global brain parcellation in EEG source analysis, we established binary functional brain networks for each participant. Furthermore, we investigated the global-scale properties of brain networks by estimating global efficiency and modularity, and examined the changes in meso-scale properties of brain networks by quantifying the distribution of high-degree and high-betweenness hubs and their corresponding rich-club coefficients. It is evident from the results that the mild sedation of anesthesia does not cause a significant change in the global-scale properties of brain networks. However, network components centered on SomMot L show a significant decrease, while those centered on Default L, Vis L and Limbic L exhibit a significant increase during the transition from wakefulness to mild sedation (p<0.05). Compared to the baseline state, mild sedation almost doubled the number of high-degree hubs in Vis L, DorsAttn L, Limbic L, Cont L, and reduced by half the number of high-degree hubs in SomMot R, DorsAttn R, SalVentAttn R. Further, mild sedation almost doubled the number of high-betweenness hubs in Vis L, Vis R, Limbic R, Cont R, and reduced by half the number of high-betweenness hubs in SomMot L, SalVentAttn L, Default L, and SomMot R. Our results indicate that mild anesthesia cannot affect the global integration and segregation of brain networks, but influence meso-scale function for integrating different resting-state systems involved in various segregation processes. Our findings suggest that the meso-scale brain network reorganization, situated between global integration and local segregation, could reflect the autonomic compensation of the brain for drug effects. As a direct response and adjustment of the brain network system to drug administration, this spontaneous reorganization of the brain network aims at maintaining consciousness in the case of sedation.

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ICML Conference 2023 Conference Paper

Robust Speech Recognition via Large-Scale Weak Supervision

Alec Radford
Jong Wook Kim
Tao Xu
Greg Brockman
Christine McLeavey
Ilya Sutskever

We study the capabilities of speech processing systems trained simply to predict large amounts of transcripts of audio on the internet. When scaled to 680, 000 hours of multilingual and multitask supervision, the resulting models generalize well to standard benchmarks and are often competitive with prior fully supervised results without the need for any dataset specific fine-tuning. When compared to humans, the models approach their accuracy and robustness. We are releasing models and inference code to serve as a foundation for further work on robust speech processing.

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IROS Conference 2021 Conference Paper

Autonomous object harvesting using synchronized optoelectronic microrobots

Christopher Bendkowski
Laurent Mennillo
Tao Xu
Mohamed Elsayed 0008
Filip Stojic
Harrison Edwards
Shuailong Zhang
Cindi Morshead

Optoelectronic tweezer-driven microrobots (OETdMs) are a versatile micromanipulation technology based on the application of light induced dielectrophoresis to move small dielectric structures (microrobots) across a photoconductive substrate. The microrobots in turn can be used to exert forces on secondary objects and carry out a wide range of micromanipulation operations, including collecting, transporting and depositing microscopic cargos. In contrast to alternative (direct) micromanipulation techniques, OETdMs are relatively gentle, making them particularly well suited to interacting with sensitive objects such as biological cells. However, at present such systems are used exclusively under manual control by a human operator. This limits the capacity for simultaneous control of multiple microrobots, reducing both experimental throughput and the possibility of cooperative multi-robot operations. In this article, we describe an approach to automated targeting and path planning to enable open-loop control of multiple microrobots. We demonstrate the performance of the method in practice, using microrobots to simultaneously collect, transport and deposit silica microspheres. Using computational simulations based on real microscopic image data, we investigate the capacity of microrobots to collect target cells from within a dissociated tissue culture. Our results indicate the feasibility of using OETdMs to autonomously carry out micromanipulation tasks within complex, unstructured environments.

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UAI Conference 2021 Conference Paper

Principal component analysis in the stochastic differential privacy model

Fanhua Shang
Zhihui Zhang
Tao Xu
Yuanyuan Liu 0001
Hongying Liu 0001

In this paper, we study the differentially private Principal Component Analysis (PCA) problem in stochastic optimization settings. We first propose a new stochastic gradient perturbation PCA mechanism (DP-SPCA) for the calculation of the right singular subspace to achieve $(\epsilon, \delta)$-differential privacy. For achieving a better utility guarantee and performance, we then present a new differential privacy stochastic variance reduction mechanism (DP-VRPCA) with gradient perturbation for PCA. To the best of our knowledge, this is the first work of stochastic gradient perturbation for $(\epsilon, \delta)$-differentially private PCA. We also compare the proposed algorithms with existing state-of-the-art methods, and experiments on real-world datasets and on classification tasks confirm the improved theoretical guarantees of our algorithms.

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JBHI Journal 2020 Journal Article

Computer-Aided Diagnosis in Histopathological Images of the Endometrium Using a Convolutional Neural Network and Attention Mechanisms

Hao Sun
Xianxu Zeng
Tao Xu
Gang Peng
Yutao Ma

Uterine cancer (also known as endometrial cancer) can seriously affect the female reproductive system, and histopathological image analysis is the gold standard for diagnosing endometrial cancer. Due to the limited ability to model the complicated relationships between histopathological images and their interpretations, existing computer-aided diagnosis (CAD) approaches using traditional machine learning algorithms often failed to achieve satisfying results. In this study, we develop a CAD approach based on a convolutional neural network (CNN) and attention mechanisms, called HIENet. In the ten-fold cross-validation on ∼3, 300 hematoxylin and eosin (H&E) image patches from ∼500 endometrial specimens, HIENet achieved a 76. 91 ± 1. 17% (mean ± s. d.) accuracy for four classes of endometrial tissue, i. e. , normal endometrium, endometrial polyp, endometrial hyperplasia, and endometrial adenocarcinoma. Also, HIENet obtained an area-under-the-curve (AUC) of 0. 9579 ± 0. 0103 with an 81. 04 ± 3. 87% sensitivity and 94. 78 ± 0. 87% specificity in a binary classification task that detected endometrioid adenocarcinoma. Besides, in the external validation on 200 H&E image patches from 50 randomly-selected female patients, HIENet achieved an 84. 50% accuracy in the four-class classification task, as well as an AUC of 0. 9829 with a 77. 97% (95% confidence interval, CI, 65. 27%∼87. 71%) sensitivity and 100% (95% CI, 97. 42%∼100. 00%) specificity. The proposed CAD method outperformed three human experts and five CNN-based classifiers regarding overall classification performance. It was also able to provide pathologists better interpretability of diagnoses by highlighting the histopathological correlations of local pixel-level image features to morphological characteristics of endometrial tissue.

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ICRA Conference 2011 Conference Paper

Rebalance strategies for humanoids walking by foot positioning compensator based on adaptive heteroscedastic SpGPs

Tao Xu
Qijun Chen
Zhiqiang Cai 0005

To solve the rebalance problem of a full-body humanoid walking, an adaptive foot positioning compensation approach is proposed. To obtain a more precise initial policy, a constrained dynamics model is used to generate the offline policy. A heteroscedastic sparse Gaussian process is applied for online calculation of the foot positioning policy. In order to make the generated policy to adapt with the full-body dynamics, a sample-efficient MAP-like updating method for the heteroscedastic sparse Gaussian process model is also proposed. Experiments on both simulation and a real full-body humanoid are developed to show the performance of the final foot positioning policy. With the help of proposed method, the full-body humanoid robot succeeded walking down an elastic deformable platform and several obvious compensation foot steps can be observed for the robot to retrieve its balance.

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