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Di Xie

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18 papers

2 author rows

JBHI Journal 2026 Journal Article

Developing Dynamic Prediction Methods for Survival Time Lost in Chronic Kidney Disease Progression under Competing Risks

Haoning Shen
Chengfeng Zhang
Xingzhi Wang
Di Xie
Shuyu Chen
Pansheng Xue
Yuanying Chen
Yawen Hou

Patients with chronic kidney disease (CKD) may progress to end-stage renal disease (ESRD) or die from other causes during long-term follow-up, making it essential to properly account for competing risks in prognostic modeling. However, most existing CKD prediction models rely on hazard-based measures, which primarily reflect relative effects, lack clinical interpretability, and cannot directly quantify survival time lost due to disease progression or death. To address this limitation, we adopted the restricted mean time lost (RMTL), an absolute and intuitive measure of survival time loss, and developed a dynamic prediction model under competing risks. Given the rich time-dependent covariate information in longitudinal CKD data and the clinical need for patients to understand their disease progression at different stages, we developed a dynamic RMTL prediction model that incorporates the landmark approach. The model captures how covariate effects evolve over time, offering insights into the dynamic impact of clinical variables, and enables individualized prediction of survival time loss over a future window from any given prediction time point. We evaluated its statistical properties through Monte Carlo simulations and demonstrated its practical utility using CKD patient data from the AASK cohort. The results showed that the proposed model yields accurate and robust estimates, captures time-varying covariate effects, and outperforms conventional static models in predictive performance. By quantifying survival time loss under competing risks, the dynamic RMTL model offers clinically interpretable and individualized risk estimates, supporting personalized risk assessment and intervention planning in chronic disease management.

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ICLR Conference 2025 Conference Paper

From Decoupling to Adaptive Transformation: a Wider Optimization Space for PTQ

Zhaojing Wen
Qiulin Zhang
Yuan Zhang
Rudan Chen
Xichao Yang
Di Xie
Jiang Zhu

Post-Training low-bit Quantization (PTQ) is useful to accelerate DNNs due to its high efficiency, the current SOTAs of which mostly adopt feature reconstruction with self-distillation finetuning. However, when bitwidth goes to be extremely low, we find the current reconstruction optimization space is not optimal. Considering all possible parameters and the ignored fact that integer weight can be obtained early before actual inference, we thoroughly explore different optimization space by quant-step decoupling, where a wider PTQ optimization space, which consistently makes a better optimum, is found out. Based on these, we propose an Adaptive Quantization Transformation (AdaQTransform) for PTQ reconstruction, which makes the quantized output feature better fit the FP32 counterpart with adaptive per-channel transformation, thus achieves lower feature reconstruction error. In addition, it incurs negligible extra finetuning cost and no extra inference cost. Based on AdaQTransform, for the first time, we build a general quantization setting paradigm subsuming current PTQs, QATs and other potential forms. Experiments demonstrate AdaQTransform expands the optimization space for PTQ and helps current PTQs find a better optimum over CNNs, ViTs, LLMs and image super-resolution networks, e.g., it improves NWQ by 5.7% on ImageNet for W2A2-MobileNet-v2.

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AAAI Conference 2025 Conference Paper

Gaze Label Alignment: Alleviating Domain Shift for Gaze Estimation

Guanzhong Zeng
Jingjing Wang
Zefu Xu
Pengwei Yin
Wenqi Ren
Di Xie
Jiang Zhu

Gaze estimation methods encounter significant performance deterioration when being evaluated across different domains, because of the domain gap between the testing and training data. Existing methods try to solve this issue by reducing the deviation of data distribution, however, they ignore the existence of label deviation in the data due to the acquisition mechanism of the gaze label and the individual physiological differences. In this paper, we first point out that the influence brought by the label deviation cannot be ignored, and propose a gaze label alignment algorithm (GLA) to eliminate the label distribution deviation. Specifically, we first train the feature extractor on all domains to get domain invariant features, and then select an anchor domain to train the gaze regressor. We predict the gaze label on remaining domains and use a mapping function to align the labels. Finally, these aligned labels can be used to train gaze estimation models. Therefore, our method can be combined with any existing method. Experimental results show that our GLA method can effectively alleviate the label distribution shift, and SOTA gaze estimation methods can be further improved obviously.

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NeurIPS Conference 2025 Conference Paper

Training-Free Test-Time Adaptation via Shape and Style Guidance for Vision-Language Models

Shenglong Zhou
Manjiang Yin
Leiyu Sun
Shicai Yang
Di Xie
Jiang Zhu

Test-time adaptation with pre-trained vision-language models shows impressive zero-shot classification abilities, and training-free methods further improve the performance without any optimization burden. However, existing training-free test-time adaptation methods typically rely on entropy criteria to select the visual features and update the visual caches, while ignoring the generalizable factors, such as shape-sensitive and style-insensitive factors. In this paper, we propose a novel shape and style guidance method (SSG) for training-free test-time adaptation in vision-language models, aiming to highlight the shape-sensitive (SHS) and style-insensitive (STI) factors in addition to entropy criteria. Specifically, SSG perturbs the raw test image with shape and style corruption operations, and measures the prediction difference between the raw and corrupted one as perturbed prediction difference (PPD). Based on the PPD measurement, SSG reweights the high-confidence visual features and corresponding predictions, aiming to highlight the effect of SHS and STI factors during the test-time procedure. Furthermore, SSG takes both PPD and entropy into consideration to update the visual cache, aiming to maintain the stored sample with high entropy and generalizable factors. Extensive experimental results on out-of-distribution and cross-domain benchmark datasets demonstrate that our proposed SSG consistently outperforms previous state-of-the-art methods while also exhibiting promising computational efficiency.

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ICML Conference 2025 Conference Paper

Unbiased Evaluation of Large Language Models from a Causal Perspective

Meilin Chen
Jian Tian
Liang Ma
Di Xie
Weijie Chen 0006
Jiang Zhu

Benchmark contamination has become a significant concern in the LLM evaluation community. Previous Agents-as-an-Evaluator address this issue by involving agents in the generation of questions. Despite their success, the biases in Agents-as-an-Evaluator methods remain largely unexplored. In this paper, we present a theoretical formulation of evaluation bias, providing valuable insights into designing unbiased evaluation protocols. Furthermore, we identify two type of bias in Agents-as-an-Evaluator through carefully designed probing tasks on a minimal Agents-as-an-Evaluator setup. To address these issues, we propose the Unbiased Evaluator, an evaluation protocol that delivers a more comprehensive, unbiased, and interpretable assessment of LLMs. Extensive experiments reveal significant room for improvement in current LLMs. Additionally, we demonstrate that the Unbiased Evaluator not only offers strong evidence of benchmark contamination but also provides interpretable evaluation results.

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IJCAI Conference 2025 Conference Paper

VQCounter: Designing Visual Prompt Queue for Accurate Open-World Counting

Fanfan Ye
Yiqi Fan
Qiaoyong Zhong
Shicai Yang
Di Xie
Jie Song
Mingli Song

Class-agnostic counting enables enumerating arbitrary object classes beyond those seen during training. Recent studies attempted to exploit the potential of visual foundation models such as GroundingDINO. Despite the considerable progress, we observe certain shortcomings, including the limited diversity of visual prompts and suboptimal training regimen. To address these issues, we introduce VQCounter, which incorporates a visual prompt queue mechanism designed to enrich the diversity of visual prompts. A random modality switching strategy is proposed during training to strengthen both textual and visual modalities. Besides, in light of weak point supervision, a Voronoi diagram-based cost (VoronoiCost) is designed to improve Hungarian matching, leading to more stable and faster convergence. Building upon the Voronoi diagram, we also propose a novel set of more stringent evaluation metrics, which take point localization into account. Extensive experiments on the FSC-147 and CARPK datasets demonstrate that VQCounter achieves state-of-the-art performance in both zero-shot and few-shot settings, significantly outperforming existing methods across nearly all evaluations.

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AAAI Conference 2024 Conference Paper

Arbitrary-Scale Point Cloud Upsampling by Voxel-Based Network with Latent Geometric-Consistent Learning

Hang Du
Xuejun Yan
Jingjing Wang
Di Xie
Shiliang Pu

Recently, arbitrary-scale point cloud upsampling mechanism became increasingly popular due to its efficiency and convenience for practical applications. To achieve this, most previous approaches formulate it as a problem of surface approximation and employ point-based networks to learn surface representations. However, learning surfaces from sparse point clouds is more challenging, and thus they often suffer from the low-fidelity geometry approximation. To address it, we propose an arbitrary-scale Point cloud Upsampling framework using Voxel-based Network (PU-VoxelNet). Thanks to the completeness and regularity inherited from the voxel representation, voxel-based networks are capable of providing predefined grid space to approximate 3D surface, and an arbitrary number of points can be reconstructed according to the predicted density distribution within each grid cell. However, we investigate the inaccurate grid sampling caused by imprecise density predictions. To address this issue, a density-guided grid resampling method is developed to generate high-fidelity points while effectively avoiding sampling outliers. Further, to improve the fine-grained details, we present an auxiliary training supervision to enforce the latent geometric consistency among local surface patches. Extensive experiments indicate the proposed approach outperforms the state-of-the-art approaches not only in terms of fixed upsampling rates but also for arbitrary-scale upsampling. The code is available at https://github.com/hikvision-research/3DVision

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AAAI Conference 2024 Conference Paper

CLIP-Gaze: Towards General Gaze Estimation via Visual-Linguistic Model

Pengwei Yin
Guanzhong Zeng
Jingjing Wang
Di Xie

Gaze estimation methods often experience significant performance degradation when evaluated across different domains, due to the domain gap between the testing and training data. Existing methods try to address this issue using various domain generalization approaches, but with little success because of the limited diversity of gaze datasets, such as appearance, wearable, and image quality. To overcome these limitations, we propose a novel framework called CLIP-Gaze that utilizes a pre-trained vision-language model to leverage its transferable knowledge. Our framework is the first to leverage the vision-and-language cross-modality approach for gaze estimation task. Specifically, we extract gaze-relevant feature by pushing it away from gaze-irrelevant features which can be flexibly constructed via language descriptions. To learn more suitable prompts, we propose a personalized context optimization method for text prompt tuning. Furthermore, we utilize the relationship among gaze samples to refine the distribution of gaze-relevant features, thereby improving the generalization capability of the gaze estimation model. Extensive experiments demonstrate the excellent performance of CLIP-Gaze over existing methods on four cross-domain evaluations.

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NeurIPS Conference 2022 Conference Paper

"Lossless" Compression of Deep Neural Networks: A High-dimensional Neural Tangent Kernel Approach

lingyu gu
Yongqi Du
Yuan Zhang
Di Xie
Shiliang Pu
Robert Qiu
Zhenyu Liao

Modern deep neural networks (DNNs) are extremely powerful; however, this comes at the price of increased depth and having more parameters per layer, making their training and inference more computationally challenging. In an attempt to address this key limitation, efforts have been devoted to the compression (e. g. , sparsification and/or quantization) of these large-scale machine learning models, so that they can be deployed on low-power IoT devices. In this paper, building upon recent research advances in the neural tangent kernel (NTK) and random matrix theory, we provide a novel compression approach to wide and fully-connected \emph{deep} neural nets. Specifically, we demonstrate that in the high-dimensional regime where the number of data points $n$ and their dimension $p$ are both large, and under a Gaussian mixture model for the data, there exists \emph{asymptotic spectral equivalence} between the NTK matrices for a large family of DNN models. This theoretical result enables ''lossless'' compression of a given DNN to be performed, in the sense that the compressed network yields asymptotically the same NTK as the original (dense and unquantized) network, with its weights and activations taking values \emph{only} in $\{ 0, \pm 1 \}$ up to scaling. Experiments on both synthetic and real-world data are conducted to support the advantages of the proposed compression scheme, with code available at https: //github. com/Model-Compression/Lossless_Compression.

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ICML Conference 2022 Conference Paper

Learning Domain Adaptive Object Detection with Probabilistic Teacher

Meilin Chen
Weijie Chen 0006
Shicai Yang
Jie Song 0011
Xinchao Wang
Lei Zhang 0038
Yunfeng Yan
Donglian Qi

Self-training for unsupervised domain adaptive object detection is a challenging task, of which the performance depends heavily on the quality of pseudo boxes. Despite the promising results, prior works have largely overlooked the uncertainty of pseudo boxes during self-training. In this paper, we present a simple yet effective framework, termed as Probabilistic Teacher (PT), which aims to capture the uncertainty of unlabeled target data from a gradually evolving teacher and guides the learning of a student in a mutually beneficial manner. Specifically, we propose to leverage the uncertainty-guided consistency training to promote classification adaptation and localization adaptation, rather than filtering pseudo boxes via an elaborate confidence threshold. In addition, we conduct anchor adaptation in parallel with localization adaptation, since anchor can be regarded as a learnable parameter. Together with this framework, we also present a novel Entropy Focal Loss (EFL) to further facilitate the uncertainty-guided self-training. Equipped with EFL, PT outperforms all previous baselines by a large margin and achieve new state-of-the-arts.

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IROS Conference 2022 Conference Paper

Self-Supervised Noisy Label Learning for Source-Free Unsupervised Domain Adaptation

Weijie Chen 0006
Luojun Lin
Shicai Yang
Di Xie
Shiliang Pu
Yueting Zhuang

Domain adaptation is an important property in robot vision, which enables the neural networks pre-trained on source domains to adapt target domains automatically without any annotation efforts. During this process, source data is not always accessible due to the constraints of expensive storage overhead and data privacy protection. Therefore, the source domain pre-trained model is expected to optimize with only unlabeled target data, termed as source-free unsupervised domain adaptation. In this paper, we view this problem as a special case of noisy label learning, since the given pre-trained model can generate noisy labels for unlabeled target data via network inference. The potential semantic cues for unsupervised domain adaptation exactly lie on these noisy labels. Inspired by this problem modeling, we propose a simple yet effective Self-Supervised Noisy Label Learning method, which injects self-supervised learning to impose the intrinsic data structure and facilitate label-denoising. Extensive experiments have been conducted on diverse benchmarks to validate the effectiveness. Our method achieves state-of-the-art performance.

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AAAI Conference 2022 Conference Paper

Topology-Aware Convolutional Neural Network for Efficient Skeleton-Based Action Recognition

Kailin Xu
Fanfan Ye
Qiaoyong Zhong
Di Xie

In the context of skeleton-based action recognition, graph convolutional networks (GCNs) have been rapidly developed, whereas convolutional neural networks (CNNs) have received less attention. One reason is that CNNs are considered poor in modeling the irregular skeleton topology. To alleviate this limitation, we propose a pure CNN architecture named Topology-aware CNN (Ta-CNN) in this paper. In particular, we develop a novel cross-channel feature augmentation module, which is a combo of map-attend-group-map operations. By applying the module to the coordinate level and the joint level subsequently, the topology feature is effectively enhanced. Notably, we theoretically prove that graph convolution is a special case of normal convolution when the joint dimension is treated as channels. This confirms that the topology modeling power of GCNs can also be implemented by using a CNN. Moreover, we creatively design a SkeletonMix strategy which mixes two persons in a unique manner and further boosts the performance. Extensive experiments are conducted on four widely used datasets, i. e. N-UCLA, SBU, NTU RGB+D and NTU RGB+D 120 to verify the effectiveness of Ta-CNN. We surpass existing CNN-based methods significantly. Compared with leading GCN-based methods, we achieve comparable performance with much less complexity in terms of the required GFLOPs and parameters.

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AAAI Conference 2021 Conference Paper

A Free Lunch for Unsupervised Domain Adaptive Object Detection without Source Data

Xianfeng Li
Weijie Chen
Di Xie
Shicai Yang
Peng Yuan
Shiliang Pu
Yueting Zhuang

Unsupervised domain adaptation (UDA) assumes that source and target domain data are freely available and trained together to reduce the domain gap. Considering the data privacy, it is impractical in real scenarios. Hence, it draws our eyes to optimize the network in the target domain without labeled source data. To explore this direction in object detection, for the first time, we propose a source data-free domain adaptive object detection (SFOD) framework via modeling it into learning with noisy labels. A straightforward method is to leverage the pre-trained network from the source domain to generate the pseudo labels for target domain. However, it is difficult to evaluate the quality of pseudo labels since no labels are available in target domain. In this paper, self-entropy descent (SED) is a metric proposed to search an appropriate confidence threshold for reliable pseudo label generation. Nonetheless, completely clean labels are still unattainable. After an experimental analysis, false negatives are found to dominate in the generated noisy labels. Undoubtedly, false negatives mining is helpful for improvement, and we ease it to false negatives simulation through data augmentation like Mosaic. Extensive experiments conducted in four adaptation tasks have demonstrated that the proposed framework can easily achieve state-of-the-art performance. From another view, it also reminds the UDA community that the labeled source data are not fully exploited in the existing methods.

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IROS Conference 2021 Conference Paper

Look Before You Act: Boosting Pseudo-LiDAR with Online Semantic Embedding

Liangjun Zhang
Tao Song
Tao Jiang
Di Xie
Shiliang Pu

Vision-based 3D object detection is a research focus in the field of autonomous driving system. While recently proposed pseudo-LiDAR is a promising solution, its performance is severely restricted by the image-based depth estimator, leading to a considerable performance gap against the LiDAR-based counterparts. In this paper, substantial advances are developed along an orthogonal direction to the previous efforts in the pseudo-LiDAR pipeline. Concretely, we propose a plug- and-play module, called Online Semantic Embedding (OSE), aligning image semantics with the pseudo-LiDAR detection in an end-to-end manner. On the KITTI object detection benchmark, existing stereo-based baselines integrated with our approach show impressive improvements without bells and whistles. Furthermore, we emphasize that OSE works in retrieving the performance under geometric imperfection conditions.

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AAAI Conference 2020 Conference Paper

Neural Inheritance Relation Guided One-Shot Layer Assignment Search

Rang Meng
Weijie Chen
Di Xie
Yuan Zhang
Shiliang Pu

Layer assignment is seldom picked out as an independent research topic in neural architecture search. In this paper, for the ﬁrst time, we systematically investigate the impact of different layer assignments to the network performance by building an architecture dataset of layer assignment on CIFAR-100. Through analyzing this dataset, we discover a neural inheritance relation among the networks with different layer assignments, that is, the optimal layer assignments for deeper networks always inherit from those for shallow networks. Inspired by this neural inheritance relation, we propose an efﬁcient one-shot layer assignment search approach via inherited sampling. Speciﬁcally, the optimal layer assignment searched in the shallow network can be provided as a strong sampling priori to train and search the deeper ones in supernet, which extremely reduces the network search space. Comprehensive experiments carried out on CIFAR-100 illustrate the efﬁciency of our proposed method. Our search results are strongly consistent with the optimal ones directly selected from the architecture dataset. To further conﬁrm the generalization of our proposed method, we also conduct experiments on Tiny- ImageNet and ImageNet. Our searched results are remarkably superior to the handcrafted ones under the unchanged computational budgets. The neural inheritance relation discovered in this paper can provide insights to the universal neural architecture search.

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AAAI Conference 2019 Conference Paper

A Layer Decomposition-Recomposition Framework for Neuron Pruning towards Accurate Lightweight Networks

Weijie Chen
Yuan Zhang
Di Xie
Shiliang Pu

Neuron pruning is an efficient method to compress the network into a slimmer one for reducing the computational cost and storage overhead. Most of state-of-the-art results are obtained in a layer-by-layer optimization mode. It discards the unimportant input neurons and uses the survived ones to reconstruct the output neurons approaching to the original ones in a layer-by-layer manner. However, an unnoticed problem arises that the information loss is accumulated as layer increases since the survived neurons still do not encode the entire information as before. A better alternative is to propagate the entire useful information to reconstruct the pruned layer instead of directly discarding the less important neurons. To this end, we propose a novel Layer Decomposition- Recomposition Framework (LDRF) for neuron pruning, by which each layer’s output information is recovered in an embedding space and then propagated to reconstruct the following pruned layers with useful information preserved. We mainly conduct our experiments on ILSVRC-12 benchmark with VGG-16 and ResNet-50. What should be emphasized is that our results before end-to-end fine-tuning are significantly superior owing to the information-preserving property of our proposed framework. With end-to-end fine-tuning, we achieve state-of-the-art results of 5. 13× and 3× speed-up with only 0. 5% and 0. 65% top-5 accuracy drop respectively, which outperform the existing neuron pruning methods.

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AAAI Conference 2019 Conference Paper

Learning Incremental Triplet Margin for Person Re-Identification

Yingying Zhang
Qiaoyong Zhong
Liang Ma
Di Xie
Shiliang Pu

Person re-identification (ReID) aims to match people across multiple non-overlapping video cameras deployed at different locations. To address this challenging problem, many metric learning approaches have been proposed, among which triplet loss is one of the state-of-the-arts. In this work, we explore the margin between positive and negative pairs of triplets and prove that large margin is beneficial. In particular, we propose a novel multi-stage training strategy which learns incremental triplet margin and improves triplet loss effectively. Multiple levels of feature maps are exploited to make the learned features more discriminative. Besides, we introduce global hard identity searching method to sample hard identities when generating a training batch. Extensive experiments on Market-1501, CUHK03, and DukeMTMCreID show that our approach yields a performance boost and outperforms most existing state-of-the-art methods.

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IJCAI Conference 2018 Conference Paper

Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation

Chao Li
Qiaoyong Zhong
Di Xie
Shiliang Pu

Skeleton-based human action recognition has recently drawn increasing attentions with the availability of large-scale skeleton datasets. The most crucial factors for this task lie in two aspects: the intra-frame representation for joint co-occurrences and the inter-frame representation for skeletons' temporal evolutions. In this paper we propose an end-to-end convolutional co-occurrence feature learning framework. The co-occurrence features are learned with a hierarchical methodology, in which different levels of contextual information are aggregated gradually. Firstly point-level information of each joint is encoded independently. Then they are assembled into semantic representation in both spatial and temporal domains. Specifically, we introduce a global spatial aggregation scheme, which is able to learn superior joint co-occurrence features over local aggregation. Besides, raw skeleton coordinates as well as their temporal difference are integrated with a two-stream paradigm. Experiments show that our approach consistently outperforms other state-of-the-arts on action recognition and detection benchmarks like NTU RGB+D, SBU Kinect Interaction and PKU-MMD.

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