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RecursiveDet: End-to-End Region-based Recursive Object Detection

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arXiv 2023年

作者： Zhao, Jing Sun, Li Li, Qingli Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China Key Laboratory of Advanced Theory and Application in Statistics and Data Science East China Normal University Shanghai China

End-to-end region-based object detectors like Sparse R-CNN usually have multiple cascade bounding box decoding stages, which refine the current predictions according to their previous results. Model parameters within each stage are independent, evolving a huge cost. In this paper, we find the general setting of decoding stages is actually redundant. By simply sharing parameters and making a recursive decoder, the detector already obtains a significant improvement. The recursive decoder can be further enhanced by positional encoding (PE) of the proposal box, which makes it aware of the exact locations and sizes of input bounding boxes, thus becoming adaptive to proposals from different stages during the recursion. Moreover, we also design centerness-based PE to distinguish the RoI feature element and dynamic convolution kernels at different positions within the bounding box. To validate the effectiveness of the proposed method, we conduct intensive ablations and build the full model on three recent mainstream region-based detectors. The RecusiveDet is able to achieve obvious performance boosts with even fewer model parameters and slightly increased computation cost. Codes are available at https://***/bravezzzzzz/RecursiveDet. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

High-dimensional inference for single-index model with latent factors

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arXiv 2025年

作者： Shi, Yanmei Hao, Meiling Tang, Yanlin Lian, Heng Guo, Xu School of Statistics Beijing Normal University Beijing China School of Statistics University of International Business and Economics Beijing China Key Laboratory of Advanced Theory and Application in Statistics and Data Science MOE School of Statistics East China Normal University Shanghai China Department of Mathematics City University of Hong Kong Hong Kong

Models with latent factors recently attract a lot of attention. However, most investigations focus on linear regression models and thus cannot capture nonlinearity. To address this issue, we propose a novel Factor Augmented Single-Index Model. We first address the concern whether it is necessary to consider the augmented part by introducing a score-type test statistic. Compared with previous test statistics, our proposed test statistic does not need to estimate the high-dimensional regression coefficients, nor high-dimensional precision matrix, making it simpler in implementation. We also propose a Gaussian multiplier bootstrap to determine the critical value. The validity of our procedure is theoretically established under suitable conditions. We further investigate the penalized estimation of the regression model. With estimated latent factors, we establish the error bounds of the estimators. Lastly, we introduce debiased estimator and construct confidence interval for individual coefficient based on the asymptotic normality. No moment condition for the error term is imposed for our proposal. Thus our procedures work well when random error follows heavy-tailed distributions or when outliers are present. We demonstrate the finite sample performance of the proposed method through comprehensive numerical studies and its application to an FRED-MD macroeconomics dataset. Copyright © 2025, The Authors. All rights reserved.

关键词： Random errors

Causal Mediation Analysis with a Three-Dimensional Image Mediator

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arXiv 2023年

作者： Chen, Minghao Zhou, Yingchun Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE Institute of Brain and Education Innovation School of Statistics East China Normal University 3663 North Zhongshan Road Shanghai200062 China

Causal mediation analysis is increasingly abundant in biology, psychology, and epidemiology studies, etc. In particular, with the advent of the big data era, the issue of high-dimensional mediators is becoming more prevalent. In neuroscience, with the widespread application of magnetic resonance technology in the field of brain imaging, studies on image being a mediator emerged. In this study, a novel causal mediation analysis method with a three-dimensional image mediator is proposed. We define the average casual effects under the potential outcome framework, explore several sufficient conditions for the valid identification, and develop techniques for estimation and inference. To verify the effectiveness of the proposed method, a series of simulations under various scenarios is performed. Finally, the proposed method is applied to a study on the causal effect of mother's delivery mode on child's IQ development. It is found that the white matter in certain regions of the frontal-parietal and brainstem-cerebellum areas has mediating effects. © 2023, CC BY.

关键词： Magnetic resonance

Distributed algorithms for u-statistics-based empirical risk minimization

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The Journal of Machine Learning Research

The Journal of Machine Learning Research 2023年第1期24卷 12301-12343页

作者： Lanjue Chen Alan T.K. Wan Shuyi Zhang Yong Zhou Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education Academy of Statistics and Interdisciplinary Sciences and School of Statistics East China Normal University Shanghai China Department of Management Sciences School of Data Science and Department of Biostatistics City University of Hong Kong Kowloon Hong Kong

Empirical risk minimization, where the underlying loss function depends on a pair of data points, covers a wide range of application areas in statistics including pairwise ranking and survival analysis. The common empirical risk estimator obtained by averaging values of a loss function over all possible pairs of observations is essentially a U-statistic. One well-known problem with minimizing U-statistic type empirical risks, is that the computational complexity of U-statistics increases quadratically with the sample size. When faced with big data, this poses computational challenges as the colossal number of observation pairs virtually prohibits centralized computing to be performed on a single machine. This paper addresses this problem by developing two computationally and statistically efficient methods based on the divide-and-conquer strategy on a decentralized computing system, whereby the data are distributed among machines to perform the tasks. One of these methods is based on a surrogate of the empirical risk, while the other method extends the one-step updating scheme in classical M-estimation to the case of pairwise loss. We show that the proposed estimators are as asymptotically efficient as the benchmark global U-estimator obtained under centralized computing. As well, we introduce two distributed iterative algorithms to facilitate the implementation of the proposed methods, and conduct extensive numerical experiments to demonstrate their merit.

关键词：

CLIP-Driven Open-Vocabulary 3D Scene Graph Generation via Cross-Modality Contrastive Learning

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CLIP-Driven Open-Vocabulary 3D Scene Graph Generation via Cr...

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Lianggangxu Chen Xuejiao Wang Jiale Lu Shaohui Lin Changbo Wang Gaoqi He School of Computer Science and Technology East China Normal University Shanghai China Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education China Chongqing Key Laboratory of Precision Optics Chongqing Institute of East China Normal University Chongqing China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

3D Scene Graph Generation (3DSGG) aims to classify objects and their predicates within 3D point cloud scenes. However, current 3DSGG methods struggle with two main challenges. 1) The dependency on labor-intensive ground-truth annotations. 2) Closed-set classes training hampers the recognition of novel objects and predicates. Addressing these issues, our idea is to extract cross-modality features by CLIP from text and image data naturally related to 3D point clouds. Cross-modality features are used to train a robust 3D scene graph (3DSG)feature extractor. Specifically, we propose a novel Cross-Modality Contrastive Learning 3DSGG (CCL-3DSGG) method. Firstly, to align the text with 3DSG, the text is parsed into word level that are consistent with the 3DSG annotation. To enhance robustness during the alignment, adjectives are exchanged for different objects as negative samples. Then, to align the image with 3DSG, the camera view is treated as a positive sample and other views as negatives. Lastly, the recognition of novel object and predicate classes is achieved by calculating the cosine similarity between prompts and 3DSG features. Our rigorous experiments confirm the superior open-vocabulary capability and applicability of CCL-3DSGG in real-world contexts.

关键词： Point cloud compression Training Computer vision Three-dimensional displays Annotations Contrastive learning Feature extraction

Rethinking Centered Kernel Alignment in Knowledge Distillation

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arXiv 2024年

作者： Zhou, Zikai Shen, Yunhang Shao, Shitong Gong, Linrui Lin, Shaohui School of Computer Science and Technology East China Normal University Shanghai China Key Laboratory of Advanced Theory and Application in Statistics and Data Science MOE China Youtu Lab Tencent Shanghai China Hunan University China

Knowledge distillation has emerged as a highly effective method for bridging the representation discrepancy between large-scale models and lightweight models. Prevalent approaches involve leveraging appropriate metrics to minimize the divergence or distance between the knowledge extracted from the teacher model and the knowledge learned by the student model. Centered Kernel Alignment (CKA) is widely used to measure representation similarity and has been applied in several knowledge distillation methods. However, these methods are complex and fail to uncover the essence of CKA, thus not answering the question of how to use CKA to achieve simple and effective distillation properly. This paper first provides a theoretical perspective to illustrate the effectiveness of CKA, which decouples CKA to the upper bound of Maximum Mean Discrepancy (MMD) and a constant term. Drawing from this, we propose a novel Relation-Centered Kernel Alignment (RCKA) framework, which practically establishes a connection between CKA and MMD. Furthermore, we dynamically customize the application of CKA based on the characteristics of each task, with less computational source yet comparable performance than the previous methods. The extensive experiments on the CIFAR-100, ImageNet-1k, and MS-COCO demonstrate that our method achieves state-of-the-art performance on almost all teacher-student pairs for image classification and object detection, validating the effectiveness of our approaches. Our code is available in https://***/Klayand/PCKA. Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

Effects of Steam Curing on the Resistance of Cementitious Materials to Chloride Penetration

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Journal of Wuhan University of Technology(Materials Science) 2022年第5期37卷 885-891页

作者： CHEN Xuefei HE Yongjia Lü Linnü ZHANG Xulong HU Shuguang State Key Laboratory of Silicate Materials for Architectures Wuhan University of TechnologyWuhan430070China School of Materials Science and Engineering Wuhan University of TechnologyWuhan430070China Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics School of ScienceWuhan University of TechnologyWuhan430070China Hubei Provincial Academy of Building Research and Design Co. LtdWuhan430071China

The resistance to chloride penetration of cement-based material with different curing regimes was investigated by solution titration,XRD,LF-NMR and rapid chloride migration *** results show that the curing regime has influences on the types and structure of the hydration products,which in turn affects their ability to bind chloride *** binding capacity of cementitious materials to chloride ions,porosity and chloride ion migration coefficient increased with the increase of water-cement ratio,while steam curing increased the porosity and chloride ion migration coefficient at the same time as it increased the chloride ion binding capacity of the *** lower water-cement ratios,the effect of steam curing on the resistance of cementitious materials to chloride ingress is negligible.

关键词： chloride penetration chloride binding capacity pore structure steam curing cementitious materials

A Two-Step Test for the Two-Sample Problem of Processes of Ornstein-Uhlenbeck Type

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Journal of Systems Science & Complexity 2020年第6期33卷 2067-2079页

作者： WANG Yinfeng TANG Yanlin ZHANG Xinsheng School of Statistics and Mathematics Shanghai Lirin University of Acounting and FinanceShanghaiChina Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of StatisticsEast China Normal UniversityShanghaiChina Department of Statistics Fudan UniversityShanghaiChina

The authors propose a two-step test for the two-sample problem of processes of OrnsteinUhlenbeck type. In the first step, the authors test the equality of correlation structures, based on the least square estimators of the correlation parameters, and the test statistic follows the standard normal distribution. If the null hypothesis is not rejected in the first step, the authors consider a second step to test the equality of marginal distributions, based on the weighted deviation of the empirical characteristic functions;the test statistic has a complicated asymptotic distribution, so that sequential bootstrap method is applied to reach a temporary decision. Simulation studies and real data analysis suggest that the proposed approach performs well in finite samples.

关键词： Empirical characteristic functions hypothesis testing process of Ornstein-Uhlenbeck type sequential Bootstrap

Two applications of Fourier Transform for Lévy Measures On Rp

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SSRN

SSRN 2023年

作者： Qiu, Rui Li, Lu Yin, Xiangrong Yu, Zhou School of Statistics East China Normal University Shanghai200062 China Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE Shanghai200062 China Department of Statistics University of Kentucky LexingtonKY40536-0082 United States

For univariate variable x ∈ R, the Lévy measures for the two real-valued continuous negative definite functions ln and lncosh(x) are shown in Böttcher et al. (2018). In this note, by utilizing the Fourier transform, we present the corresponding Lévy measures for ln and lncosh(|x|) with x ∈ Rp. These Lévy measures have significant potential applications across various domains. © 2023, The Authors. All rights reserved.

关键词： Fourier transforms