arXiv subjects

Junbiao Pang

Publications and source records attributed to Junbiao Pang.

At least 37 records · Page 2Linked to original sources

arXiv2025

Rectify and Align GPS Points to Parking Spots via Rank-1 Constraint

Parking spots are essential components, providing vital mobile resources for residents in a city. Accurate Global Positioning System (GPS) points of parking spots are the core data for subsequent applications,e.g., parking management, parking policy, and urban development. However, high-rise buildings tend to cause GPS points to drift from the actual locations of parking spots; besides, the standard lower-cost GPS equipment itself has a certain location error. Therefore, it is a non-trivial task to correct a few wrong GPS points from a large number of parking spots in an unsupervised approach. In this paper, motivated by the physical constraints of parking spots (i.e., parking spots are parallel to the sides of roads), we propose an unsupervised low-rank method to effectively rectify errors in GPS points and further align them to the parking spots in a unified framework. The proposed unconventional rectification and alignment method is simple and yet effective for any type of GPS point errors. Extensive experiments demonstrate the superiority of the proposed method to solve a practical problem. The data set and the code are publicly accessible at:https://github.com/pangjunbiao/ITS-Parking-spots-Dataset.

cs.LG↗

arXiv2025

Few Shot Semi-Supervised Learning for Abnormal Stop Detection from Sparse GPS Trajectories

Abnormal stop detection (ASD) in intercity coach transportation is critical for ensuring passenger safety, operational reliability, and regulatory compliance. However, two key challenges hinder ASD effectiveness: sparse GPS trajectories, which obscure short or unauthorized stops, and limited labeled data, which restricts supervised learning. Existing methods often assume dense sampling or regular movement patterns, limiting their applicability. To address data sparsity, we propose a Sparsity-Aware Segmentation (SAS) method that adaptively defines segment boundaries based on local spatial-temporal density. Building upon these segments, we introduce three domain-specific indicators to capture abnormal stop behaviors. To further mitigate the impact of sparsity, we develop Locally Temporal-Indicator Guided Adjustment (LTIGA), which smooths these indicators via local similarity graphs. To overcome label scarcity, we construct a spatial-temporal graph where each segment is a node with LTIGA-refined features. We apply label propagation to expand weak supervision across the graph, followed by a GCN to learn relational patterns. A final self-training module incorporates high-confidence pseudo-labels to iteratively improve predictions. Experiments on real-world coach data show an AUC of 0.854 and AP of 0.866 using only 10 labeled instances, outperforming prior methods. The code and dataset are publicly available at \href{https://github.com/pangjunbiao/Abnormal-Stop-Detection-SSL.git}

cs.LG↗

arXiv2025

Adaptively Sampling-Reusing-Mixing Decomposed Gradients to Speed Up Sharpness Aware Minimization

Sharpness-Aware Minimization (SAM) improves model generalization but doubles the computational cost of Stochastic Gradient Descent (SGD) by requiring twice the gradient calculations per optimization step. To mitigate this, we propose Adaptively sampling-Reusing-mixing decomposed gradients to significantly accelerate SAM (ARSAM). Concretely, we firstly discover that SAM's gradient can be decomposed into the SGD gradient and the Projection of the Second-order gradient onto the First-order gradient (PSF). Furthermore, we observe that the SGD gradient and PSF dynamically evolve during training, emphasizing the growing role of the PSF to achieve a flat minima. Therefore, ARSAM is proposed to the reused PSF and the timely updated PSF still maintain the model's generalization ability. Extensive experiments show that ARSAM achieves state-of-the-art accuracies comparable to SAM across diverse network architectures. On CIFAR-10/100, ARSAM is comparable to SAM while providing a speedup of about 40\%. Moreover, ARSAM accelerates optimization for the various challenge tasks (\textit{e.g.}, human pose estimation, and model quantization) without sacrificing performance, demonstrating its broad practicality.% The code is publicly accessible at: https://github.com/ajiaaa/ARSAM.

cs.CV↗

arXiv2025

MEC-Quant: Maximum Entropy Coding for Extremely Low Bit Quantization-Aware Training

Quantization-Aware Training (QAT) has driven much attention to produce efficient neural networks. Current QAT still obtains inferior performances compared with the Full Precision (FP) counterpart. In this work, we argue that quantization inevitably introduce biases into the learned representation, especially under the extremely low-bit setting. To cope with this issue, we propose Maximum Entropy Coding Quantization (MEC-Quant), a more principled objective that explicitly optimizes on the structure of the representation, so that the learned representation is less biased and thus generalizes better to unseen in-distribution samples. To make the objective end-to-end trainable, we propose to leverage the minimal coding length in lossy data coding as a computationally tractable surrogate for the entropy, and further derive a scalable reformulation of the objective based on Mixture Of Experts (MOE) that not only allows fast computation but also handles the long-tailed distribution for weights or activation values. Extensive experiments on various tasks on computer vision tasks prove its superiority. With MEC-Qaunt, the limit of QAT is pushed to the x-bit activation for the first time and the accuracy of MEC-Quant is comparable to or even surpass the FP counterpart. Without bells and whistles, MEC-Qaunt establishes a new state of the art for QAT.

cs.CV↗

arXiv2025

Asymptotic Unbiased Sample Sampling to Speed Up Sharpness-Aware Minimization

Sharpness-Aware Minimization (SAM) has emerged as a promising approach for effectively reducing the generalization error. However, SAM incurs twice the computational cost compared to base optimizer (e.g., SGD). We propose Asymptotic Unbiased Sampling with respect to iterations to accelerate SAM (AUSAM), which maintains the model's generalization capacity while significantly enhancing computational efficiency. Concretely, we probabilistically sample a subset of data points beneficial for SAM optimization based on a theoretically guaranteed criterion, i.e., the Gradient Norm of each Sample (GNS). We further approximate the GNS by the difference in loss values before and after perturbation in SAM. As a plug-and-play, architecture-agnostic method, our approach consistently accelerates SAM across a range of tasks and networks, i.e., classification, human pose estimation and network quantization. On CIFAR10/100 and Tiny-ImageNet, AUSAM achieves results comparable to SAM while providing a speedup of over 70%. Compared to recent dynamic data pruning methods, AUSAM is better suited for SAM and excels in maintaining performance. Additionally, AUSAM accelerates optimization in human pose estimation and model quantization without sacrificing performance, demonstrating its broad practicality.

cs.CV↗

arXiv2025

Stabilizing Quantization-Aware Training by Implicit-Regularization on Hessian Matrix

Quantization-Aware Training (QAT) is one of the prevailing neural network compression solutions. However, its stability has been challenged for yielding deteriorating performances as the quantization error is inevitable. We find that the sharp landscape of loss, which leads to a dramatic performance drop, is an essential factor that causes instability. Theoretically, we have discovered that the perturbations in the feature would bring a flat local minima. However, simply adding perturbations into either weight or feature empirically deteriorates the performance of the Full Precision (FP) model. In this paper, we propose Feature-Perturbed Quantization (FPQ) to stochastically perturb the feature and employ the feature distillation method to the quantized model. Our method generalizes well to different network architectures and various QAT methods. Furthermore, we mathematically show that FPQ implicitly regularizes the Hessian norm, which calibrates the smoothness of a loss landscape. Extensive experiments demonstrate that our approach significantly outperforms the current State-Of-The-Art (SOTA) QAT methods and even the FP counterparts.

cs.CV↗

arXiv2025

Uncertainty-aware Long-tailed Weights Model the Utility of Pseudo-labels for Semi-supervised Learning

Current Semi-supervised Learning (SSL) adopts the pseudo-labeling strategy and further filters pseudo-labels based on confidence thresholds. However, this mechanism has notable drawbacks: 1) setting the reasonable threshold is an open problem which significantly influences the selection of the high-quality pseudo-labels; and 2) deep models often exhibit the over-confidence phenomenon which makes the confidence value an unreliable indicator for assessing the quality of pseudo-labels due to the scarcity of labeled data. In this paper, we propose an Uncertainty-aware Ensemble Structure (UES) to assess the utility of pseudo-labels for unlabeled samples. We further model the utility of pseudo-labels as long-tailed weights to avoid the open problem of setting the threshold. Concretely, the advantage of the long-tailed weights ensures that even unreliable pseudo-labels still contribute to enhancing the model's robustness. Besides, UES is lightweight and architecture-agnostic, easily extending to various computer vision tasks, including classification and regression. Experimental results demonstrate that combining the proposed method with DualPose leads to a 3.47% improvement in Percentage of Correct Keypoints (PCK) on the Sniffing dataset with 100 data points (30 labeled), a 7.29\% improvement in PCK on the FLIC dataset with 100 data points (50 labeled), and a 3.91% improvement in PCK on the LSP dataset with 200 data points (100 labeled). Furthermore, when combined with FixMatch, the proposed method achieves a 0.2% accuracy improvement on the CIFAR-10 dataset with 40 labeled data points and a 0.26% accuracy improvement on the CIFAR-100 dataset with 400 labeled data points.

cs.CV↗

arXiv2025

In-Distribution Consistency Regularization Improves the Generalization of Quantization-Aware Training

Although existing Quantization-Aware Training (QAT) methods intensively depend on knowledge distillation to guarantee performance, QAT still suffers from severe performance drop. The experiments have shown that vanilla quantization is sensitive to the perturbation from both the input and weights. Therefore, we assume that the generalization ability of QAT is predominantly caused by both the intrinsic instability (training time) and the limited generalization ability (testing time). In this paper, we address both issues from a new perspective by leveraging Consistency Regularization (CR) to improve the generalization ability of QAT. Empirical results and theoretical analysis verify that CR would bring a good generalization ability to different network architectures and various QAT methods. Extensive experiments demonstrate that our approach significantly outperforms current state-of-the-art QAT methods and even the FP counterparts. On CIFAR-10, the proposed method improves by 3.79% compared to the baseline method using ResNet18, and improves by 3.84% compared to the baseline method using the lightweight model MobileNet.

cs.CV↗

arXiv2024

Unsupervised Abnormal Stop Detection for Long Distance Coaches with Low-Frequency GPS

In our urban life, long distance coaches supply a convenient yet economic approach to the transportation of the public. One notable problem is to discover the abnormal stop of the coaches due to the important reason, i.e., illegal pick up on the way which possibly endangers the safety of passengers. It has become a pressing issue to detect the coach abnormal stop with low-quality GPS. In this paper, we propose an unsupervised method that helps transportation managers to efficiently discover the Abnormal Stop Detection (ASD) for long distance coaches. Concretely, our method converts the ASD problem into an unsupervised clustering framework in which both the normal stop and the abnormal one are decomposed. Firstly, we propose a stop duration model for the low frequency GPS based on the assumption that a coach changes speed approximately in a linear approach. Secondly, we strip the abnormal stops from the normal stop points by the low rank assumption. The proposed method is conceptually simple yet efficient, by leveraging low rank assumption to handle normal stop points, our approach enables domain experts to discover the ASD for coaches, from a case study motivated by traffic managers. Datset and code are publicly available at: https://github.com/pangjunbiao/IPPs.

cs.LG↗

arXiv2024

Modeling the Popularity of Events on Web by Sparsity and Mutual-Excitation Guided Graph Neural Network

The content of a webpage described or posted an event in the cyberspace inevitably reflects viewpoints, values and trends of the physical society. Mapping an event on web to the popularity score plays a pivot role to sense the social trends from the cyberspace. However, the complex semantic correspondence between texts and images, as well as the implicit text-image-popularity mapping mechanics pose a significant challenge to this non-trivial task. In this paper, we address this problem from a viewpoint of understanding the interpretable mapping mechanics. Concretely, we organize the keywords from different events into an unified graph. The unified graph facilitates to model the popularity of events via two-level mappings, i.e., the self excitation and the mutual excitation. The self-excitation assumes that each keyword forms the popularity while the mutual-excitation models that two keywords would excite each other to determine the popularity of an event. Specifically, we use Graph Neural Network (GNN) as the backbone to model the self-excitation, the mutual excitation and the context of images into a sparse and deep factor model. Besides, to our best knowledge, we release a challenge web event dataset for the popularity prediction task. The experimental results on three public datasets demonstrate that our method achieves significant improvements and outperforms the state-of-the-art methods. Dataset is publicly available at: https://github.com/pangjunbiao/Hot-events-dataset.

cs.MM↗

arXiv2024

Bilateral Sharpness-Aware Minimization for Flatter Minima

Sharpness-Aware Minimization (SAM) enhances generalization by reducing a Max-Sharpness (MaxS). Despite the practical success, we empirically found that the MAxS behind SAM's generalization enhancements face the "Flatness Indicator Problem" (FIP), where SAM only considers the flatness in the direction of gradient ascent, resulting in a next minimization region that is not sufficiently flat. A better Flatness Indicator (FI) would bring a better generalization of neural networks. Because SAM is a greedy search method in nature. In this paper, we propose to utilize the difference between the training loss and the minimum loss over the neighborhood surrounding the current weight, which we denote as Min-Sharpness (MinS). By merging MaxS and MinS, we created a better FI that indicates a flatter direction during the optimization. Specially, we combine this FI with SAM into the proposed Bilateral SAM (BSAM) which finds a more flatter minimum than that of SAM. The theoretical analysis proves that BSAM converges to local minima. Extensive experiments demonstrate that BSAM offers superior generalization performance and robustness compared to vanilla SAM across various tasks, i.e., classification, transfer learning, human pose estimation, and network quantization. Code is publicly available at: https://github.com/ajiaaa/BSAM.

cs.CV↗

arXiv2024

Bundle Fragments into a Whole: Mining More Complete Clusters via Submodular Selection of Interesting webpages for Web Topic Detection

Organizing interesting webpages into hot topics is one of key steps to understand the trends of multimodal web data. A state-of-the-art solution is firstly to organize webpages into a large volume of multi-granularity topic candidates; hot topics are further identified by estimating their interestingness. However, these topic candidates contain a large number of fragments of hot topics due to both the inefficient feature representations and the unsupervised topic generation. This paper proposes a bundling-refining approach to mine more complete hot topics from fragments. Concretely, the bundling step organizes the fragment topics into coarse topics; next, the refining step proposes a submodular-based method to refine coarse topics in a scalable approach. The propose unconventional method is simple, yet powerful by leveraging submodular optimization, our approach outperforms the traditional ranking methods which involve the careful design and complex steps. Extensive experiments demonstrate that the proposed approach surpasses the state-of-the-art method (i.e., latent Poisson deconvolution Pang et al. (2016)) 20% accuracy and 10% one on two public data sets, respectively.

cs.IR↗

arXiv2024

Decorrelating Structure via Adapters Makes Ensemble Learning Practical for Semi-supervised Learning

In computer vision, traditional ensemble learning methods exhibit either a low training efficiency or the limited performance to enhance the reliability of deep neural networks. In this paper, we propose a lightweight, loss-function-free, and architecture-agnostic ensemble learning by the Decorrelating Structure via Adapters (DSA) for various visual tasks. Concretely, the proposed DSA leverages the structure-diverse adapters to decorrelate multiple prediction heads without any tailed regularization or loss. This allows DSA to be easily extensible to architecture-agnostic networks for a range of computer vision tasks. Importantly, the theoretically analysis shows that the proposed DSA has a lower bias and variance than that of the single head based method (which is adopted by most of the state of art approaches). Consequently, the DSA makes deep networks reliable and robust for the various real-world challenges, \textit{e.g.}, data corruption, and label noises. Extensive experiments combining the proposed method with FreeMatch achieved the accuracy improvements of 5.35% on CIFAR-10 dataset with 40 labeled data and 0.71% on CIFAR-100 dataset with 400 labeled data. Besides, combining the proposed method with DualPose achieved the improvements in the Percentage of Correct Keypoints (PCK) by 2.08% on the Sniffing dataset with 100 data (30 labeled data), 5.2% on the FLIC dataset with 100 data (including 50 labeled data), and 2.35% on the LSP dataset with 200 data (100 labeled data).

cs.CV↗

arXiv2024

Towards Scalable Topic Detection on Web via Simulating Levy Walks Nature of Topics in Similarity Space

Organizing a few webpages from social media websites into popular topics is one of the key steps to understand trends on web. Discovering popular topics from web faces a sea of noise webpages which never evolve into popular topics. In this paper, we discover that the similarity values between webpages in a popular topic contain the statistically similar features observed in Levy walks. Consequently, we present a simple, novel, yet very powerful Explore-Exploit (EE) approach to group topics by simulating Levy walks nature in the similarity space. The proposed EE-based topic clustering is an effective and effcient method which is a solid move towards handling a sea of noise webpages. Experiments on two public data sets demonstrate that our approach is not only comparable to the state-of-the-art methods in terms of effectiveness but also significantly outperforms the state-of-the-art methods in terms of efficiency.

cs.IR↗

arXiv2024

Modeling Multi-Granularity Context Information Flow for Pavement Crack Detection

Crack detection has become an indispensable, interesting yet challenging task in the computer vision community. Specially, pavement cracks have a highly complex spatial structure, a low contrasting background and a weak spatial continuity, posing a significant challenge to an effective crack detection method. In this paper, we address these problems from a view that utilizes contexts of the cracks and propose an end-to-end deep learning method to model the context information flow. To precisely localize crack from an image, it is critical to effectively extract and aggregate multi-granularity context, including the fine-grained local context around the cracks (in spatial-level) and the coarse-grained semantics (in segment-level). Concretely, in Convolutional Neural Network (CNN), low-level features extracted by the shallow layers represent the local information, while the deep layers extract the semantic features. Additionally, a second main insight in this work is that the semantic context should be an guidance to local context feature. By the above insights, the proposed method we first apply the dilated convolution as the backbone feature extractor to model local context, then we build a context guidance module to leverage semantic context to guide local feature extraction at multiple stages. To handle label alignment between stages, we apply the Multiple Instance Learning (MIL) strategy to align the high-level feature to the low-level ones in the stage-wise context flow. In addition, compared with these public crack datasets, to our best knowledge, we release the largest, most complex and most challenging Bitumen Pavement Crack (BPC) dataset. The experimental results on the three crack datasets demonstrate that the proposed method performs well and outperforms the current state-of-the-art methods.

cs.CV↗

arXiv2024

Finding A Taxi with Illegal Driver Substitution Activity via Behavior Modelings

In our urban life, Illegal Driver Substitution (IDS) activity for a taxi is a grave unlawful activity in the taxi industry, possibly causing severe traffic accidents and painful social repercussions. Currently, the IDS activity is manually supervised by law enforcers, i.e., law enforcers empirically choose a taxi and inspect it. The pressing problem of this scheme is the dilemma between the limited number of law-enforcers and the large volume of taxis. In this paper, motivated by this problem, we propose a computational method that helps law enforcers efficiently find the taxis which tend to have the IDS activity. Firstly, our method converts the identification of the IDS activity to a supervised learning task. Secondly, two kinds of taxi driver behaviors, i.e., the Sleeping Time and Location (STL) behavior and the Pick-Up (PU) behavior are proposed. Thirdly, the multiple scale pooling on self-similarity is proposed to encode the individual behaviors into the universal features for all taxis. Finally, a Multiple Component- Multiple Instance Learning (MC-MIL) method is proposed to handle the deficiency of the behavior features and to align the behavior features simultaneously. Extensive experiments on a real-world data set shows that the proposed behavior features have a good generalization ability across different classifiers, and the proposed MC-MIL method suppresses the baseline methods.

cs.CY↗

arXiv2024

Pixel-Wise Symbol Spotting via Progressive Points Location for Parsing CAD Images

Parsing Computer-Aided Design (CAD) drawings is a fundamental step for CAD revision, semantic-based management, and the generation of 3D prototypes in both the architecture and engineering industries. Labeling symbols from a CAD drawing is a challenging yet notorious task from a practical point of view. In this work, we propose to label and spot symbols from CAD images that are converted from CAD drawings. The advantage of spotting symbols from CAD images lies in the low requirement of labelers and the low-cost annotation. However, pixel-wise spotting symbols from CAD images is challenging work. We propose a pixel-wise point location via Progressive Gaussian Kernels (PGK) to balance between training efficiency and location accuracy. Besides, we introduce a local offset to the heatmap-based point location method. Based on the keypoints detection, we propose a symbol grouping method to redraw the rectangle symbols in CAD images. We have released a dataset containing CAD images of equipment rooms from telecommunication industrial CAD drawings. Extensive experiments on this real-world dataset show that the proposed method has good generalization ability.

cs.CV↗

arXiv2024

A Channel-ensemble Approach: Unbiased and Low-variance Pseudo-labels is Critical for Semi-supervised Classification

Semi-supervised learning (SSL) is a practical challenge in computer vision. Pseudo-label (PL) methods, e.g., FixMatch and FreeMatch, obtain the State Of The Art (SOTA) performances in SSL. These approaches employ a threshold-to-pseudo-label (T2L) process to generate PLs by truncating the confidence scores of unlabeled data predicted by the self-training method. However, self-trained models typically yield biased and high-variance predictions, especially in the scenarios when a little labeled data are supplied. To address this issue, we propose a lightweight channel-based ensemble method to effectively consolidate multiple inferior PLs into the theoretically guaranteed unbiased and low-variance one. Importantly, our approach can be readily extended to any SSL framework, such as FixMatch or FreeMatch. Experimental results demonstrate that our method significantly outperforms state-of-the-art techniques on CIFAR10/100 in terms of effectiveness and efficiency.

cs.CV↗