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Unsupervised Abnormal Stop Detection for Long Distance Coaches with Low-Frequency GPS
Jiaxin Deng, Junbiao Pang, Jiayu Xu +1
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…
Bilateral Sharpness-Aware Minimization for Flatter Minima
Jiaxin Deng, Junbiao Pang, Baochang Zhang +1
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 genera…
Asymptotic Unbiased Sample Sampling to Speed Up Sharpness-Aware Minimization
Jiaxin Deng, Junbiao Pang, Baochang Zhang
Sharpness-Aware Minimization (SAM) has emerged as a promising approach for effectively reducing the generalization error. However, SAM incurs twice the computational cost compared…
Modeling Multi-Granularity Context Information Flow for Pavement Crack Detection
Junbiao Pang, Baocheng Xiong, Jiaqi Wu
Crack detection has become an indispensable, interesting yet challenging task in the computer vision community. Specially, pavement cracks have a highly complex spatial structure,…
Pixel-Wise Symbol Spotting via Progressive Points Location for Parsing CAD Images
Junbiao Pang, Zailin Dong, Jiaxin Deng +2
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 eng…
A Channel-ensemble Approach: Unbiased and Low-variance Pseudo-labels is Critical for Semi-supervised Classification
Jiaqi Wu, Junbiao Pang, Baochang Zhang +1
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…
Effective Gradient Sample Size via Variation Estimation for Accelerating Sharpness aware Minimization
Jiaxin Deng, Junbiao Pang, Baochang Zhang +1
Sharpness-aware Minimization (SAM) has been proposed recently to improve model generalization ability. However, SAM calculates the gradient twice in each optimization step, thereby…
In-Distribution Consistency Regularization Improves the Generalization of Quantization-Aware Training
Junbiao Pang, Tianyang Cai, Baochang Zhang +1
Although existing Quantization-Aware Training (QAT) methods intensively depend on knowledge distillation to guarantee performance, QAT still suffers from severe performance drop. T…