In this study, we investigate the task of active testing for label-efficient evaluation, which aims to estimate a model’s performance on an unlabeled test dataset with a limited annotation budget. Previous approaches relied on deep ensemble models ...$\mathrm{}$

In this demonstration, we present VCR, an automated slice discovery method (SDM) for object detection models that helps practitioners identify and explain specific scenarios in which their models exhibit systematic errors. VCR leverages the capabilities ...

In this paper, according to the element discretization characteristic of the geometric model, a complex function theory based transformation boundary element method is proposed to perform the steady-state thermal analysis of plate structures. The ...

As machine learning (ML) gains wider adoption in real-world applications, the validation of ML models becomes fundamental for its productization, particularly in safety-critical applications. Recently, data slice finding has emerged as a popular method ...

Fatigue driving is one of the primary causative factors of road accidents. It is of great significance to discern, identify and warn drivers in time for traffic safety and reduce traffic accidents. In this paper, a systematic review for the fatigue ...

Detecting out-of-distribution (OOD) data is crucial for ensuring the safe deployment of machine learning models in real-world applications. However, existing OOD detection approaches primarily rely on the feature maps or the full gradient space ...

In this study, we investigate the task of data pre-selection, which aims to select instances for labeling from an unlabeled dataset through a single pass, thereby optimizing performance for undefined downstream tasks with a limited annotation budget. ...

Open-world object detection (OWOD) is an emerging computer vision problem that involves not only the identification of predefined object classes, like what general object detectors do, but also detects new unknown objects simultaneously. Recently, several ...

• An improved beetle antennae search algorithm (IBAS) is proposed.
• Fifteen ...

The beetle antennae search (BAS) algorithm is a single-solution metaheuristic optimizer that imitates the foraging behavior of beetles. Due to its easy implementation and fast convergence, it has been applied in various engineering ...

Machine vision is being employed in defect detection, size measurement, pattern recognition, image fusion, target tracking and 3D reconstruction. Traditional cancer detection methods are dominated by manual detection, which wastes time ...

• A review of machine vision in cancer cell detection methods.
• Analyze the main ...

Semantic segmentation is a critical component in autonomous driving and has to be thoroughly evaluated due to safety concerns. Deep neural network (DNN) based semantic segmentation models are widely used in autonomous driving. However, it is challenging ...

We present the Feature Tracking Kit (FTK), a framework that simplifies, scales, and delivers various feature-tracking algorithms for scientific data. The key of FTK is our simplicial spacetime meshing scheme that generalizes both regular and unstructured ...

Multi-set query is a fundamental problem in computer systems and applications. Most traditional solutions for multi-set query are based on hash tables or bloom filters. However, when the sizes of multi-sets are large, these solutions cannot achieve ...

Ensemble simulations are becoming prevalent in various scientific and engineering disciplines, such as computational fluid dynamics, aerodynamics, climate, and weather research. Scientists routinely conduct a set of simulations with different ...

The objective of this paper is to understand transport behavior in uncertain time-varying flow fields by redefining the finite-time Lyapunov exponent (FTLE) and Lagrangian coherent structure (LCS) as stochastic counterparts of their traditional ...

This paper presents a method of improving fuzzy k-nearest neighbor (k-NN) using genetic algorithm (GA). k-NN is an important classification algorithm, However, a major drawback of the method is that each of the patterns of known classification is ...

To consider spatial information in spatial clustering, the Neighborhood Expectation-Maximization (NEM) algorithm incorporates a spatial penalty term in the objective function. Such an addition leads to multiple iterations in the E-step. Besides, the ...