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Deep learning-based low-light image enhancement (LLIE) is a task of leveraging deep neural networks to enhance the image illumination while keeping the image content unchanged. From the perspective of training data, existing methods complete the LLIE task ...

Ensuring product quality through automated anomaly detection is crucial in manufacturing. Traditional methods often struggle to capture both local and global features effectively, relying heavily on predefined templates that limit their ...

Weakly supervised object detection methods have achieved great success in natural scenes. However, they confront challenges such as small and weak appearances when detecting surface defects in industrial scenes. To address this problem, we propose ...

Predicting accurate future trajectories of agents is essential for autonomous navigation in complex scenarios. Although numerous work has made great progress on this goal, it is still challenging due to the uncertainty and continuity of behavioral ...

Numerous redundant and irrelevant features are usually contained in high-dimensional data whose presence, if ignored, can bring detrimental effects on the performance of data processing tasks. Unsupervised feature selection (UFS) as a trending ...

The challenge of non-line-of-sight (NLOS) imaging lies in the multiple reflections of light paths, causing a significant drop in signal-to-noise ratio. Visible light is easily affected by illumination conditions, making passive NLOS reconstruction ...

With the rapid development of wireless networks and smart devices, spatial crowdsourcing (SC) has become increasingly prevalent. The key issue in SC is efficiently assigning spatial tasks, such as parcel and food delivery, to mobile workers in order to ...

Few-shot detection is a major task in pattern recognition which seeks to localize objects using models trained with few labeled data. One of the mainstream few-shot methods is transfer learning which consists in pretraining a detection model in a ...

Monocular 3D lane detection plays a crucial role in autonomous driving, assisting vehicles in safe navigation. Existing methods primarily utilize calibrated camera parameters in the dataset to conduct 3D lane detection from a single image. However, errors ...

Defect detection plays an important role in the industrial production and manufacturing sectors, directly impacting product quality and production costs. Most defect detection methods relying on RGB images often encounter challenges such as ...

Non-line-of-sight (NLOS) pedestrian detection is vital for uncovering hidden pedestrians and is crucial for autonomous driving and emergency response. Traditional methods depend on high-speed lasers and precise photon detection devices, which are ...

High-quality pixel-level annotations are crucial for semantic segmentation tasks. It is extremely time-consuming and laborious to manually label a large number of samples. DiffuMask provides a feasible method to generate synthetic image-mask ...

Rigid and articulated objects are common in our daily lives. Pose estimation tasks for both types of objects have been extensively studied within their respective domains. However, a universal framework capable of estimating the pose of both rigid ...

Distant supervised relation extraction has been increasingly popular in recent years, which generates datasets automatically without human intervention. However, the distant supervised assumption has the limitation that the generated datasets have ...

The objective of sound event localization and detection (SELD) is to accurately identify the temporal occurrence and spatial coordinates of a specific sound category. The existing mainstream offline methods may unintentionally introduce ...

Hand gesture recognition (HGR) plays a pivotal role in natural and intuitive human-computer interactions. Recent HGR methods focus on recognizing gestures from vision-based images or videos. However, vision-based methods are limited in recognizing micro ...

Mainstream image captioning models are usually two-stage captioners, i.e., encoding the region features by a pre-trained detector and then feeding them into a language model to generate the captions. However, such a two-stage procedure will lead to a ...

Traffic intersections are important scenes that can be seen almost everywhere in the traffic system. Currently, most simulation methods perform well at highways and urban traffic networks. In intersection scenarios, the challenge lies in the lack of ...

The electrocardiogram (ECG) signal comprising P-, Q-, R-, S-, and T-waves is an indispensable noninvasive diagnostic tool for analyzing physiological conditions of the heart. In general, traditional ECG intelligent diagnosis methods gradually ...

The adoption of unmanned vehicles in urban logistics has gradually become a trend. It can effectively lower carbon emissions, reduce labor costs, and improve logistics efficiency. In this paper, we investigate a novel problem of unmanned vehicle ...