Using a deep learning method to classify a large amount of labelled remote sensing scene data produces good performance. However, it is challenging for deep learning based methods to generalise to classification tasks with limited data. Few‐shot ...

The authors present a new framework, SRL‐ProtoNet, for remote sensing scene few‐shot classification. The self‐supervised contrastive learning is introduced to encourage inter‐class contrastive property and the Pre‐prototype and ProtoMixer is proposed for ...

Boundary detection, as a fundamental task for computer vision applications, plays an important role in many tasks such as image deblurring, semantic segmentation, camouflaged object detection, and salient object detection. The thickness problem ...

Multi-domain recommendation and multi-task recommendation have demonstrated their effectiveness in leveraging common information from different domains and objectives for comprehensive user modeling. Nonetheless, the practical recommendation usually ...

Since the birth of drone technology in the 1920s, its aerospace value, measurement value, and military value have been fully recognized. This paper presents a comprehensive study on the application of drones in disaster management, with a particular ...

In the era of information explosion, spatio-temporal data mining serves as a critical part of urban management. Considering the various fields demanding attention, e.g., traffic state, human activity, and social event, predicting multiple spatio-temporal ...

Traffic prediction is a typical spatio-temporal data mining task and has great significance to the public transportation system. Considering the demand for its grand application, we recognize key factors for an ideal spatio-temporal prediction method: ...

With the acceleration of urbanization, traffic forecasting has become an essential role in smart city construction. In the context of spatio-temporal prediction, the key lies in how to model the dependencies of sensors. However, existing works basically ...

Semi-supervised semantic segmentation methods are the main solution to alleviate the problem of high annotation consumption in semantic segmentation. However, the class imbalance problem makes the model favor the head classes with sufficient training ...

Due to the large computational and GPUs memory cost of semantic segmentation, some works focus on designing a lite weight model to achieve a good trade‐off between computational cost and accuracy. A common method is to combined CNN and vision ...

We propose AG Self‐Attention, Enhanced Atrous Self‐Attention, and Gate Attention to fix the ignore of multi fields context and inject detailed information. image image

As a critical component of ensuring the safe and stable operation of trains, the detection of bird’s nests on the rail catenary has always been essential. Low-resolution images and the lack of labelled data, however, make it difficult to detect ...

Multimodal sentiment analysis aims to predict the overall sentiment polarity from multimodal signals, an essential task for many applications. A central part of this task is designing a suitable fusion model to integrate heterogeneous information from ...

In this article, we propose a comprehensive approach to a responsible artificial intelligence (AI)-based software architecture for the digitalization of industry drawings, serving as a software engineering reference for responsible AI in other industry ...

Recently, with the development of deep‐learning, the performance of multiple object tracking (MOT) algorithm based on deep neural networks has been greatly improved. However, it is still a difficult problem to successfully solve the tracking ...

This paper mainly introduces a data association method based on quadratic graph matching. We construct the objects in each frame as a graph. In addition to using vertex information, we also consider using edge information. That is, using structural ...

Edge detection algorithms are beneficial to the implementation of upstream tasks. The algorithms used to treat all edges equally, but edges in edge detection can be classified into four types according to the discontinuity as reflectance, ...

Spatio-temporal prediction plays a critical role in smart city construction. Jointly modeling multiple spatio-temporal tasks can further promote an intelligent city life by integrating their inseparable relationship. However, existing studies fail to ...

Photovoltaic power generation has significant energy, environmental protection and economic benefits. With the global attention to green energy, the development of photovoltaic power generation has become an inevitable trend. Photovoltaic panel ...

Multimodal sentiment analysis aims to predict sentiment polarity from several modalities, which is an essential task for widespread applications. The core part of this task is to design a suitable fusion schema to integrate the heterogeneous ...

Offline user identification is a scenario that users use their bio-information like faces as identification in offline venues, which has been applied in many offline scenarios such as verification in banks, check-in in hotels and making a purchase in ...

The wide application of the Internet of Things (IoT), large-scale satellite clusters and drone clusters requires multiple access capabilities for massive devices. In order to meet the high-capacity and low-latency access requirements in ...

• We propose a novel SRKD framework to solve the low-resolution classification problem.

With the development of deep convolutional neural networks, the high-resolution image classification has achieved excellent classification results. However, in natural scenes, low-resolution images are very common, such as images taken ...