The existing optical remote sensing image super-resolution reconstruction method is mainly to generate visually satisfactory images, and does not take into account the particularity of the subsequent target detection task, so it cannot be effectively applied to target detection. Therefore, a double ...

The deep Q-learning network (DQN) has become an effective method to solve the traffic signal timing problem because of its strong perception and decision-making ability. However, in the field of traffic signal timing systems, the problem of parameter uncertainty caused by external environment distur...

Images obtained in foggy conditions often suffer from low contrast, color loss, and noise. At present, many traditional dehazing methods mainly focus on solving problems such as low contrast and color loss, but do not consider the hidden noise light scattered by dust particles in the air, resulting ...

During the predictive coding of quantized block compressive sensing, a large quantity of inefficient candidates will lead to low rate-distortion performance. To efficiently reduce the encoding distortion, this paper proposes a region-hierarchical predictive coding method for quantized block compress...

To resolve the conflict between the increasing amount of video data and the demand for high-quality video experience, HEVC has boosted the compression performance by 50% based on dramatically increased complexity of H.264/AVC. In this paper, a fast coding unit (CU) partition algorithm is proposed to...

The tracking method based on the Siamese network trains the tracking model offline. Therefore, it maintains a good balance between tracking accuracy and speed, which attracts the interest of a growing number of researchers recently. The existing Siamese network object tracking method uses a fixed th...

Image captioning takes image as input and outputs a text sequence. Nowadays, most images included in image captioning datasets are captured from daily life of internet users. Captions of these images are consequently composed of a few common words and many rare words. Most existing studies focus on ...

U-Net is currently the most widely used segmentation model, and its "coding-decoding" structure has also become the most commonly used structure for building medical image segmentation models. Although U-Net has achieved very high segmentation accuracy in many fields, but there are problems such as ...

Image quality assessment is a basic and challenging problem in the field of image processing, among which the contrast distortion has a greater impact on the perception of image quality. However, there is relatively little research on the no-reference image quality assessment of contrast-distorted i...

Knowledge graph (KG) completion aims to predict missing links based on the known triples in a knowledge base. Since most KG completion methods dealt with triples independently without capture the heterogeneous structure of KG and the rich information that was inherent the in neighbor nodes, which re...

The image captioning task requires the machine to automatically generate natural language text to describe the semantic content of the image, thus transforming visual information into textual descriptions that facilitate image management, retrieval, classification, and other tasks. Image difference ...

Spectral clustering is widely used in data mining and pattern recognition. However, due to the high computational cost of eigenvector solutions and the huge memory requirements brought by big data, spectral clustering algorithm is greatly limited when it is applied to large-scale data. Therefore, th...

Aiming at the problem that the traditional crowd density estimation methods are not applicable under the distortion of fisheye images, this paper presents a crowd density estimation method for fisheye images, which realizes the monitoring of human traffic in scene of using fisheye lens. For model st...

Change detection (CD) is an important task of remote sensing, always facing many pseudo changes and large scale variations. However, existing methods mainly focus on modeling difference features and neglect extracting sufficient information from the original images, which affects feature discriminat...

Smoke has the characteristics of semi-transparency, irregularity and blurry boundaries, leading to the challenging task of image smoke segmentation. To solve these problems, we propose an attention modeling method to extract the correlation of long-distance information. The attention method can capt...

Hypernymy is the foundation of many downstream tasks in natural language processing (NLP), so hypernymy detection has received considerable attention in the field of NLP. Adopting random initialization word vectors, existing word embedding methods cannot well capture the asymmetry and transferabilit...

3D object detection is an important scene understanding task in computer vision and autonomous driving. However, most of these methods do not fully consider the large differences in scales between multiple objects. Thus, objects with a small scale are easily ignored, resulting in low detection accur...

The continuous spread of the COVID-19 has brought profound impacts on human society. For the prevention and control of virus spreading, it is critical to predict the future trend of epidemic situation. Existing studies on COVID-19 spread prediction, based on classic SEIR models or naive time-series ...

Hyperspectral images contain rich and valuable spectral information, which brings great challenges to storage and transmission. However, most current hyperspectral image compression methods cannot consider spatial and spectral redundancy simultaneously, resulting in limited compression performance. ...

Underwater object recognition and segmentation with high accuracy have become a challenge with the development of underwater object grabbing technology. The existing underwater object detection technology can only give the general position of an object, unable to give more detailed information such ...

On the basis of DaSiamese-RPN, a target tracking approach of appearance and action adaptation is proposed to limit the effect of appearance deformation on target tracking when the target is moving.First of all, the appearance and action adaptive module is introduced in the subnet of the Siamese netw...

Cross-domain was an important application scenario in person re-identification, but the apparent difference of person image in illumination condition, shooting angle, imaging background and style between the source domain and target domain was the most important factor that leads to the decline of t...

Objective In soccer matches, player data analysis is crucial to improve the viewing experience for viewers and to aid coaches in performance evaluation. The difficulty of player data analysis is how to locate the coordinates of players on the soccer field, i.e., how to determine the mapping relation...