Aiming at the problem that the attention-mechanism-based image super-resolution reconstruction network ignores the heterogeneity of attentional features and treats features at different levels uniformly by directly incorporating the attention mechanism into the network model. This study designs a no...

Due to different imaging mechanisms, infrared imaging can capture target information under special conditions where the targetis obstructed, while visible light imaging can capture the texture details of the scenarios. Therefore, to obtain a fusion image containing both target information and textur...

To explore 3D space, mobile robots need to obtain a large amount of scene information, which includes semantic, instance objects, and positional relationships. The accuracy and computational complexity of scene analysis are the two focuses of mobile terminals. Therefore, a spatial information-enhanc...

A novel multi-frame self-supervised single-image depth estimation technique based on coordinate-aware attention has been presented to tackle the issue of hazy depth prediction near object edges in single-image depth estimation methods. Firstly, a coordinate-aware attention module is proposed to enha...

A recursive gated enhancement and pyramid prediction railway panoramic segmentation network is proposed to address the issues of insufficient target feature extraction and blurred edge contour segmentation in high-speed railway scene panoramic segmentation. On the basis of the DETR panoramic segment...

Aiming at the problems of artificially selecting the threshold and poor determination performance in the existing object drift determination criteria, this paper proposes a cascaded object drift determination network with adaptive threshold selection. Firstly, through the cascade design of the two s...

A semi-supervised video object segmentation (VOS) method was proposed to address the issues of increasing memory consumption during inference and the difficulty of training relying solely on low-level pixel features. The method is based on dynamic embedding features and an auxiliary loss function. F...

The image and text sentiment in social media is an important factor affecting public opinion and is receiving increasing attention in the field of natural language processing (NLP). Currently, the analysis of image and text sentiment in social media has mainly focused on single image and text pairs,...

Multi-label image classification aims to predict a set of labels for a given input image. Existing studies based on semantic information either use the correlation between semantic and visual space to guide the feature extraction process to generate effective feature representations or use the corre...

Insufficient target feature extraction and target occlusion situations frequently occur in single-stage multipal object tracking（MOT） algorithms, resulting in a large number of identity switches and degraded tracking performance. A multi-target tracking algorithm based on dual-branch feature enhance...

Due to the significant correlation between neighboring slices in two-dimensional (2D) multi-slice magnetic resonance data, higher quality slice pictures can be reconstructed by taking use of the redundancy between slices. However, 2D multi-slice magnetic resonance imaging requires an amount of time....

Image sentiment analysis aims to analyze the emotions conveyed by visual content. A key challenge in this field is to bridge the affective gap between latent visual features and abstract emotions. Existing deep learning models attempt to address this issue by directly learning discriminative high-le...

Emergency surveillance video transmission is a key technical means to improve emergency handling capability under circumstances such as emergency monitoring, public security incident handling, and post-disaster reconstruction. It has gradually become a key focus of research and development in the co...

In the realm of intelligent agent path planning, the ant colony algorithm stands as a prominent path-solving strategy that has garnered extensive adoption. However, conventional ant colony algorithms exhibit issues pertaining to local optima and excessive inflection points. The adaptive polymorphic ...

This paper presents a few-shot object detection of aerial images based on language guidance vision in response to the problem of decreased detection accuracy in existing aerial image object detection brought on by a lack of training data and changes in the aerial image dataset, such as changes in sh...

To further solve the problems of object omission and repeated detection and improve the accuracy of object detection, this paper proposes an object detection algorithm based on feature enhancement and adaptive threshold non-maximum suppression(NMS). The attention-guided multi-scale context module(AM...

Aiming at the problem of insufficient population learning and adaptability of the Harris hawks optimization (HHO) algorithm, this paper proposes a learning Harris hawks optimization based on the signal-to-noise ratio（SLHHO）algorithm. By using the signal-to-noise ratio as a metric to assess individua...

For distributed video compressive sensing, the inter-frame multi-hypothesis prediction offers low computational complexity at the encoding end and good restoration quality for non-key frames at the decoding end. In recent years, many optimization algorithms related to it have been proposed. However,...

The weakly-supervised video moment retrieval (WSVMR) task retrieves in and out points of specific events from untrimmed videos based on natural language query text, using a deep learning algorithm model trained through video and natural language text matching relationships. Most existing WSVMR algor...

A lightweight neural network design method is proposed to efficiently represent small ships in infrared remote sensing images. To improve the representation effect of infrared dim and small targets, a method for simulating the visual receptive field adjustment mechanism that incorporates multi-scale...

Existing omnidirectional image quality assessment (OIQA) models extract local features from each viewport independently, increasing computational complexity and making it difficult to describe the correlations between viewports using an end-to-end fusion model. To solve these issues, a quality asses...

Exploring the dependency relationships of multi-source remote sensing image data features to leverage the complementary advantages between different modalities has become a prominent research direction in the field of remote sensing. Existing joint classification tasks of hyperspectral and synthetic...

Object tracking in unmanned aerial vehicle (UAV) scenarios has been widely applied in many real-world tasks. Different from general object tracking, UAV object tracking is more easily affected by complex environmental interferences and computational limitations. In this paper, a saliency-aware tripl...

Traditional image stitching approaches predominantly depend on accurate feature localization and distribution, which leads to suboptimal robustness in intricate aerial photography contexts. Consequently, a comprehensive unsupervised deep learning framework for aerial image stitching was devised, enc...

Although text sentiment analyses using dictionaries are efficient and unsupervised, their accuracy relies heavily on the dictionary quality. The quality of existing Chinese sentiment dictionaries in cross-domain applications needs to be improved, as the manually constructed Chinese dictionaries fail...

Continual learning has become a new research hotspot in recent years. However, in the continual learning of multimodal architecture, the data are generally not fully utilized, resulting in catastrophic forgetting and learning obstruction. To address these issues, a multimodal continual learning meth...

Batch image steganography provides an effective means for covert communication on social networks by embedding secret messages into multiple cover images through cover selection. Compared with traditional image steganography, a key challenge of batch image steganography lies in designing an effectiv...

Current unsupervised domain adaption algorithms usually consider only single-label learning, failing to adapt to multi-label classification tasks in pedestrian attribute recognition. To address this issue, a multi-label adversarial domain adaptation algorithm was proposed for pedestrian attribute re...

Image style transfer aims to adjust the visual properties of a content image based on a style reference image, preserving the original content while presenting specific styles to generate visually appealing stylized images. Most existing representative methods focus on extracting local image feature...

Current synthetic aperture radar (SAR) ship detection methods primarily encounter two challenges: 1) the variability of target sizes and the abundance of interfering factors; 2) multiple orientations of targets and a limited quantity of training samples. To address these issues, this paper introduce...

This paper proposes an improved YOLOv7-based aerial small target detection method to address the high rates of missed and false detections in current detection technologies for aerial small target detection tasks. First, a CBAM fusion attention mechanism is incorporated into the backbone network, al...

Many multi-object pedestrian tracking algorithms have been proposed in computer vision, and great progress has been made in tracking efficiency and accuracy recently. Practical applications are severely hampered by the fact that the majority of tracking techniques now in use are still unable to addr...

Currently, most of the image retrieval methods based on deep learning are supervised techniques, which require massive labeled data. However, it is very difficult and expensive to label so much data in real applications. Furthermore, the network learned picture similarity poorly since the triple los...

In the age of intelligent information, managing the recommendations of Internet information service algorithms is a crucial step in creating a national Internet governance framework. Personalized recommendation algorithm is one of the important technologies for Internet information service algorithm...

Camouflaged object identification is a new visual detection job that has many applications in several fields. Its goal is to identify camouflaged targets that are completely disguised in their environment. To address the problem of current camouflaged object detection algorithms failing to accuratel...

Visual simultaneous localization and mapping (VSLAM) is a technology that utilizes visual and other sensory sensors to acquire information about unknown environments. It is widely applied in fields such as autonomous driving, robotics, augmented reality, and more. However, pixel-level semantic segme...