33,830 Result(s)
within
**hu Li
Artificial Intelligence
-
Article
Cross-Architecture Knowledge Distillation
The Transformer network architecture has gained attention due to its ability to learn global relations and its superior performance. To boost performance, it is natural to distill complementary knowledge from ...
-
Article
Correction to: Fast aggregation method of WSNs dynamic data based on micro-cluster evolutionary learning
-
Article
SplatFlow: Learning Multi-frame Optical Flow via Splatting
The occlusion problem remains a crucial challenge in optical flow estimation (OFE). Despite the recent significant progress brought about by deep learning, most existing deep learning OFE methods still struggl...
-
Article
ConDA: state-based data augmentation for context-dependent text-to-SQL
The context-dependent text-to-SQL task has profound real-world implications, as it facilitates users in extracting knowledge from vast databases, which allows users to acquire the information interactively for...
-
Article
BPSO-SLM: a binary particle swarm optimization-based self-labeled method for semi-supervised classification
The self-labeled methods have been favored by scholars in semi-supervised classification. Mislabeling is a great challenge for self-labeled methods and one of the reasons for mislabeling is that high-confidenc...
-
Article
Cross-Modal Fusion and Progressive Decoding Network for RGB-D Salient Object Detection
Most existing RGB-D salient object detection (SOD) methods tend to achieve higher performance by integrating additional modules, such as feature enhancement and edge generation. There is no doubt that these mo...
-
Article
Dual flow fusion graph convolutional network for traffic flow prediction
In recent decades, motor vehicle ownership has increased worldwide year by year, which causes that the accurate prediction of traffic flow on urban road networks becomes more important. However, the dual depen...
-
Article
TAENet: transencoder-based all-in-one image enhancement with depth awareness
Recently, CNN-based all-in-one image enhancement methods have been proposed to solve multiple image degradation tasks. However, these CNN-based methods usually have two limitations. One limitation is that they...
-
Article
Hugs Bring Double Benefits: Unsupervised Cross-Modal Hashing with Multi-granularity Aligned Transformers
Unsupervised cross-modal hashing (UCMH) has been commonly explored to support large-scale cross-modal retrieval of unlabeled data. Despite promising progress, most existing approaches are developed on convolut...
-
Article
Open AccessDomain Generalization with Small Data
In this work, we propose to tackle the problem of domain generalization in the context of insufficient samples. Instead of extracting latent feature embeddings based on deterministic models, we propose to learn a...
-
Article
Open AccessEvent-Based Non-rigid Reconstruction of Low-Rank Parametrized Deformations from Contours
Visual reconstruction of fast non-rigid object deformations over time is a challenge for conventional frame-based cameras. In recent years, event cameras have gained significant attention due to their bio-insp...
-
Article
Softmax-Free Linear Transformers
Vision transformers (ViTs) have pushed the state-of-the-art for visual perception tasks. The self-attention mechanism underpinning the strength of ViTs has a quadratic complexity in both computation and memory...
-
Article
Fast aggregation method of WSNs dynamic data based on micro-cluster evolutionary learning
In order to completely restore WSNs data and improve the quality of data aggregation, a fast aggregation method of WSNs dynamic data based on micro-cluster evolutionary learning is proposed. The wireless senso...
-
Article
Deep Learning Technique for Human Parsing: A Survey and Outlook
Human parsing aims to partition humans in image or video into multiple pixel-level semantic parts. In the last decade, it has gained significantly increased interest in the computer vision community and has be...
-
Article
Open AccessMDGCL: Graph Contrastive Learning Framework with Multiple Graph Diffusion Methods
In recent years, some classical graph contrastive learning(GCL) frameworks have been proposed to address the problem of sparse labeling of graph data in the real world. However, in node classification tasks, t...
-
Article
Pricing of shout option in uncertain financial market
The shout option allows the investors to make "shouts" to the seller throughout the option’s duration. The investors’ payoff is higher between the intrinsic value at shout time and the intrinsic value at the m...
-
Article
DGNN-MN: Dynamic Graph Neural Network via memory regenerate and neighbor propagation
Dynamic Graph Neural Network (DGNN) models have been widely used for modelling, prediction and recommendation tasks in domains such as e-commerce and social networks, due to their ability to capture node inter...
-
Article
Achieving accurate and balanced regional electric vehicle charging load forecasting with a dynamic road network: a case study of Lanzhou City
Spatial and temporal predictions of electric vehicle (EV) charging loads provide a basis for further research on synergistic operation of road-vehicle-electricity networks with different attributes, which is i...
-
Article
On weighted threshold moment estimation of uncertain differential equations with applications in interbank rates analysis
Uncertainty theory is a branch of mathematics for modeling belief degrees. Within the framework of uncertainty theory, uncertain variable is used to represent quantities with uncertainty, and uncertain process...
-
Article
Vector-based uncertain ordered density weighted averaging: a family of incentive-oriented aggregation operators
Incentive is a common phenomenon in the process of decision management. It is important and necessary to integrate incentive requirement into the decision-making process. To this problem, the paper proposed a ...
