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Found 14 papers, 0 papers with code
Distributed Task-Oriented Communication Networks with Multimodal Semantic Relay and Edge Intelligence
In this article, we present a novel framework, named distributed task-oriented communication networks (DTCN), based on recent advances in multimodal semantic transmission and edge intelligence.
Attention-guided Multi-step Fusion: A Hierarchical Fusion Network for Multimodal Recommendation
The main idea of multimodal recommendation is the rational utilization of the item's multimodal information to improve the recommendation performance.
HGAN: Hierarchical Graph Alignment Network for Image-Text Retrieval
Then, a multi-granularity shared space is established with a designed Multi-granularity Feature Aggregation and Rearrangement (MFAR) module, which enhances the semantic corresponding relations between the local and global information, and obtains more accurate feature representations for the image and text modalities.
Task-Oriented Image Transmission for Scene Classification in Unmanned Aerial Systems
The vigorous developments of Internet of Things make it possible to extend its computing and storage capabilities to computing tasks in the aerial system with collaboration of cloud and edge, especially for artificial intelligence (AI) tasks based on deep learning (DL).
Joint Prediction and Time Estimation of COVID-19 Developing Severe Symptoms using Chest CT Scan
To our knowledge, this study is the first work to predict the disease progression and the conversion time, which could help clinicians to deal with the potential severe cases in time or even save the patients' lives.
Dual-Sampling Attention Network for Diagnosis of COVID-19 from Community Acquired Pneumonia
To this end, we develop a dual-sampling attention network to automatically diagnose COVID- 19 from the community acquired pneumonia (CAP) in chest computed tomography (CT).
Self-Selective Correlation Ship Tracking Method for Smart Ocean System
In recent years, with the development of the marine industry, navigation environment becomes more complicated.
Structured Bayesian Compression for Deep models in mobile enabled devices for connected healthcare
Deep Models, typically Deep neural networks, have millions of parameters, analyze medical data accurately, yet in a time-consuming method.
Super-Resolution of Brain MRI Images using Overcomplete Dictionaries and Nonlocal Similarity
The sparsity and self-similarity of the image blocks are taken as the constraints.
Interest-Related Item Similarity Model Based on Multimodal Data for Top-N Recommendation
Specifically, the Multimodal IRIS model consists of three modules, i. e., multimodal feature learning module, the Interest-Related Network (IRN) module and item similarity recommendation module.
Social Vehicle Swarms: A Novel Perspective on Social-aware Vehicular Communication Architecture
Internet of vehicles is a promising area related to D2D communication and internet of things.
Saliency deep embedding for aurora image search
In this paper, we aim to explore a new search method for images captured with circular fisheye lens, especially the aurora images.
FPAN: Fine-grained and Progressive Attention Localization Network for Data Retrieval
The Localization of the target object for data retrieval is a key issue in the Intelligent and Connected Transportation Systems (ICTS).
Training-Free Synthesized Face Sketch Recognition Using Image Quality Assessment Metrics
In this paper, we proposed a synthesized face sketch recognition framework based on full-reference image quality assessment metrics.
