Search Results for author:
Found 12 papers, 5 papers with code
A new communication paradigm: from bit accuracy to semantic fidelity
In this article, we deploy semantics to solve the spectrum and power bottleneck and propose a first understanding and then transmission framework with high semantic fidelity.
Networking and Internet Architecture
Temporal Graph Modeling for Skeleton-based Action Recognition
Specifically, the constructed temporal relation graph explicitly builds connections between semantically related temporal features to model temporal relations between both adjacent and non-adjacent time steps.
Knowledge-guided Semantic Computing Network
Besides, to enhance the recognition ability of the semantic tree in aspects of the diversity, randomicity and variability, we use the traditional neural network to aid the semantic tree to learn some indescribable features.
Perceptual Compressive Sensing
In detail, we employ perceptual loss, defined on feature level, to enhance the structure information of the recovered images.
Full Image Recover for Block-Based Compressive Sensing
In measurement part, the input image is adaptively measured block by block to acquire a group of measurements.
ConvCSNet: A Convolutional Compressive Sensing Framework Based on Deep Learning
Instead of reconstructing individual blocks, the whole image is reconstructed from the linear convolutional measurements.
Fully Convolutional Measurement Network for Compressive Sensing Image Reconstruction
Recently, deep learning methods have made a significant improvement in compressive sensing image reconstruction task.
Real-Time Illegal Parking Detection System Based on Deep Learning
Experiments show that the system can achieve a 99% accuracy and real-time (25FPS) detection with strong robustness in complex environments.
Adaptive Measurement Network for CS Image Reconstruction
In this paper, we propose an adaptive measurement network in which measurement is obtained by learning.
Feature-Fused SSD: Fast Detection for Small Objects
We propose a multi-level feature fusion method for introducing contextual information in SSD, in order to improve the accuracy for small objects.
Learning Parametric Sparse Models for Image Super-Resolution
More specifically, the parametric sparse prior of the desirable high-resolution (HR) image patches are learned from both the input low-resolution (LR) image and a training image dataset.
Learning Parametric Distributions for Image Super-Resolution: Where Patch Matching Meets Sparse Coding
Existing approaches toward Image super-resolution (SR) is often either data-driven (e. g., based on internet-scale matching and web image retrieval) or model-based (e. g., formulated as an Maximizing a Posterior estimation problem).
