Search Results for author:
Found 14 papers, 1 papers with code
Master-Auxiliary: an efficient aggregation strategy for video anomaly detection
First, the aggregation strategy chooses one detector as master detector by experience, and sets the remaining detectors as auxiliary detectors.
MRI Reconstruction with Interpretable Pixel-Wise Operations Using Reinforcement Learning
In this work, we propose a deep reinforcement learning based method to reconstruct the corrupted images with meaningful pixel-wise operations (e. g. edge enhancing filters), so that the reconstruction process is transparent to users.
A Promotion Method for Generation Error Based Video Anomaly Detection
To address these problems, we propose a promotion method: utilize the maximum of block-level GEs on the frame to detect anomaly.
Real-Time Steganalysis for Stream Media Based on Multi-channel Convolutional Sliding Windows
To solve these two challenges, in this paper, combined with the sliding window detection algorithm and Convolution Neural Network we propose a real-time VoIP steganalysis method which based on multi-channel convolution sliding windows.
A Wasserstein GAN model with the total variational regularization
It is well known that the generative adversarial nets (GANs) are remarkably difficult to train.
Rendering Portraitures from Monocular Camera and Beyond
Specifically, we first exploit Convolutional Neural Networks to estimate the relative depth and portrait segmentation maps from a single input image.
An Image dehazing approach based on the airlight field estimation
This paper proposes a scheme for single image haze removal based on the airlight field (ALF) estimation.
Learning to Super-Resolve Blurry Face and Text Images
We present an algorithm to directly restore a clear high-resolution image from a blurry low-resolution input.
Image Captioning with Object Detection and Localization
Automatically generating a natural language description of an image is a task close to the heart of image understanding.
A Deep and Autoregressive Approach for Topic Modeling of Multimodal Data
Second, we propose a deep extension of our model and provide an efficient way of training the deep model.
Image Tag Completion by Low-rank Factorization with Dual Reconstruction Structure Preserved
A novel tag completion algorithm is proposed in this paper, which is designed with the following features: 1) Low-rank and error s-parsity: the incomplete initial tagging matrix D is decomposed into the complete tagging matrix A and a sparse error matrix E. However, instead of minimizing its nuclear norm, A is further factor-ized into a basis matrix U and a sparse coefficient matrix V, i. e. D=UV+E.
Image retrieval with hierarchical matching pursuit
A novel representation of images for image retrieval is introduced in this paper, by using a new type of feature with remarkable discriminative power.
Topic Modeling of Multimodal Data: An Autoregressive Approach
Topic modeling based on latent Dirichlet allocation (LDA) has been a framework of choice to deal with multimodal data, such as in image annotation tasks.
A Supervised Neural Autoregressive Topic Model for Simultaneous Image Classification and Annotation
Topic modeling based on latent Dirichlet allocation (LDA) has been a framework of choice to perform scene recognition and annotation.
