Search Results for author:
Found 20 papers, 5 papers with code
AutoPose: Searching Multi-Scale Branch Aggregation for Pose Estimation
Such simplification limits the fusion of information at different scales and fails to maintain high-resolution representations.
Graph Signal Processing for Geometric Data and Beyond: Theory and Applications
Geometric data acquired from real-world scenes, e. g., 2D depth images, 3D point clouds, and 4D dynamic point clouds, have found a wide range of applications including immersive telepresence, autonomous driving, surveillance, etc.
Single Image Deraining via Scale-space Invariant Attention Neural Network
Image enhancement from degradation of rainy artifacts plays a critical role in outdoor visual computing systems.
Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery
Recently, single gray/RGB image super-resolution reconstruction task has been extensively studied and made significant progress by leveraging the advanced machine learning techniques based on deep convolutional neural networks (DCNNs).
Rectified Meta-Learning from Noisy Labels for Robust Image-based Plant Disease Diagnosis
However, the performance of CNN-based classification approach depends on a large amount of high-quality manually labeled training data, which are inevitably introduced noise on labels in practice, leading to model overfitting and performance degradation.
Rapid Whole Slide Imaging via Learning-based Two-shot Virtual Autofocusing
Whole slide imaging (WSI) is an emerging technology for digital pathology.
ADRN: Attention-based Deep Residual Network for Hyperspectral Image Denoising
Hyperspectral image (HSI) denoising is of crucial importance for many subsequent applications, such as HSI classification and interpretation.
FasterSeg: Searching for Faster Real-time Semantic Segmentation
We present FasterSeg, an automatically designed semantic segmentation network with not only state-of-the-art performance but also faster speed than current methods.
Ranked #1 on
Semantic Segmentation
on BDD
Real Time Visual Tracking using Spatial-Aware Temporal Aggregation Network
Our tracker achieves leading performance in OTB2013, OTB2015, VOT2015, VOT2016 and LaSOT, and operates at a real-time speed of 26 FPS, which indicates our method is effective and practical.
Depth Restoration: A fast low-rank matrix completion via dual-graph regularization
As a real scenes sensing approach, depth information obtains the widespread applications.
Single Image Blind Deblurring Using Multi-Scale Latent Structure Prior
Blind image deblurring is a challenging problem in computer vision, which aims to restore both the blur kernel and the latent sharp image from only a blurry observation.
Hyperspectral Image Classification in the Presence of Noisy Labels
The key idea of RLPA is to exploit knowledge (e. g., the superpixel based spectral-spatial constraints) from the observed hyperspectral images and apply it to the process of label propagation.
Connecting Image Denoising and High-Level Vision Tasks via Deep Learning
Second we propose a deep neural network solution that cascades two modules for image denoising and various high-level tasks, respectively, and use the joint loss for updating only the denoising network via back-propagation.
Graph-Based Blind Image Deblurring From a Single Photograph
We leverage the new graph spectral interpretation for RGTV to design an efficient algorithm that solves for the skeleton image and the blur kernel alternately.
Blind Image Deblurring via Reweighted Graph Total Variation
The problem can be solved in two parts: i) estimate a blur kernel from the blurry image, and ii) given estimated blur kernel, de-convolve blurry input to restore the target image.
Mapping the world population one building at a time
Obtaining high accuracy in estimation of population distribution in rural areas remains a very challenging task due to the simultaneous requirements of sufficient sensitivity and resolution to detect very sparse populations through remote sensing as well as reliable performance at a global scale.
Robust Video Super-Resolution With Learned Temporal Dynamics
Second, we reduce the complexity of motion between neighboring frames using a spatial alignment network that is much more robust and efficient than competing alignment methods and can be jointly trained with the temporal adaptive network in an end-to-end manner.
Building Detection from Satellite Images on a Global Scale
Our work is some of the first to create population density maps from building detection on a large scale.
Look and Think Twice: Capturing Top-Down Visual Attention With Feedback Convolutional Neural Networks
While feedforward deep convolutional neural networks (CNNs) have been a great success in computer vision, it is important to remember that the human visual contex contains generally more feedback connections than foward connections.
Understanding Image Structure via Hierarchical Shape Parsing
A hierarchical shape parsing strategy is proposed to partition and organize image components into a hierarchical structure in the scale space.
