23 citations · 39 across the 9 of their papers we have counts for
10 papers
Hausdorff Dimension and Lebesgue Measure of Codiagonal of Embedded Vector Bundles over Submanifolds in Euclidean Space
Hanwen Liu
In this paper we study measure theoretical size of the image of naturally embedded vector bundles in under the codiagonal morphism, i.e. $Δ_{…
Back-Projection Pipeline
Pablo Navarrete Michelini, Hanwen Liu, Yunhua Lu +1
We propose a simple extension of residual networks that works simultaneously in multiple resolutions. Our network design is inspired by the iterative back-projection algorithm but…
Multi-Grid Back-Projection Networks
Pablo Navarrete Michelini, Wenbin Chen, Hanwen Liu +2
Multi-Grid Back-Projection (MGBP) is a fully-convolutional network architecture that can learn to restore images and videos with upscaling artifacts. Using the same strategy of mul…
Anchor-Based Spatio-Temporal Attention 3D Convolutional Networks for Dynamic 3D Point Cloud Sequences
Guangming Wang, Muyao Chen, Hanwen Liu +3
With the rapid development of measurement technology, LiDAR and depth cameras are widely used in the perception of the 3D environment. Recent learning based methods for robot perce…
MGBPv2: Scaling Up Multi-Grid Back-Projection Networks
Pablo Navarrete Michelini, Wenbin Chen, Hanwen Liu +1
Here, we describe our solution for the AIM-2019 Extreme Super-Resolution Challenge, where we won the 1st place in terms of perceptual quality (MOS) similar to the ground truth and…
A Tour of Convolutional Networks Guided by Linear Interpreters
Pablo Navarrete Michelini, Hanwen Liu, Yunhua Lu +1
Convolutional networks are large linear systems divided into layers and connected by non-linear units. These units are the "articulations" that allow the network to adapt to the in…
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report
Andrey Ignatov, Radu Timofte, Thang Van Vu +45
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones. The challenge consisted of two tra…
Multi-Scale Recursive and Perception-Distortion Controllable Image Super-Resolution
Pablo Navarrete Michelini, Dan Zhu, Hanwen Liu
We describe our solution for the PIRM Super-Resolution Challenge 2018 where we achieved the 2nd best perceptual quality for average RMSE<=16, 5th best for RMSE<=12.5, and 7th best…
Multigrid Backprojection Super-Resolution and Deep Filter Visualization
Pablo Navarrete Michelini, Hanwen Liu, Dan Zhu
We introduce a novel deep-learning architecture for image upscaling by large factors (e.g. 4x, 8x) based on examples of pristine high-resolution images. Our target is to reconstruc…
Convolutional Networks with MuxOut Layers as Multi-rate Systems for Image Upscaling
Pablo Navarrete Michelini, Hanwen Liu
We interpret convolutional networks as adaptive filters and combine them with so-called MuxOut layers to efficiently upscale low resolution images. We formalize this interpretation…