506 citations · 1.2k across the 55 of their papers we have counts for
11 papers · 1 filter
Document Domain Randomization for Deep Learning Document Layout Extraction
Meng Ling, Jian Chen, Torsten Möller +7
We present document domain randomization (DDR), the first successful transfer of convolutional neural networks (CNNs) trained only on graphically rendered pseudo-paper pages to rea…
OmniLayout: Room Layout Reconstruction from Indoor Spherical Panoramas
Shivansh Rao, Vikas Kumar, Daniel Kifer +2
Given a single RGB panorama, the goal of 3D layout reconstruction is to estimate the room layout by predicting the corners, floor boundary, and ceiling boundary. A common approach…
Sibling Neural Estimators: Improving Iterative Image Decoding with Gradient Communication
Ankur Mali, Alexander G. Ororbia, Clyde Lee Giles
For lossy image compression, we develop a neural-based system which learns a nonlinear estimator for decoding from quantized representations. The system links two recurrent network…
TextContourNet: a Flexible and Effective Framework for Improving Scene Text Detection Architecture with a Multi-task Cascade
Dafang He, Xiao Yang, Daniel Kifer +1
We study the problem of extracting text instance contour information from images and use it to assist scene text detection. We propose a novel and effective framework for this and…
A Neural Temporal Model for Human Motion Prediction
Anand Gopalakrishnan, Ankur Mali, Dan Kifer +2
We propose novel neural temporal models for predicting and synthesizing human motion, achieving state-of-the-art in modeling long-term motion trajectories while being competitive w…
Large Scale Scene Text Verification with Guided Attention
Dafang He, Yeqing Li, Alexander Gorban +5
Many tasks are related to determining if a particular text string exists in an image. In this work, we propose a new framework that learns this task in an end-to-end way. The frame…
Learned Neural Iterative Decoding for Lossy Image Compression Systems
Alexander G. Ororbia, Ankur Mali, Jian Wu +3
For lossy image compression systems, we develop an algorithm, iterative refinement, to improve the decoder's reconstruction compared to standard decoding techniques. Specifically,…
Learning to Extract Semantic Structure from Documents Using Multimodal Fully Convolutional Neural Network
Xiao Yang, Ersin Yumer, Paul Asente +3
We present an end-to-end, multimodal, fully convolutional network for extracting semantic structures from document images. We consider document semantic structure extraction as a p…
Learning a Hierarchical Latent-Variable Model of 3D Shapes
Shikun Liu, C. Lee Giles, Alexander G. Ororbia
We propose the Variational Shape Learner (VSL), a generative model that learns the underlying structure of voxelized 3D shapes in an unsupervised fashion. Through the use of skip-c…
Smart Library: Identifying Books in a Library using Richly Supervised Deep Scene Text Reading
Xiao Yang, Dafang He, Wenyi Huang +4
Physical library collections are valuable and long standing resources for knowledge and learning. However, managing books in a large bookshelf and finding books on it often leads t…
Automatic Identification and Data Extraction from 2-Dimensional Plots in Digital Documents
William Brouwer, Saurabh Kataria, Sujatha Das +2
Most search engines index the textual content of documents in digital libraries. However, scholarly articles frequently report important findings in figures for visual impact and t…