scholarly journals Quality assessment for 3D reconstruction of building interiors

2020 ◽  
Vol 2020 (17) ◽  
pp. 36-1-36-7
Author(s):  
Umamaheswaran RAMAN KUMAR ◽  
Inge COUDRON ◽  
Steven PUTTEMANS ◽  
Patrick VANDEWALLE
Keyword(s):  
3D Reconstruction ◽  
Real World ◽  
State Of The Art ◽  
Ground Truth ◽  
3D Models ◽  
Indoor Environments ◽  
Ground Truth Data ◽  
Complex Design ◽  
Reconstructed Model

Applications ranging from simple visualization to complex design require 3D models of indoor environments. This has given rise to advancements in the field of automated reconstruction of such models. In this paper, we review several state-of-the-art metrics proposed for geometric comparison of 3D models of building interiors. We evaluate their performance on a real-world dataset and propose one tailored metric which can be used to assess the quality of the reconstructed model. In addition, the proposed metric can also be easily visualized to highlight the regions or structures where the reconstruction failed. To demonstrate the versatility of the proposed metric we conducted experiments on various interior models by comparison with ground truth data created by expert Blender artists. The results of the experiments were then used to improve the reconstruction pipeline.

FluidC+: A novel community detection algorithm based on fluid propagation

2019 ◽  
Vol 30 (04) ◽  
pp. 1950021
Author(s):  
Jinfang Sheng ◽  
Kai Wang ◽  
Zejun Sun ◽  
Jie Hu ◽  
Bin Wang ◽  
...  
Keyword(s):  
Community Detection ◽  
Real World ◽  
Seed Set ◽  
State Of The Art ◽  
Ground Truth ◽  
Detection Algorithm ◽  
Original Algorithm ◽  
Community Detection Algorithm ◽  
Better Than

In recent years, community detection has gradually become a hot topic in the complex network data mining field. The research of community detection is helpful not only to understand network topology structure but also to explore network hiding function. In this paper, we improve FluidC which is a novel community detection algorithm based on fluid propagation, by ameliorating the quality of seed set based on positive feedback and determining the node update order. We first summarize the shortcomings of FluidC and analyze the reasons result in these drawbacks. Then, we took some effective measures to overcome them and proposed an efficient community detection algorithm, called FluidC+. Finally, experiments on the generated network and real-world network show that our method not only greatly improves the performance of the original algorithm FluidC but also is better than many state-of-the-art algorithms, especially in the performance on real-world network with ground truth.

scholarly journals DeepMAsED: evaluating the quality of metagenomic assemblies

2020 ◽  
Vol 36 (10) ◽  
pp. 3011-3017 ◽  
Author(s):  
Olga Mineeva ◽  
Mateo Rojas-Carulla ◽  
Ruth E Ley ◽  
Bernhard Schölkopf ◽  
Nicholas D Youngblut
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Ground Truth ◽  
Supplementary Information ◽  
Learning Approach ◽  
Wide Range ◽  
Metagenome Assembly ◽  
Model Training ◽  
Reference Genomes

Abstract Motivation Methodological advances in metagenome assembly are rapidly increasing in the number of published metagenome assemblies. However, identifying misassemblies is challenging due to a lack of closely related reference genomes that can act as pseudo ground truth. Existing reference-free methods are no longer maintained, can make strong assumptions that may not hold across a diversity of research projects, and have not been validated on large-scale metagenome assemblies. Results We present DeepMAsED, a deep learning approach for identifying misassembled contigs without the need for reference genomes. Moreover, we provide an in silico pipeline for generating large-scale, realistic metagenome assemblies for comprehensive model training and testing. DeepMAsED accuracy substantially exceeds the state-of-the-art when applied to large and complex metagenome assemblies. Our model estimates a 1% contig misassembly rate in two recent large-scale metagenome assembly publications. Conclusions DeepMAsED accurately identifies misassemblies in metagenome-assembled contigs from a broad diversity of bacteria and archaea without the need for reference genomes or strong modeling assumptions. Running DeepMAsED is straight-forward, as well as is model re-training with our dataset generation pipeline. Therefore, DeepMAsED is a flexible misassembly classifier that can be applied to a wide range of metagenome assembly projects. Availability and implementation DeepMAsED is available from GitHub at https://github.com/leylabmpi/DeepMAsED. Supplementary information Supplementary data are available at Bioinformatics online.

Glean

2021 ◽  
Vol 14 (6) ◽  
pp. 997-1005
Author(s):  
Sandeep Tata ◽  
Navneet Potti ◽  
James B. Wendt ◽  
Lauro Beltrão Costa ◽  
Marc Najork ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Management ◽  
Real World ◽  
Empirical Studies ◽  
Ground Truth ◽  
Training Data ◽  
Ground Truth Data ◽  
Document Type ◽  
Machine Learning Model ◽  
Structured Information

Extracting structured information from templatic documents is an important problem with the potential to automate many real-world business workflows such as payment, procurement, and payroll. The core challenge is that such documents can be laid out in virtually infinitely different ways. A good solution to this problem is one that generalizes well not only to known templates such as invoices from a known vendor, but also to unseen ones. We developed a system called Glean to tackle this problem. Given a target schema for a document type and some labeled documents of that type, Glean uses machine learning to automatically extract structured information from other documents of that type. In this paper, we describe the overall architecture of Glean, and discuss three key data management challenges : 1) managing the quality of ground truth data, 2) generating training data for the machine learning model using labeled documents, and 3) building tools that help a developer rapidly build and improve a model for a given document type. Through empirical studies on a real-world dataset, we show that these data management techniques allow us to train a model that is over 5 F1 points better than the exact same model architecture without the techniques we describe. We argue that for such information-extraction problems, designing abstractions that carefully manage the training data is at least as important as choosing a good model architecture.

On transferability and contexts when using simulated grasp databases

Robotica ◽  
2014 ◽  
Vol 33 (5) ◽  
pp. 1131-1146
Author(s):  
Jimmy A. Rytz ◽  
Lars-Peter Ellekilde ◽  
Dirk Kraft ◽  
Henrik G. Petersen ◽  
Norbert Krüger
Keyword(s):  
Real World ◽  
State Of The Art ◽  
Specific Context ◽  
Grasp Planning ◽  
Large Databases ◽  
Outcome Space ◽  
Bin Picking ◽  
Use Context ◽  
Context Specific

SUMMARYIt has become a common practice to use simulation to generate large databases of good grasps for grasp planning in robotics research. However, the existence of a generic simulation context that enables the generation of high quality grasps that can be used in several different contexts such as bin-picking or picking objects from a table, has to our knowledge not yet been discussed in the literature.In this paper, we investigate how well the quality of grasps simulated in a commonly used “generic” context transfers to a specific context, both, in simulation and in the real world.We generate a large database of grasp hypotheses for several objects and grippers, which we then evaluate in different dynamic simulation contexts e.g., free floating (no gravity, no obstacles), standing on a table and lying on a table.We present a comparison on the intersection of the grasp outcome space across the different contexts and quantitatively show that to generate reliable grasp databases, it is important to use context specific simulation.We furthermore evaluate how well a state of the art grasp database transfers from two simulated contexts to a real world context of picking an object from a table and discuss how to evaluate transferability into non-deterministic real world contexts.

scholarly journals Separate in Latent Space: Unsupervised Single Image Layer Separation

2020 ◽  
Vol 34 (07) ◽  
pp. 11661-11668 ◽  
Author(s):  
Yunfei Liu ◽  
Feng Lu
Keyword(s):  
Real World ◽  
Ground Truth ◽  
Single Image ◽  
Ground Truth Data ◽  
Layer Separation ◽  
World Vision ◽  
Image Layer ◽  
Latent Space ◽  
Consistency Constraints ◽  
Ill Posed

Many real world vision tasks, such as reflection removal from a transparent surface and intrinsic image decomposition, can be modeled as single image layer separation. However, this problem is highly ill-posed, requiring accurately aligned and hard to collect triplet data to train the CNN models. To address this problem, this paper proposes an unsupervised method that requires no ground truth data triplet in training. At the core of the method are two assumptions about data distributions in the latent spaces of different layers, based on which a novel unsupervised layer separation pipeline can be derived. Then the method can be constructed based on the GANs framework with self-supervision and cycle consistency constraints, etc. Experimental results demonstrate its successfulness in outperforming existing unsupervised methods in both synthetic and real world tasks. The method also shows its ability to solve a more challenging multi-layer separation task.

scholarly journals The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations

2019 ◽  
Author(s):  
Thibault Laugel ◽  
Marie-Jeanne Lesot ◽  
Christophe Marsala ◽  
Xavier Renard ◽  
Marcin Detyniecki
Keyword(s):  
State Of The Art ◽  
Ground Truth ◽  
Black Box ◽  
Box Model ◽  
Ground Truth Data ◽  
Actual Knowledge ◽  
Post Hoc

Post-hoc interpretability approaches have been proven to be powerful tools to generate explanations for the predictions made by a trained black-box model. However, they create the risk of having explanations that are a result of some artifacts learned by the model instead of actual knowledge from the data. This paper focuses on the case of counterfactual explanations and asks whether the generated instances can be justified, i.e. continuously connected to some ground-truth data. We evaluate the risk of generating unjustified counterfactual examples by investigating the local neighborhoods of instances whose predictions are to be explained and show that this risk is quite high for several datasets. Furthermore, we show that most state of the art approaches do not differentiate justified from unjustified counterfactual examples, leading to less useful explanations.

scholarly journals Partial Label Learning with Self-Guided Retraining

2019 ◽  
Vol 33 ◽  
pp. 3542-3549 ◽  
Author(s):  
Lei Feng ◽  
Bo An
Keyword(s):  
Real World ◽  
Optimization Problem ◽  
State Of The Art ◽  
Ground Truth ◽  
Learning Approaches ◽  
High Confidence ◽  
Infinity Norm ◽  
Real World Datasets ◽  
Partial Label Learning ◽  
Optimization Efficiency

Partial label learning deals with the problem where each training instance is assigned a set of candidate labels, only one of which is correct. This paper provides the first attempt to leverage the idea of self-training for dealing with partially labeled examples. Specifically, we propose a unified formulation with proper constraints to train the desired model and perform pseudo-labeling jointly. For pseudo-labeling, unlike traditional self-training that manually differentiates the ground-truth label with enough high confidence, we introduce the maximum infinity norm regularization on the modeling outputs to automatically achieve this consideratum, which results in a convex-concave optimization problem. We show that optimizing this convex-concave problem is equivalent to solving a set of quadratic programming (QP) problems. By proposing an upper-bound surrogate objective function, we turn to solving only one QP problem for improving the optimization efficiency. Extensive experiments on synthesized and real-world datasets demonstrate that the proposed approach significantly outperforms the state-of-the-art partial label learning approaches.

scholarly journals Unsupervised Quality Estimation for Neural Machine Translation

2020 ◽  
Vol 8 ◽  
pp. 539-555
Author(s):  
Marina Fomicheva ◽  
Shuo Sun ◽  
Lisa Yankovskaya ◽  
Frédéric Blain ◽  
Francisco Guzmán ◽  
...  
Keyword(s):  
Machine Translation ◽  
Real World ◽  
State Of The Art ◽  
Black Box ◽  
Test Time ◽  
Quality Estimation ◽  
Neural Machine Translation ◽  
Real World Applications ◽  
Unsupervised Approach

Quality Estimation (QE) is an important component in making Machine Translation (MT) useful in real-world applications, as it is aimed to inform the user on the quality of the MT output at test time. Existing approaches require large amounts of expert annotated data, computation, and time for training. As an alternative, we devise an unsupervised approach to QE where no training or access to additional resources besides the MT system itself is required. Different from most of the current work that treats the MT system as a black box, we explore useful information that can be extracted from the MT system as a by-product of translation. By utilizing methods for uncertainty quantification, we achieve very good correlation with human judgments of quality, rivaling state-of-the-art supervised QE models. To evaluate our approach we collect the first dataset that enables work on both black-box and glass-box approaches to QE.

A tool for semi-automatic ground truth annotation of traffic videos

2020 ◽  
Vol 2020 (16) ◽  
pp. 200-1-200-7
Author(s):  
Florian Groh ◽  
Dominik Schörkhuber ◽  
Margrit Gelautz
Keyword(s):  
User Study ◽  
State Of The Art ◽  
User Interaction ◽  
Ground Truth ◽  
Automatic Annotation ◽  
Video Traffic ◽  
Ground Truth Data ◽  
Lighting Conditions ◽  
Ground Truth Generation ◽  
Dynamic Lighting

We have developed a semi-automatic annotation tool – “CVL Annotator” – for bounding box ground truth generation in videos. Our research is particularly motivated by the need for reference annotations of challenging nighttime traffic scenes with highly dynamic lighting conditions due to reflections, headlights and halos from oncoming traffic. Our tool incorporates a suite of different state-of-the-art tracking algorithms in order to minimize the amount of human input necessary to generate high-quality ground truth data. We focus our user interface on the premise of minimizing user interaction and visualizing all information relevant to the user at a glance. We perform a preliminary user study to measure the amount of time and clicks necessary to produce ground truth annotations of video traffic scenes and evaluate the accuracy of the final annotation results.

Tablet-Based Traffic Counting Application Designed to Minimize Human Error

2013 ◽  
Vol 2339 (1) ◽  
pp. 39-46 ◽  
Author(s):  
Christopher Toth ◽  
Wonho Suh ◽  
Vetri Elango ◽  
Ramik Sadana ◽  
Angshuman Guin ◽  
...  
Keyword(s):  
Count Data ◽  
Transportation Planning ◽  
Human Error ◽  
Ground Truth ◽  
Model Verification ◽  
Freeway Traffic ◽  
Ground Truth Data ◽  
Traffic Count ◽  
System Verification

Basic traffic counts are among the key elements in transportation planning and forecasting. As emerging data collection technologies proliferate, the availability of traffic count data will expand by orders of magnitude. However, availability of data does not always guarantee data accuracy, and it is essential that observed data are compared with ground truth data. Little research or guidance is available that ensures the quality of ground truth data with which the count results of automated technologies can be compared. To address the issue of ground truth data based on manual counts, a manual traffic counting application was developed for an Android tablet. Unlike other manual count applications, this application allows data collectors to replay and toggle through the video in supervisory mode to review and correct counts made in the first pass. For system verification, the review function of the application was used to count and recount freeway traffic in videos from the Atlanta, Georgia, metropolitan area. Initial counts and reviewed counts were compared, and improvements in count accuracy were assessed. The results indicated the benefit of the review process and suggested that this application could minimize human error and provide more accurate ground truth traffic count data for use in transportation planning applications and for model verification.

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