Showing 1-25 of 2,109 resultsfor "Index Terms":Annotation Errors
"Index Terms":Annotation Errors
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Comparing the Quality of Neural Machine Translation and Professional Post-Editing
2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX)
Year: 2019 | Conference Paper |
This empirical corpus study explores the quality of neural machine translations (NMT) and their post-edits (NMTPE) at the German Department of the European Commission's Directorate-General for Translation (DGT) by evaluating NMT outputs, NMTPE, and respective revisions (REV) with the automatic error annotation tool Hjerson (Popović 2011) and the more fine-grained manual MQM framework (Lommel 2014)...Show More
BERT-Assisted Semantic Annotation Correction for Emotion-Related Questions
2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
Annotated data have traditionally been used to provide the input for training a supervised machine learning (ML) model. However, current pre-trained ML models for natural language processing (NLP) contain embedded linguistic information that can be used to inform the annotation process. We use the BERT neural language model to feed information back into an annotation task that involves semantic la...Show More
Assistive Image Annotation Systems with Deep Learning and Natural Language Capabilities: A Review
2024 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC)
Year: 2024 | Conference Paper |
While supervised learning has achieved significant success in computer vision tasks, acquiring high-quality annotated data remains a bottleneck. This paper explores both scholarly and non-scholarly works in AI-assistive deep learning image annotation systems that provide textual suggestions, captions, or descriptions of the input image to the annotator. This potentially results in higher annotatio...Show More
Better quantifying inter-annotator variability: A step towards citizen science in underwater passive acoustics
Gabriel Dubus;Maëlle Torterotot;Paul Nguyen Hong Duc;Julie Beesau;Dorian Cazau;Olivier Adam
Deployments of underwater passive acoustic recorders have been widely used to study marine biodiversity, especially to detect vocal cetaceans. To process the huge amount of data collected, automatic detection and classification methods are necessary. Recently the development of such methods, which includes training and then testing the models, is mainly based on so-called ground-truth labels, obta...Show More
Automatically generated metadata from large collections is an essential component of digital libraries. It is beginning to emerge as fundamental to the study of languages. Morphosyntactic annotation captures the form of individual words and their function. Nonetheless automated syntactic analysis is still imperfect and human annotators can be significantly more accurate. On the other hand, human w...Show More
Semi-supervised annotation of Transcranial Doppler ultrasound micro-embolic data
Yamil Vindas;Emmanuel Roux;Blaise Kévin Guépié;Marilys Almar;Philippe Delachartre
2021 IEEE International Ultrasonics Symposium (IUS)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
Transcranial Doppler (TCD) is a non-invasive ultrasound monitoring technique allowing real time measurements of the blood flow velocity mainly in the Middle Cerebral Artery. It is commonly used to monitor patients with stroke risk by detecting micro-emboli. This technique generates a considerable amount of data whose annotation is expensive and time-consuming. We propose a semi-supervised learning...Show More
Automatic image annotation based on feature fusion and cost-sensitive learning
2021 6th International Symposium on Computer and Information Processing Technology (ISCIPT)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
In order to solve the problem that it is difficult to recognize small-scale objects in the process of image annotation, an automatic image annotation method based on multi-scale features is proposed. This method combines transfer learning and deep learning, adds a feature fusion layer to the convolutional neural network model, establishes a top-down feature fusion mechanism, and builds a convoluti...Show More
Performance of Human Annotators in Object Detection and Segmentation of Remotely Sensed Data
IEEE Transactions on Geoscience and Remote Sensing
Year: 2025 | Early Access Article |
This study introduces a laboratory experiment designed to assess the influence of annotation strategies, levels of class imbalance, and prior experience, on the performance of human annotators. The experiment focuses on labeling aerial imagery, using ArcGIS Pro, to detect and segment small-scale PVs, selected as a case study for rectangular objects. The experiment is conducted using images with a ...Show More
Research on English Grammar Error Correction Technology Based on BLSTM Sequence Annotation
2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)
Year: 2021 | Conference Paper |
Cited by: Papers (2)
Grammatical errors are one of the most common types of errors in English learning and writing. Intelligent detection and correction of English grammatical errors can effectively help English learners improve their learning efficiency. The research is based on the BLSTM bi-directional and long-short-term memory neural network to construct a sequence labeling model to detect and correct English gram...Show More
Towards the automatic data annotation for human activity recognition based on wearables and BLE beacons
2021 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL)
Year: 2021 | Conference Paper |
Cited by: Papers (6)
In machine learning, the data annotation process is an essential, but error-prone and time-consuming manual activity, which associates metadata to the samples of a dataset. In the context of Human Activity Recognition (HAR) this generally reflects in a human watching the video recordings of the activities carried out by the target user to assign a label to each video frame. The label can refer, fo...Show More
A New Bounding Box based Pseudo Annotation Generation Method for Semantic Segmentation
Xiaolong Xu;Fanman Meng;Hongliang Li;Qingbo Wu;King Ngi Ngan;Shuai Chen
2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)
Year: 2020 | Conference Paper |
Cited by: Papers (2)
This paper proposes a fusion-based method to generate pseudo-annotations from bounding boxes for semantic segmentation. The idea is to first generate diverse foreground masks by multiple bounding box segmentation methods, and then combine these masks to generate pseudo-annotations. Existing methods generate foreground masks from bounding boxes by classical segmentation methods driving by low-level...Show More
Learning Semantic Concepts from Noisy Media Collection for Automatic Image Annotation
Chinese Journal of Electronics
Cited by: Papers (1)
Along with the explosive growth of images, automatic image annotation has attracted great interest of various research communities. However, despite the great progress achieved in the past two decades, automatic annotation is still an important open problem in computer vision, and can hardly achieve satisfactory performance in real-world environment. In this paper, we address the problem of annota...Show More
Keyword Annotation of Medical Image with Random Forest Classifier and Confidence Assigning
2011 Eighth International Conference Computer Graphics, Imaging and Visualization
Year: 2011 | Conference Paper |
Cited by: Papers (2)
This paper introduces an efficient keyword based medical image retrieval method using image classification and confidence assigning of each keyword. To classify images, we first extract wavelet-based CSLBP (WCS-LBP) descriptors from local parts of the images and then we apply the extracted feature vector to decision trees to construct random forests, which are an ensemble of random decision trees....Show More
On Data Annotation Efficiency for Image Based Crowd Counting
2022 IEEE International Conference on Visual Communications and Image Processing (VCIP)
Year: 2022 | Conference Paper |
Crowd counting aims at automatically estimating the number of persons in still images. It has attracted much attention due to its potential usage in surveillance, intelligent transportation and many other scenarios. In the recent decade, most researchers have been focusing on the design of novel deep learning models for improved crowd counting performance. Such attempts include proposing advanced ...Show More
Localized generalization error based active learning for image annotation
2008 IEEE International Conference on Systems, Man and Cybernetics
Year: 2008 | Conference Paper |
Cited by: Papers (20)
Content-based image auto-annotation becomes a hot research topic owing to the development of image retrieval system and the storing technology of multimedia information. It is a key step in most of those image processing applications. In this work, we adopt active learning to image annotation for reducing the number of labeled images required for supervised learning procedure. Localized Generaliza...Show More
MAAD-Face: A Massively Annotated Attribute Dataset for Face Images
Philipp Terhörst;Daniel Fährmann;Jan Niklas Kolf;Naser Damer;Florian Kirchbuchner;Arjan Kuijper
Soft-biometrics play an important role in face biometrics and related fields since these might lead to biased performances, threaten the user's privacy, or are valuable for commercial aspects. Current face databases are specifically constructed for the development of face recognition applications. Consequently, these databases contain a large number of face images but lack in the number of attribu...Show More
SHADE: Semantic Hypernym Annotator for Domain-Specific Entities - Dungeons and Dragons Domain Use Case
2023 IEEE 17th International Conference on Industrial and Information Systems (ICIIS)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Manual data annotation is an important NLP task but one that takes a considerable amount of resources and effort. In spite of the costs, labelling and categorizing entities are essential for NLP tasks such as semantic evaluation. Even though annotation can be done by non-experts in most cases, due to the fact that this requires human labour, the process is costly. Another major challenge encounter...Show More
Scalable Semi-Automatic Annotation for Multi-Camera Person Tracking
Jorge Nino-Castañeda;Andrés Frías-Velázquez;Nyan Bo Bo;Maarten Slembrouck;Junzhi Guan;Glen Debard;Bart Vanrumste;Tinne Tuytelaars;Wilfried Philips
This paper proposes a generic methodology for the semi-automatic generation of reliable position annotations for evaluating multi-camera people-trackers on large video data sets. Most of the annotation data are automatically computed, by estimating a consensus tracking result from multiple existing trackers and people detectors and classifying it as either reliable or not. A small subset of the da...Show More
Panicle Segmentation on UAV Captured Multispectral Paddy Crop Imagery
IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
Year: 2024 | Conference Paper |
Automation of crop yield estimation is crucial to cultivate efficient breeding techniques to fulfill the increasing population demands and adapt to climate change. The panicle count of paddy is directly associated with the yield of crop. In this work, we implement computer vision-based image segmentation methods for segmenting panicles by utilizing Unmanned Aerial Vehicle (UAV) captured multispect...Show More
FRETA-D: A Toolkit of Automatic Annotation of Grammatical and Phonetic Error Types in French Dictations
2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)
Year: 2022 | Conference Paper |
Dictation is considered as an efficient practice for testing French as a Foreign Language (FFL) learners’ language proficiency. However, in-class dictation and teachers’ manual correction greatly reduce teaching efficiency. An existing dictation platform can only partly resolve these problems by providing instant error correction. To pursue better pedagogical feedback, this study develops an annot...Show More
LabelVizier: Interactive Validation and Relabeling for Technical Text Annotations
Xiaoyu Zhang;Xiwei Xuan;Alden Dima;Thurston Sexton;Kwan-Liu Ma
2023 IEEE 16th Pacific Visualization Symposium (PacificVis)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
With the rapid accumulation of text data produced by data-driven techniques, the task of extracting "data annotations"—concise, high-quality data summaries from unstructured raw text—has become increasingly important. The recent advances in weak supervision and crowd-sourcing techniques provide promising solutions to efficiently create annotations (labels) for large-scale technical text data. Howe...Show More
Development of a Synchronous Collaborative Writing Revision Instrument for Teaching English
2009 International Conference on Information Management and Engineering
Year: 2009 | Conference Paper |
Cited by: Papers (1)
Collaborative writing, one of the methodological innovations for language teaching, is "the social act of creating a single, coordinate document with two or more participants". Revision is the last process for writing. Corrective feedback and error correction, critical tasks for revision, are important for English as a second language/English as a foreign language (ESL/EFL) writing instruction. Re...Show More
Semi-automatic System for Rapid Annotation of Moving Objects in Surveillance Videos using Deep Detection and Multi-object Tracking Techniques
Noor M. AL-Shakarji;Ekincan Ufuktepe;Filiz Bunyak;Hadi Aliakbarpour;Guna Seetharaman;Kannappan Palaniappan
2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)
Year: 2020 | Conference Paper |
Cited by: Papers (3)
With the recent advances in video sensor technologies, emergence of new applications associated with these technologies, and demand for automated video analytics have increased the need for ground-truth annotations. Researchers attempt to explore different methodologies and algorithms on different challenging datasets. Ground-truth annotations are needed for quantitative evaluation and comparison ...Show More
Radar Target Recognition Architecture with Automatic Sample Annotation Capability
2024 4th International Symposium on Computer Technology and Information Science (ISCTIS)
Year: 2024 | Conference Paper |
Radar data acquisition constrained by uncertainties requires that the radar target recognition system needs to have dynamic learning capability. As a key, automatic sample annotation directly restricts the practicalization of radar target recognition technology, which is mainly based on the supervised learning method. Aiming at the automatic sample annotation problem in radar target recognition ba...Show More
Annotation Effort Reduction for Introducing AI into Optical Inspection Systems
2024 International Symposium on Semiconductor Manufacturing (ISSM)
Year: 2024 | Conference Paper |
Optical wafer inspection equipment featuring rule-based inspection criteria is widely used in semiconductor manufacturing. Recently, we have seen an increasing movement to introduce AI, especially deep learning, to improve optical inspection. On the other hand, deep learning requires a large amount of training data, and creating the training data requires highly accurate labeling (annotation) of i...Show More
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