Relevant bibliographies by topics / Dataset annotation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Dataset annotation'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Dataset annotation"

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Benitez-Garcia, Gibran, Jesus Olivares-Mercado, Gabriel Sanchez-Perez, and Hiroki Takahashi. "IPN HandS: Efficient Annotation Tool and Dataset for Skeleton-Based Hand Gesture Recognition." Applied Sciences 15, no. 11 (2025): 6321. https://doi.org/10.3390/app15116321.

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Hand gesture recognition (HGR) heavily relies on high-quality annotated datasets. However, annotating hand landmarks in video sequences is a time-intensive challenge. In this work, we introduce IPN HandS, an enhanced version of our IPN Hand dataset, which now includes approximately 700,000 hand skeleton annotations and corrected gesture boundaries. To generate these annotations efficiently, we propose a novel annotation tool that combines automatic detection, inter-frame interpolation, copy–paste capabilities, and manual refinement. This tool significantly reduces annotation time from 70 min t
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VanBerlo, Bennett, Delaney Smith, Jared Tschirhart, et al. "Enhancing Annotation Efficiency with Machine Learning: Automated Partitioning of a Lung Ultrasound Dataset by View." Diagnostics 12, no. 10 (2022): 2351. http://dx.doi.org/10.3390/diagnostics12102351.

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Background: Annotating large medical imaging datasets is an arduous and expensive task, especially when the datasets in question are not organized according to deep learning goals. Here, we propose a method that exploits the hierarchical organization of annotating tasks to optimize efficiency. Methods: We trained a machine learning model to accurately distinguish between one of two classes of lung ultrasound (LUS) views using 2908 clips from a larger dataset. Partitioning the remaining dataset by view would reduce downstream labelling efforts by enabling annotators to focus on annotating patho
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Ahmed, Saad, Saman Hina, Raheela Asif, Sana Ahmed, and Munad Ahmed. "Development and Evaluation of Gold Standard Dataset for Sentiment Analysis of Tweets." Pakistan Journal of Engineering and Technology 6, no. 4 (2024): 7–12. http://dx.doi.org/10.51846/vol6iss4pp7-12.

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Pre-labeled data is typically required for supervised machine learning. A limited number of object classes in the majority of open access and pre-annotated datasets make them unsuitable for certain tasks, even though they are readily available for training machine learning algorithms. For custom models, previously available pre-annotated data is typically insufficient, so gathering and preparing training data is necessary for the majority of real-world applications. The quantity and quality of annotations clearly trade-off with one another. Either more annotated data can be produced or better
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Zenonas, Theodosiou, and Tsapatsoulis Nicolas. "Image annotation: the effects of content, lexicon and annotation method." International Journal of Multimedia Information Retrieval 9 (March 1, 2020): 191–203. https://doi.org/10.1007/s13735-020-00193-z.

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Image annotation is the process of assigning metadata to images, allowing effective retrieval by text-based search techniques. Despite the lots of eorts in automatic multimedia analysis, automatic semantic annotation of multimedia is still inefficient due to the problems in modelling high level semantic terms. In this paper we examine the factors affecting the quality of annotations collected through crowdsourcing platforms. An image dataset was manually annotated utilizing: (i) a vocabulary consists of pre-selected set of keywords,(ii) an hierarchical vocabulary, and (iii) free keywords. The
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Raumanns, Ralf, Gerard Schouten, Max Joosten, Josien P. W. Pluim, and Veronika Cheplygina. "ENHANCE (ENriching Health data by ANnotations of Crowd and Experts): A case study for skin lesion classification." Machine Learning for Biomedical Imaging 1, December 2021 (2021): 1–26. http://dx.doi.org/10.59275/j.melba.2021-geb9.

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We present ENHANCE, an open dataset with multiple annotations to complement the existing ISIC and PH2 skin lesion classification datasets. This dataset contains annotations of visual ABC (asymmetry, border, colour) features from non-expert annotation sources: undergraduate students, crowd workers from Amazon MTurk and classic image processing algorithms. In this paper we first analyse the correlations between the annotations and the diagnostic label of the lesion, as well as study the agreement between different annotation sources. Overall we find weak correlations of non-expert annotations wi
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Jacobs, Gilles, and Véronique Hoste. "SENTiVENT: enabling supervised information extraction of company-specific events in economic and financial news." Language Resources and Evaluation 56, no. 1 (2021): 225–57. http://dx.doi.org/10.1007/s10579-021-09562-4.

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AbstractWe present SENTiVENT, a corpus of fine-grained company-specific events in English economic news articles. The domain of event processing is highly productive and various general domain, fine-grained event extraction corpora are freely available but economically-focused resources are lacking. This work fills a large need for a manually annotated dataset for economic and financial text mining applications. A representative corpus of business news is crawled and an annotation scheme developed with an iteratively refined economic event typology. The annotations are compatible with benchmar
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Marchesoni-Acland, Franco, Jean-Michel Morel, Josselin Kherroubi, and Gabriele Facciolo. "Optimal and Efficient Binary Questioning for Accelerated Annotation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 13 (2025): 14336–43. https://doi.org/10.1609/aaai.v39i13.33570.

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Even though data annotation is extremely important for interpretability, research, and development of artificial intelligence solutions, annotating data remains costly. Research efforts such as active learning or few-shot learning alleviate the cost by increasing sample efficiency, yet the problem of annotating data more quickly has received comparatively little attention. Leveraging a predictor has been shown to reduce annotation cost in practice but has not been theoretically considered. We ask the following question: to annotate a binary classification dataset with N samples, can the annota
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Schilling, Marcel P., Niket Ahuja, Luca Rettenberger, Tim Scherr, and Markus Reischl. "Impact of Annotation Noise on Histopathology Nucleus Segmentation." Current Directions in Biomedical Engineering 8, no. 2 (2022): 197–200. http://dx.doi.org/10.1515/cdbme-2022-1051.

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Abstract Deep learning is often used for automated diagnosis support in biomedical image processing scenarios. Annotated datasets are essential for the supervised training of deep neural networks. The problem of consistent and noise-free annotation remains for experts such as pathologists. The variability within an annotator (intra) and the variability between annotators (inter) are current challenges. In clinical practice or biology, instance segmentation is a common task, but a comprehensive and quantitative study regarding the impact of noisy annotations lacks. In this paper, we present a c
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Rasmussen, Christoffer Bøgelund, Kristian Kirk, and Thomas B. Moeslund. "The Challenge of Data Annotation in Deep Learning—A Case Study on Whole Plant Corn Silage." Sensors 22, no. 4 (2022): 1596. http://dx.doi.org/10.3390/s22041596.

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Recent advances in computer vision are primarily driven by the usage of deep learning, which is known to require large amounts of data, and creating datasets for this purpose is not a trivial task. Larger benchmark datasets often have detailed processes with multiple stages and users with different roles during annotation. However, this can be difficult to implement in smaller projects where resources can be limited. Therefore, in this work we present our processes for creating an image dataset for kernel fragmentation and stover overlengths in Whole Plant Corn Silage. This includes the guidel
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Philipp, Markus, Anna Alperovich, Alexander Lisogorov, et al. "Annotation-efficient learning of surgical instrument activity in neurosurgery." Current Directions in Biomedical Engineering 8, no. 1 (2022): 30–33. http://dx.doi.org/10.1515/cdbme-2022-0008.

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Abstract Machine learning-based solutions rely heavily on the quality and quantity of the training data. In the medical domain, the main challenge is to acquire rich and diverse annotated datasets for training. We propose to decrease the annotation efforts and further diversify the dataset by introducing an annotation-efficient learning workflow. Instead of costly pixel-level annotation, we require only image-level labels as the remainder is covered by simulation. Thus, we obtain a large-scale dataset with realistic images and accurate ground truth annotations. We use this dataset for the inst
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Dissertations / Theses on the topic "Dataset annotation"

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Herbst, Alyssa Kathryn. "Bounded Expectation of Label Assignment: Dataset Annotation by Supervised Splitting with Bias-Reduction Techniques." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/96517.

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Annotating large unlabeled datasets can be a major bottleneck for machine learning applications. We introduce a scheme for inferring labels of unlabeled data at a fraction of the cost of labeling the entire dataset. We refer to the scheme as Bounded Expectation of Label Assignment (BELA). BELA greedily queries an oracle (or human labeler) and partitions a dataset to find data subsets that have mostly the same label. BELA can then infer labels by majority vote of the known labels in each subset. BELA makes the decision to split or label from a subset by maximizing a lower bound on the expected
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Mezírka, Martin. "Pokročilé metody detekce hran v obraze." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234883.

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The goal of this work is to investigate options how to apply trainable edge detection algorithm Structured forest for fast edge detection to information extraction from historici maps and medical images. For the work, annotated dataset was created and the detektor was tested on it. Structured forest achieved better results on map data, compared with classical detectors. Success rate of finding edges of bones was similar at both approaches. Aim of the work is focused on comparing different image annotation styles, experiments with dataset, including determining parameters and evaluation of the
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Tagebrand, Emil, and Ek Emil Gustafsson. "Dataset Generation in a Simulated Environment Using Real Flight Data for Reliable Runway Detection Capabilities." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-54974.

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Implementing object detection methods for runway detection during landing approaches is limited in the safety-critical aircraft domain. This limitation is due to the difficulty that comes with verification of the design and the ability to understand how the object detection behaves during operation. During operation, object detection needs to consider the aircraft's position, environmental factors, different runways and aircraft attitudes. Training such an object detection model requires a comprehensive dataset that defines the features mentioned above. The feature's impact on the detection ca
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ALZETTA, CHIARA. "From Texts to Prerequisites. Identifying and Annotating Propaedeutic Relations in Educational Textual Resources." Doctoral thesis, Università degli studi di Genova, 2021. http://hdl.handle.net/11567/1050378.

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Prerequisite Relations (PRs) are dependency relations established between two distinct concepts expressing which piece(s) of information a student has to learn first in order to understand a certain target concept. Such relations are one of the most fundamental in Education, playing a crucial role not only for what concerns new knowledge acquisition, but also in the novel applications of Artificial Intelligence to distant and e-learning. Indeed, resources annotated with such information could be used to develop automatic systems able to acquire and organize the knowledge embodied in educationa
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Zhang, Ao. "Object Detection from FMCW Radar Using Deep Learning." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42512.

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Sensors, as a crucial part of autonomous driving, are primarily used for perceiving the environment. The recent deep learning development of different sensors has demonstrated the ability of machines recognizing and understanding their surroundings. Automotive radar, as a primary sensor for self-driving vehicles, is well-known for its robustness against variable lighting and weather conditions. Compared with camera-based deep learning development, Object detection using automotive radars has not been explored to its full extent. This can be attributed to the lack of public radar datasets. I
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Mahmood, Muhammad Habib. "Motion annotation in complex video datasets." Doctoral thesis, Universitat de Girona, 2018. http://hdl.handle.net/10803/667583.

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Motion segmentation refers to the process of separating regions and trajectories from a video sequence into coherent subsets of space and time. In this thesis, we created a new multifaceted motion segmentation dataset enclosing real-life long and short sequences, with different numbers of motions and frames per sequence, and real distortions with missing data. Trajectory- and region-based ground-truth is provided on all the frames of all the sequences. We also proposed a new semi-automatic tool for delineating the trajectories in complex videos, even in videos captured from moving cameras. Wit
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Csóka, Pavel. "Rozpoznávání textu pomocí konvolučních sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2016. http://www.nusl.cz/ntk/nusl-255303.

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This thesis aims at creation of new datasets for text recognition machine learning tasks and experiments with convolutional neural networks on these datasets. It describes architecture of convolutional nets, difficulties of recognizing text from photographs and contemporary works using these networks. Next, creation of annotation, using Tesseract OCR, for dataset comprised from photos of document pages, taken by mobile phones, named Mobile Page Photos. From this dataset two additional are created by cropping characters out of its photos formatted as Street View House Numbers dataset. Dataset M
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Li, Jin. "Constructing classification trees with exception annotations for large datasets." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0027/MQ51392.pdf.

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Romuld, Daniel, and Markus Ruhmén. "Compiling attention datasets : Developing a method for annotating face datasets with human performance attention labels using crowdsourcing." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166708.

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This essay expands on the problem of human attention detection in computer vision. This is achieved by providing a method for annotating existing face datasets with attention labels through the use of human intelligence. The work described in this essay is justified by a lack of human performance attention datasets and the potential uses of the developed method. Several images of crowds were generated using the Labeled Faces in the Wild dataset of images depicting faces. Thus enabling evaluation of the level of attention of the depicted subjects as part of a crowd. The data collection methodol
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Liu, Jixiong. "Semantic Annotations for Tabular Data Using Embeddings : Application to Datasets Indexing and Table Augmentation." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS529.

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Avec le développement de l'Open Data, un grand nombre de sources de données sont mises à disposition des communautés (notamment les data scientists et les data analysts). Ces données constituent des sources importantes pour les services numériques sous réserve que les données soient nettoyées, non biaisées, et combinées à une sémantique explicite et compréhensible par les algorithmes afin de favoriser leur exploitation. En particulier, les sources de données structurées (CSV, JSON, XML, etc.) constituent la matière première de nombreux processus de science des données. Cependant, ces données p
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Book chapters on the topic "Dataset annotation"

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Alves, Diego, Gaurisha Thakkar, and Marko Tadić. "UNER: Universal Named-Entity Recognition Framework." In Event Analytics across Languages and Communities. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64451-1_1.

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AbstractNamed-entity recognition and classification (NERC) is an essential natural language processing (NLP) task involved in many applications like interactive question answering, summarising, relation extraction, and text mining. Available NERC corpora follow different annotation schemes that vary in terms of formats and levels of complexity according to research requirements: from 1-level hierarchy annotations (e.g., “Person”, “Location”, and “Organisation”) to multi-level schemes. Inspired by the work of the Universal Dependencies framework in terms of a standard representation of parsed t
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Visi, Federico, Rodrigo Schramm, Kerstin Frödin, Åsa Unander-Scharin, and Stefan Östersjö. "Empirical Analysis of Gestural Sonic Objects Combining Qualitative and Quantitative Methods." In Current Research in Systematic Musicology. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57892-2_7.

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AbstractIn this chapter, we describe a series of studies related to our research on using gestural sonic objects in music analysis. These include developing a method for annotating the qualities of gestural sonic objects on multimodal recordings; ranking which features in a multimodal dataset are good predictors of basic qualities of gestural sonic objects using the Random Forests algorithm; and a supervised learning method for automated spotting designed to assist human annotators. The subject of our analyses is a performance of Fragmente2, a choreomusical composition based on the Japanese co
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Tontodimamma, Alice, Stefano Anzani, Marco Antonio Stranisci, Valerio Basile, Elisa Ignazzi, and Lara Fontanella. "An experimental annotation task to investigate annotators’ subjectivity in a Misogyny dataset." In Proceedings e report. Firenze University Press and Genova University Press, 2023. http://dx.doi.org/10.36253/979-12-215-0106-3.49.

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In recent years, hatred directed against women has spread exponentially, especially in online social media. Although this alarming phenomenon has given rise to many studies both from the viewpoint of computational linguistics and from that of machine learning, less effort has been devoted to analysing whether models for the detection of misogyny are affected by bias. An emerging topic that challenges traditional approaches for the creation of corpora is the presence of social bias in natural language processing (NLP). Many NLP tasks are subjective, in the sense that a variety of valid beliefs
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Gruber, Roland, Steffen Rüger, Moritz Ottenweller, Norman Uhlmann, and Stefan Gerth. "XXL-CT Dataset Segmentation." In Unlocking Artificial Intelligence. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64832-8_18.

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AbstractThe objective of XXL-CT dataset segmentation is to use machine learning to virtually divide 3D volumes of complete vehicles, acquired through XXL computer tomography, into their individual components. Gathering labeled training data for this type of data is challenging. Previously, entity classification from XXL-CT data required significant manual effort involving over 120 employees for several months. This chapter shows how to develop entity segmentation procedures which significantly reduce the time from measurement to virtual analysis. The most time-consuming part of the data proces
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Azkune, Gorka, Aitor Almeida, Diego López-de-Ipiña, and Liming Chen. "A Knowledge-Driven Tool for Automatic Activity Dataset Annotation." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11313-5_52.

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Apoorva, G. Drushti, and Radhika Mamidi. "BolLy: Annotation of Sentiment Polarity in Bollywood Lyrics Dataset." In Communications in Computer and Information Science. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8438-6_4.

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Tolovski, Ilin, Sašo Džeroski, and Panče Panov. "Semantic Annotation of Predictive Modelling Experiments." In Discovery Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61527-7_9.

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Abstract In this paper, we address the task of representation, semantic annotation, storage, and querying of predictive modelling experiments. We introduce OntoExp, an OntoDM module which gives a more granular representation of a predictive modeling experiment and enables annotation of the experiment’s provenance, algorithm implementations, parameter settings and output metrics. This module is incorporated in SemanticHub, an online system that allows execution, annotation, storage and querying of predictive modeling experiments. The system offers two different user scenarios. The users can eit
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Yoon, Jihun, Jiwon Lee, SungHyun Park, Woo Jin Hyung, and Min-Kook Choi. "Semi-supervised Learning for Instrument Detection with a Class Imbalanced Dataset." In Interpretable and Annotation-Efficient Learning for Medical Image Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61166-8_28.

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Koeva, Svetla. "Multilingual Image Corpus." In European Language Grid. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17258-8_22.

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AbstractThe ELG pilot project Multilingual Image Corpus (MIC 21) provides a large image dataset with annotated objects and multilingual descriptions in 25 languages. Our main contributions are: the provision of a large collection of highquality, copyright-free images; the formulation of an ontology of visual objects based on WordNet noun hierarchies; precise manual correction of automatic image segmentation and annotation of object classes; and association of objects and images with extended multilingual descriptions. The dataset is designed for image classification, object detection and seman
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Beneventano, Domenico, Sonia Bergamaschi, and Serena Sorrentino. "Semantic Annotation of the CEREALAB Database by the AGROVOC Linked Dataset." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39637-3_16.

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Conference papers on the topic "Dataset annotation"

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Bello, Rotimi-Williams, Pius A. Owolawi, Etienne A. van Wyk, and Chunling Tu. "Image annotation tools and dataset: a comparative analysis in brief." In International Conference on AI-generated Content (AIGC 2024), edited by Duoqian Miao and Feng Zhao. SPIE, 2025. https://doi.org/10.1117/12.3065175.

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Choi, Juhwan, JungMin Yun, Kyohoon Jin, and YoungBin Kim. "Multi-News+: Cost-efficient Dataset Cleansing via LLM-based Data Annotation." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-main.2.

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Wang, Yan, Yawen Zeng, Jingsheng Zheng, Xiaofen Xing, Jin Xu, and Xiangmin Xu. "VideoCoT: A Video Chain-of-Thought Dataset with Active Annotation Tool." In Proceedings of the 3rd Workshop on Advances in Language and Vision Research (ALVR). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.alvr-1.8.

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Xie, Leiyu, Federico Angelini, and Syed Mohsen Naqvi. "NCL-DASB: GEO-Located Maritime Surveillance Labeled Dataset and Annotation API." In 2024 27th International Conference on Information Fusion (FUSION). IEEE, 2024. http://dx.doi.org/10.23919/fusion59988.2024.10706534.

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Nedorosleva, Sofia, and Maria Kyrarini. "Translucent Object Dataset and Automated 6D Pose Annotation Method for Robotic Manipulation." In 2025 11th International Conference on Automation, Robotics, and Applications (ICARA). IEEE, 2025. https://doi.org/10.1109/icara64554.2025.10977667.

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Li, Rui, Qi Liu, Liyang He, et al. "Optimizing Code Retrieval: High-Quality and Scalable Dataset Annotation through Large Language Models." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-main.123.

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Agamirov, Levon V., Vladimir L. Agamirov, Vadim S. Los, Maxim Nosikov, and Nataliya V. Toutova. "Dataset Annotation Converter for Training Neural Networks to Detect Defects on Various Surfaces." In 2024 Intelligent Technologies and Electronic Devices in Vehicle and Road Transport Complex (TIRVED). IEEE, 2024. https://doi.org/10.1109/tirved63561.2024.10769786.

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Hou, Qinyao, Ding Ding, Jiaju Yang, and Jiahang Tu. "EOSAD: An Event-Oriented Physiological and Behavioral Social Anxiety Annotation Dataset in Virtual Reality*." In 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2024. https://doi.org/10.1109/smc54092.2024.10832025.

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Riou, Kévin, Kaiwen Dong, Yujie Huang, Kévin Subrin, D. Patrick Le Callet, and Yanjing Sun. "Evaluating 3D Human Pose Estimation in Occluded Multi-Sensor Scenarios: Dataset and Annotation Approach." In 2024 IEEE International Conference on Image Processing (ICIP). IEEE, 2024. http://dx.doi.org/10.1109/icip51287.2024.10647858.

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Sheng, Diwei, Anbang Yang, John-Ross Rizzo, and Chen Feng. "NYC-Indoor-VPR: A Long-Term Indoor Visual Place Recognition Dataset with Semi-Automatic Annotation." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10610564.

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Reports on the topic "Dataset annotation"

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Ghanim, Murad, Joe Cicero, Judith K. Brown, and Henryk Czosnek. Dissection of Whitefly-geminivirus Interactions at the Transcriptomic, Proteomic and Cellular Levels. United States Department of Agriculture, 2010. http://dx.doi.org/10.32747/2010.7592654.bard.

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Our project focuses on gene expression and proteomics of the whitefly Bemisia tabaci (Gennadius) species complex in relation to the internal anatomy and localization of expressed genes and virions in the whitefly vector, which poses a major constraint to vegetable and fiber production in Israel and the USA. While many biological parameters are known for begomovirus transmission, nothing is known about vector proteins involved in the specific interactions between begomoviruses and their whitefly vectors. Identifying such proteins is expected to lead to the design of novel control methods that i
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Rodriguez Muxica, Natalia. Open configuration options Bioinformatics for Researchers in Life Sciences: Tools and Learning Resources. Inter-American Development Bank, 2022. http://dx.doi.org/10.18235/0003982.

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The COVID-19 pandemic has shown that bioinformatics--a multidisciplinary field that combines biological knowledge with computer programming concerned with the acquisition, storage, analysis, and dissemination of biological data--has a fundamental role in scientific research strategies in all disciplines involved in fighting the virus and its variants. It aids in sequencing and annotating genomes and their observed mutations; analyzing gene and protein expression; simulation and modeling of DNA, RNA, proteins and biomolecular interactions; and mining of biological literature, among many other c
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