Topic
Annotation
About: Annotation is a research topic. Over the lifetime, 6719 publications have been published within this topic receiving 203463 citations. The topic is also known as: note & markup.
Papers published on a yearly basis
Papers
More filters
••
TL;DR: This paper introduces two novel elements to learn the video object segmentation model, the scribble attention module, which captures more accurate context information and learns an effective attention map to enhance the contrast between foreground and background, and the scribbles-supervised loss, which can optimize the unlabeled pixels and dynamically correct inaccurate segmented areas during the training stage.
Abstract: Recently, video object segmentation has received great attention in the computer vision community. Most of the existing methods heavily rely on the pixel-wise human annotations, which are expensive and time-consuming to obtain. To tackle this problem, we make an early attempt to achieve video object segmentation with scribble-level supervision, which can alleviate large amounts of human labor for collecting the manual annotation. However, using conventional network architectures and learning objective functions under this scenario cannot work well as the supervision information is highly sparse and incomplete. To address this issue, this paper introduces two novel elements to learn the video object segmentation model. The first one is the scribble attention module, which captures more accurate context information and learns an effective attention map to enhance the contrast between foreground and background. The other one is the scribble-supervised loss, which can optimize the unlabeled pixels and dynamically correct inaccurate segmented areas during the training stage. To evaluate the proposed method, we implement experiments on two video object segmentation benchmark datasets, YouTube-video object segmentation (VOS), and densely annotated video segmentation (DAVIS)-2017. We first generate the scribble annotations from the original per-pixel annotations. Then, we train our model and compare its test performance with the baseline models and other existing works. Extensive experiments demonstrate that the proposed method can work effectively and approach to the methods requiring the dense per-pixel annotations.
40 citations
•
IBM1
TL;DR: In this article, the authors present methods, systems, and articles of manufacture for creating and sharing an annotation associated with a data object other than the particular data object described by the annotation.
Abstract: Methods, systems, and articles of manufacture for creating and sharing an annotation associated with a data object other than the particular data object described by the annotation are provided. The annotation may be associated with an entity, even though the annotation may describe only a particular data object encompassed by the entity. By associating the annotation with the entity, the annotation may be made available to other users viewing information related to the entity, even if the particular data object described by the annotation is not displayed in the information being viewed.
40 citations
••
TL;DR: A novel method called ARGOT (Annotation Retrieval of Gene Ontology Terms) that is able to process quickly thousands of sequences for functional inference and was proven to outperform existing methods and to be suitable for function prediction of single proteins due to its high degree of sensitivity, specificity and coverage.
Abstract: Background
Large-scale sequencing projects have now become routine lab practice and this has led to the development of a new generation of tools involving function prediction methods, bringing the latter back to the fore. The advent of Gene Ontology, with its structured vocabulary and paradigm, has provided computational biologists with an appropriate means for this task.
Methodology
We present here a novel method called ARGOT (Annotation Retrieval of Gene Ontology Terms) that is able to process quickly thousands of sequences for functional inference. The tool exploits for the first time an integrated approach which combines clustering of GO terms, based on their semantic similarities, with a weighting scheme which assesses retrieved hits sharing a certain number of biological features with the sequence to be annotated. These hits may be obtained by different methods and in this work we have based ARGOT processing on BLAST results.
Conclusions
The extensive benchmark involved 10,000 protein sequences, the complete S. cerevisiae genome and a small subset of proteins for purposes of comparison with other available tools. The algorithm was proven to outperform existing methods and to be suitable for function prediction of single proteins due to its high degree of sensitivity, specificity and coverage.
40 citations
••
TL;DR: This document provides a step-by-step guide to producing a SO compliant file describing a sequence annotation, and shows where the terms needed to describe the gene's features are located in SO and their relationships to one another.
Abstract: This Sequence Ontology (SO) [13] aims to unify the way in which we describe sequence annotations, by providing a controlled vocabulary of terms and the relationships between them. Using SO terms to label the parts of sequence annotations greatly facilitates downstream analyses of their contents, as it ensures that annotations produced by different groups conform to a single standard. This greatly facilitates analyses of annotation contents and characteristics, e.g. comparisons of UTRs, alternative splicing, etc. Because SO also specifies the relationships between features, e.g. part_of, kind_of, annotations described with SO terms are also better substrates for validation and visualization software.This document provides a step-by-step guide to producing a SO compliant file describing a sequence annotation. We illustrate this by using an annotated gene as an example. First we show where the terms needed to describe the gene's features are located in SO and their relationships to one another. We then show line by line how to format the file to construct a SO compliant annotation of this gene.
40 citations
••
01 Jun 2016TL;DR: FirstWeb as mentioned in this paper is a browser-based interface for Rhetorical Structure Theory and other discourse relation annotations, which allows annotators to work online using only a browser and collects multiple annotations of the same documents on a central server, keeping track of annotation processes and assigning tasks and annotation schemes to users.
Abstract: This paper presents rstWeb, a new browserbased interface for Rhetorical Structure Theory and other discourse relation annotations. Expanding on previous tools for RST, rstWeb allows annotators to work online using only a browser. Project administrators can easily collect multiple annotations of the same documents on a central server, keep track of annotation processes and assign tasks and annotation schemes to users. A local version using an embedded web framework is also available, running offline on a desktop browser under the localhost.
40 citations