# Annotation Methodologies for Vision and Language Dataset Creation

Source: [Annotation Methodologies for Vision and Language Dataset Creation](<https://www.sota2.com/research/paper/68fa629a1e209131ce9fa172>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:22:26.903Z

Authors: Gitit Kehat, James Pustejovsky

Published: 2016-07-10T18:11:27.000Z

## Abstract

Annotated datasets are commonly used in the training and evaluation of tasks involving natural language and vision (image description generation, action recognition and visual question answering). However, many of the existing datasets reflect problems that emerge in the process of data selection and annotation. Here we point out some of the difficulties and problems one confronts when creating and validating annotated vision and language datasets.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1607.02769>)

[PDF](<https://arxiv.org/pdf/1607.02769.pdf>)