Generating Natural Questions About an Image (1603.06059v3)

Abstract: There has been an explosion of work in the vision & language community during the past few years from image captioning to video transcription, and answering questions about images. These tasks have focused on literal descriptions of the image. To move beyond the literal, we choose to explore how questions about an image are often directed at commonsense inference and the abstract events evoked by objects in the image. In this paper, we introduce the novel task of Visual Question Generation (VQG), where the system is tasked with asking a natural and engaging question when shown an image. We provide three datasets which cover a variety of images from object-centric to event-centric, with considerably more abstract training data than provided to state-of-the-art captioning systems thus far. We train and test several generative and retrieval models to tackle the task of VQG. Evaluation results show that while such models ask reasonable questions for a variety of images, there is still a wide gap with human performance which motivates further work on connecting images with commonsense knowledge and pragmatics. Our proposed task offers a new challenge to the community which we hope furthers interest in exploring deeper connections between vision & language.

Analysis of a Placeholder PDF Document in LaTeX

The provided text is a LaTeX template for compiling a PDF document, and not an actual academic paper complete with research content or data. Consequently, an analytical exploration of the content typically found in a research paper, such as methodology, results, or conclusions, is not applicable here. Instead, this text serves as a basic framework for producing a formalized scholarly article format, suggesting inclusion of a document class and metadata through LaTeX commands.

Structure and Functionality

In its essence, this document layout specifies:

1. Document Class: It designates a4paper as the document's paper size within the article class. This is a common choice in academic publishing for papers intended for print distribution on A4-sized sheets.
2. PDF Metadata: Through the pdfinfo command, metadata such as Title, Author, Subject, and Keywords can be included. This metadata serves the purpose of document identification and classification, facilitating search and retrieval in digital repositories.
3. Content Inclusion: The \includepdf function implies that the main content is type-set in a separate PDF (here, a placeholder arxiv-pdf.pdf). This method might be employed when the main content is generated or provided as a standalone PDF file, while supplementary front matter (like a cover page) is added through this tex file.

Implications and Use Cases

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The utility of this template extends to major academic fields that utilize PDF documents for disseminating research findings, particularly in physics, computer science, and engineering. The availability of proper metadata ensures compliance with archiving standards and enhances the discoverability of research outputs.

Future Research Considerations

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