You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories:
- Background: provides context or foundational information related to the topic.
- Extends: builds upon the cited work.
- Uses: applies the methods or findings of the cited work.
- Motivation: cites the work as inspiration or rationale for the research.
- CompareOrContrast: compares or contrasts the cited work with others.
- FutureWork: cites the work as a direction for future research.

Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response.

Section Title:
related work

Context before the citation:
The first work to do this with topic models is Feng and Lapata (2010b). As computer vision techniques have improved over the past decade, other research has begun directly using visual information in place of feature norms. Silberer and Lapata (2012) introduce a new method of multimodal integration based on Canonical Correlation Analysis, and performs a systematic comparison between their CCA-based model and others on association norm prediction, held out feature prediction, and word similarity.

Citation Sentence:
The first work to do this with topic models is Feng and Lapata ( 2010b ) .

Context after the citation:
They use a Bag of Visual Words (BoVW) model (Lowe, 2004) to create a bimodal vocabulary describing documents. The topic model using the bimodal vocabulary outperforms a purely textual based model in word association and word similarity prediction. Bruni et al. (2012a) show how a BoVW model may be easily combined with a distributional vector space model of language using only vector concatenation. Bruni et al. (2012b) show that the contextual visual words (i.e. the visual features around an object, rather than of the object itself) are even more useful at times, suggesting the plausibility of a sort of distributional hypothesis for images.
Background