Definition: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Input: We propose a model to learn visually grounded word embeddings (vis-w2v) to capture visual notions of semantic relatedness. While word embeddings trained using text have been extremely successful, they cannot uncover notions of semantic relatedness implicit in our visual world. For instance, visual grounding can help us realize that concepts like eating and staring at are related, since when people are eating something, they also tend to stare at the food. Grounding a rich variety of relations like eating and stare at in vision is a challenging task, despite recent progress in vision. We realize the visual grounding for words depends on the semantics of our visual world, and not the literal pixels. We thus use abstract scenes created from clipart to provide the visual grounding. We find that the embeddings we learn capture fine-grained visually grounded notions of semantic relatedness. We show improvements over text only word embeddings (word2vec) on three tasks: common-sense assertion classification, visual paraphrasing and text-based image retrieval. Our code and datasets will be available online.

Visual Word2Vec (vis-w2v): Learning Visually Grounded Word Embeddings Using Abstract Scenes