# Better Text Understanding Through Image-To-Text Transfer

arXiv: https://arxiv.org/abs/1705.08386 · HTML: https://mlsanity.com/paper/1705.08386/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2017.

## Built on by (most cited first, 2 of 2)

- [Learning Zero-Shot Multifaceted Visually Grounded Word Embeddings via Multi-Task Training](https://mlsanity.com/paper/2104.07500/) (2021)
- [Visual Grounding of Inter-lingual Word-Embeddings](https://mlsanity.com/paper/2209.03714/) (2022)

## Abstract (arXiv)

Generic text embeddings are successfully used in a variety of tasks. However, they are often learnt by capturing the co-occurrence structure from pure text corpora, resulting in limitations of their ability to generalize. In this paper, we explore models that incorporate visual information into the text representation. Based on comprehensive ablation studies, we propose a conceptually simple, yet well performing architecture. It outperforms previous multimodal approaches on a set of well established benchmarks. We also improve the state-of-the-art results for image-related text datasets, using orders of magnitude less data.