# Joint Learning of Distributed Representations for Images and Texts

Source: [Joint Learning of Distributed Representations for Images and Texts](<https://www.sota2.com/research/paper/68fa61e01e209131ce9f6468>)

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

Data updated: 2025-10-25T20:07:28.348Z

Authors: Xiaodong He, Rupesh Srivastava, Jianfeng Gao, Li Deng

Published: 2015-04-13T07:36:08.000Z

## Abstract

This technical report provides extra details of the deep multimodal similarity model (DMSM) which was proposed in (Fang et al. 2015, arXiv:1411.4952). The model is trained via maximizing global semantic similarity between images and their captions in natural language using the public Microsoft COCO database, which consists of a large set of images and their corresponding captions. The learned representations attempt to capture the combination of various visual concepts and cues.

## 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/1504.03083>)

[PDF](<https://arxiv.org/pdf/1504.03083.pdf>)