# Where to put the Image in an Image Caption Generator

## TL;DR

This paper compares two methods of integrating image features into caption-generating RNNs, finding that merging is more practical without sacrificing performance, and suggests delaying multimodal integration.

## Contribution

It provides a systematic empirical comparison of injecting versus merging image features in RNN caption models, highlighting practical advantages of merging.

## Key findings

- Both architectures perform similarly in caption quality.
- Merging allows smaller RNN hidden states, reducing memory usage.
- Delaying multimodal integration does not harm performance.

## Abstract

When a recurrent neural network language model is used for caption generation, the image information can be fed to the neural network either by directly incorporating it in the RNN -- conditioning the language model by `injecting' image features -- or in a layer following the RNN -- conditioning the language model by `merging' image features. While both options are attested in the literature, there is as yet no systematic comparison between the two. In this paper we empirically show that it is not especially detrimental to performance whether one architecture is used or another. The merge architecture does have practical advantages, as conditioning by merging allows the RNN's hidden state vector to shrink in size by up to four times. Our results suggest that the visual and linguistic modalities for caption generation need not be jointly encoded by the RNN as that yields large, memory-intensive models with few tangible advantages in performance; rather, the multimodal integration should be delayed to a subsequent stage.