# Review Networks for Caption Generation

arXiv: https://arxiv.org/abs/1605.07912 · HTML: https://mlsanity.com/paper/1605.07912/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2016.

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

- [Minions: Accelerating Large Language Model Inference with Aggregated Speculative Execution](https://mlsanity.com/paper/2402.15678/) (2024)
- [Goal-driven text descriptions for images](https://mlsanity.com/paper/2108.12575/) (2021)

## Abstract (arXiv)

We propose a novel extension of the encoder-decoder framework, called a review network. The review network is generic and can enhance any existing encoder- decoder model: in this paper, we consider RNN decoders with both CNN and RNN encoders. The review network performs a number of review steps with attention mechanism on the encoder hidden states, and outputs a thought vector after each review step; the thought vectors are used as the input of the attention mechanism in the decoder. We show that conventional encoder-decoders are a special case of our framework. Empirically, we show that our framework improves over state-of- the-art encoder-decoder systems on the tasks of image captioning and source code captioning.