# An Empirical Study of Language CNN for Image Captioning

arXiv: https://arxiv.org/abs/1612.07086 · HTML: https://mlsanity.com/paper/1612.07086/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2016.

## Built on by (most cited first, 15 of 15)

- [From Show to Tell: A Survey on Deep Learning-based Image Captioning](https://mlsanity.com/paper/2107.06912/) (2021)
- [Multimodal Contrastive Training for Visual Representation Learning](https://mlsanity.com/paper/2104.12836/) (2021)
- [MeaCap: Memory-Augmented Zero-shot Image Captioning](https://mlsanity.com/paper/2403.03715/) (2024)
- [ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based Polishing](https://mlsanity.com/paper/2303.02437/) (2023)
- [Beyond a Pre-Trained Object Detector: Cross-Modal Textual and Visual Context for Image Captioning](https://mlsanity.com/paper/2205.04363/) (2022)
- [Semi-Autoregressive Transformer for Image Captioning](https://mlsanity.com/paper/2106.09436/) (2021)
- [Personalized Semi-Supervised Federated Learning for Human Activity Recognition](https://mlsanity.com/paper/2104.08094/) (2021)
- [Image Captioning via Compact Bidirectional Architecture](https://mlsanity.com/paper/2201.01984/) (2022)
- [Text-to-Image Generation via Implicit Visual Guidance and Hypernetwork](https://mlsanity.com/paper/2208.08493/) (2022)
- [A Survey on Image-text Multimodal Models](https://mlsanity.com/paper/2309.15857/) (2023)
- [ORID: Organ-Regional Information Driven Framework for Radiology Report Generation](https://mlsanity.com/paper/2411.13025/) (2024)
- [Synchronized Audio-Visual Frames with Fractional Positional Encoding for Transformers in Video-to-Text Translation](https://mlsanity.com/paper/2112.14088/) (2021)
- [Goal-driven text descriptions for images](https://mlsanity.com/paper/2108.12575/) (2021)
- [Cross Modification Attention Based Deliberation Model for Image Captioning](https://mlsanity.com/paper/2109.08411/) (2021)
- [Surveying the Landscape of Image Captioning Evaluation: A Comprehensive Taxonomy, Trends and Metrics Analysis](https://mlsanity.com/paper/2408.04909/) (2024)

## Abstract (arXiv)

Language Models based on recurrent neural networks have dominated recent image caption generation tasks. In this paper, we introduce a Language CNN model which is suitable for statistical language modeling tasks and shows competitive performance in image captioning. In contrast to previous models which predict next word based on one previous word and hidden state, our language CNN is fed with all the previous words and can model the long-range dependencies of history words, which are critical for image captioning. The effectiveness of our approach is validated on two datasets MS COCO and Flickr30K. Our extensive experimental results show that our method outperforms the vanilla recurrent neural network based language models and is competitive with the state-of-the-art methods.