# Multimodal Transformer with Multi-View Visual Representation for Image Captioning

arXiv: https://arxiv.org/abs/1905.07841 · HTML: https://mlsanity.com/paper/1905.07841/

## Key facts

- Cited by 86 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2019.

## Built on by (most cited first, 30 of 86)

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- [Video2Music: Suitable Music Generation from Videos using an Affective Multimodal Transformer model](https://mlsanity.com/paper/2311.00968/) (2023)
- [Reading-strategy Inspired Visual Representation Learning for Text-to-Video Retrieval](https://mlsanity.com/paper/2201.09168/) (2022)
- [Deep Reinforcement Learning for Solving the Heterogeneous Capacitated Vehicle Routing Problem](https://mlsanity.com/paper/2110.02629/) (2021)
- [Robust Multimodal Learning with Missing Modalities via Parameter-Efficient Adaptation](https://mlsanity.com/paper/2310.03986/) (2023)
- [LocalTrans: A Multiscale Local Transformer Network for Cross-Resolution Homography Estimation](https://mlsanity.com/paper/2106.04067/) (2021)
- [Multi-Attention Network for Compressed Video Referring Object Segmentation](https://mlsanity.com/paper/2207.12622/) (2022)
- [Betrayed by Captions: Joint Caption Grounding and Generation for Open Vocabulary Instance Segmentation](https://mlsanity.com/paper/2301.00805/) (2023)
- [MAGIC: Multimodal relAtional Graph adversarIal inferenCe for Diverse and Unpaired Text-based Image Captioning](https://mlsanity.com/paper/2112.06558/) (2021)
- [HEALNet: Multimodal Fusion for Heterogeneous Biomedical Data](https://mlsanity.com/paper/2311.09115/) (2023)
- [Prophet: Prompting Large Language Models with Complementary Answer Heuristics for Knowledge-based Visual Question Answering](https://mlsanity.com/paper/2303.01903/) (2023)
- [Word2Pix: Word to Pixel Cross Attention Transformer in Visual Grounding](https://mlsanity.com/paper/2108.00205/) (2021)
- [Pretrain like Your Inference: Masked Tuning Improves Zero-Shot Composed Image Retrieval](https://mlsanity.com/paper/2311.07622/) (2023)
- [Kernel Identification Through Transformers](https://mlsanity.com/paper/2106.08185/) (2021)
- [Deep Relation Learning for Regression and Its Application to Brain Age Estimation](https://mlsanity.com/paper/2204.06598/) (2022)
- [HaVQA: A Dataset for Visual Question Answering and Multimodal Research in Hausa Language](https://mlsanity.com/paper/2305.17690/) (2023)
- [Learning Emergent Discrete Message Communication for Cooperative Reinforcement Learning](https://mlsanity.com/paper/2102.12550/) (2021)
- [Multimodality in Meta-Learning: A Comprehensive Survey](https://mlsanity.com/paper/2109.13576/) (2021)
- [Neural Attention for Image Captioning: Review of Outstanding Methods](https://mlsanity.com/paper/2111.15015/) (2021)
- [Dual-branch Hybrid Learning Network for Unbiased Scene Graph Generation](https://mlsanity.com/paper/2207.07913/) (2022)
- [A Comprehensive Survey of 3D Dense Captioning: Localizing and Describing Objects in 3D Scenes](https://mlsanity.com/paper/2403.07469/) (2024)
- [Enhancing Descriptive Captions with Visual Attributes for Multimodal Perception](https://mlsanity.com/paper/2412.14233/) (2024)
- [Detection of Illicit Drug Trafficking Events on Instagram: A Deep Multimodal Multilabel Learning Approach](https://mlsanity.com/paper/2108.08920/) (2021)
- [Sentiment-oriented Transformer-based Variational Autoencoder Network for Live Video Commenting](https://mlsanity.com/paper/2404.12782/) (2024)

## Abstract (arXiv)

Image captioning aims to automatically generate a natural language description of a given image, and most state-of-the-art models have adopted an encoder-decoder framework. The framework consists of a convolution neural network (CNN)-based image encoder that extracts region-based visual features from the input image, and an recurrent neural network (RNN)-based caption decoder that generates the output caption words based on the visual features with the attention mechanism. Despite the success of existing studies, current methods only model the co-attention that characterizes the inter-modal interactions while neglecting the self-attention that characterizes the intra-modal interactions. Inspired by the success of the Transformer model in machine translation, here we extend it to a Multimodal Transformer (MT) model for image captioning. Compared to existing image captioning approaches, the MT model simultaneously captures intra- and inter-modal interactions in a unified attention block. Due to the in-depth modular composition of such attention blocks, the MT model can perform complex multimodal reasoning and output accurate captions. Moreover, to further improve the image captioning performance, multi-view visual features are seamlessly introduced into the MT model. We quantitatively and qualitatively evaluate our approach using the benchmark MSCOCO image captioning dataset and conduct extensive ablation studies to investigate the reasons behind its effectiveness. The experimental results show that our method significantly outperforms the previous state-of-the-art methods. With an ensemble of seven models, our solution ranks the 1st place on the real-time leaderboard of the MSCOCO image captioning challenge at the time of the writing of this paper.