# MMIS: Multimodal Dataset for Interior Scene Visual Generation and Recognition

arXiv: https://arxiv.org/abs/2407.05980 · HTML: https://mlsanity.com/paper/2407.05980/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 19 references that are in the map.
- First posted on arXiv 8 Jul 2024.

## Builds on (most cited first, 13 of 13)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [Show and Tell: A Neural Image Caption Generator](https://mlsanity.com/paper/1411.4555/) (2014)
- [Asymmetric Loss For Multi-Label Classification](https://mlsanity.com/paper/2009.14119/) (2020)
- [Projected GANs Converge Faster](https://mlsanity.com/paper/2111.01007/) (2021)
- [DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation](https://mlsanity.com/paper/1604.02677/) (2016)
- [Image-text Retrieval: A Survey on Recent Research and Development](https://mlsanity.com/paper/2203.14713/) (2022)
- [RenAIssance: A Survey into AI Text-to-Image Generation in the Era of Large Model](https://mlsanity.com/paper/2309.00810/) (2023)
- [OmniVec: Learning robust representations with cross modal sharing](https://mlsanity.com/paper/2311.05709/) (2023)
- [Fine-grained Visual-textual Representation Learning](https://mlsanity.com/paper/1709.00340/) (2017)
- [Recurrent Affine Transformation for Text-to-image Synthesis](https://mlsanity.com/paper/2204.10482/) (2022)
- [MARL: Multimodal Attentional Representation Learning for Disease Prediction](https://mlsanity.com/paper/2105.00310/) (2021)

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

- [MAGIC: Transition-Aware Generation of Navigable Multi-Scene Game Worlds with Large Language Models](https://mlsanity.com/paper/2607.11594/) (2026)
- [BLM-SGAN: Bidirectional Language Modeling for Semantic-Spatial Text-to-Image Generation](https://mlsanity.com/paper/2606.08847/) (2026)

## Abstract (arXiv)

We introduce MMIS, a novel dataset designed to advance MultiModal Interior Scene generation and recognition. MMIS consists of nearly 160,000 images. Each image within the dataset is accompanied by its corresponding textual description and an audio recording of that description, providing rich and diverse sources of information for scene generation and recognition. MMIS encompasses a wide range of interior spaces, capturing various styles, layouts, and furnishings. To construct this dataset, we employed careful processes involving the collection of images, the generation of textual descriptions, and corresponding speech annotations. The presented dataset contributes to research in multi-modal representation learning tasks such as image generation, retrieval, captioning, and classification.