# Multimodal Interactions Using Pretrained Unimodal Models for SIMMC 2.0

arXiv: https://arxiv.org/abs/2112.05328 · HTML: https://mlsanity.com/paper/2112.05328/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 21 references that are in the map.
- First posted on arXiv 10 Dec 2021.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [Visual Dialog](https://mlsanity.com/paper/1611.08669/) (2016)
- [DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images](https://mlsanity.com/paper/1901.07973/) (2019)
- [GuessWhat?! Visual object discovery through multi-modal dialogue](https://mlsanity.com/paper/1611.08481/) (2016)
- [SIMMC 2.0: A Task-oriented Dialog Dataset for Immersive Multimodal Conversations](https://mlsanity.com/paper/2104.08667/) (2021)
- [Multimodal Transformer Networks for End-to-End Video-Grounded Dialogue Systems](https://mlsanity.com/paper/1907.01166/) (2019)
- [Situated and Interactive Multimodal Conversations](https://mlsanity.com/paper/2006.01460/) (2020)
- [Transforming Multi-Conditioned Generation from Meaning Representation](https://mlsanity.com/paper/2101.04257/) (2021)

## Built on by (most cited first, 1 of 1)

- [SPRING: Situated Conversation Agent Pretrained with Multimodal Questions from Incremental Layout Graph](https://mlsanity.com/paper/2301.01949/) (2023)

## Abstract (arXiv)

This paper presents our work on the Situated Interactive MultiModal Conversations 2.0 challenge held at Dialog State Tracking Challenge 10. SIMMC 2.0 includes 4 subtasks, and we introduce our multimodal approaches for the subtask #1, #2 and the generation of subtask #4. SIMMC 2.0 dataset is a multimodal dataset containing image and text information, which is more challenging than the problem of only text-based conversations because it must be solved by understanding the relationship between image and text. Therefore, since there is a limit to solving only text models such as BERT or GPT2, we propose a multimodal model combining image and text. We first pretrain the multimodal model to understand the relationship between image and text, then finetune our model for each task. We achieve the 3rd best performance in subtask #1, #2 and a runner-up in the generation of subtask #4. The source code is available at https://github.com/rungjoo/simmc2.0.