# DVM-CAR: A large-scale automotive dataset for visual marketing research and applications

arXiv: https://arxiv.org/abs/2109.00881 · HTML: https://mlsanity.com/paper/2109.00881/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 52 references that are in the map.
- First posted on arXiv 10 Aug 2021.

## Builds on (most cited first, 10 of 10)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://mlsanity.com/paper/1704.08063/) (2017)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale](https://mlsanity.com/paper/1811.00982/) (2018)
- [A Large-Scale Car Dataset for Fine-Grained Categorization and Verification](https://mlsanity.com/paper/1506.08959/) (2015)
- [Photo Aesthetics Ranking Network with Attributes and Content Adaptation](https://mlsanity.com/paper/1606.01621/) (2016)
- [DeSIGN: Design Inspiration from Generative Networks](https://mlsanity.com/paper/1804.00921/) (2018)

## Built on by (most cited first, 17 of 17)

- [Best of Both Worlds: Multimodal Contrastive Learning with Tabular and Imaging Data](https://mlsanity.com/paper/2303.14080/) (2023)
- [TIP: Tabular-Image Pre-training for Multimodal Classification with Incomplete Data](https://mlsanity.com/paper/2407.07582/) (2024)
- [STiL: Semi-supervised Tabular-Image Learning for Comprehensive Task-Relevant Information Exploration in Multimodal Classification](https://mlsanity.com/paper/2503.06277/) (2025)
- [Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data](https://mlsanity.com/paper/2412.16243/) (2024)
- [Tables Guide Vision: Learning to See the Heart through Tabular Data](https://mlsanity.com/paper/2503.14998/) (2025)
- [Inference-Time Dynamic Modality Selection for Incomplete Multimodal Classification](https://mlsanity.com/paper/2601.22853/) (2026)
- [Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodings](https://mlsanity.com/paper/2210.14056/) (2022)
- [Car-Studio: Learning Car Radiance Fields from Single-View and Endless In-the-wild Images](https://mlsanity.com/paper/2307.14009/) (2023)
- [Camera Height Doesn't Change: Unsupervised Training for Metric Monocular Road-Scene Depth Estimation](https://mlsanity.com/paper/2312.04530/) (2023)
- [VT-Bench: A Unified Benchmark for Visual-Tabular Multi-Modal Learning](https://mlsanity.com/paper/2605.08146/) (2026)
- [Gamma-from-Mono: Road-Relative, Metric, Self-Supervised Monocular Geometry for Vehicular Applications](https://mlsanity.com/paper/2512.04303/) (2025)
- [Harmonized Tabular-Image Fusion via Gradient-Aligned Alternating Learning](https://mlsanity.com/paper/2604.01579/) (2026)
- [Code and Pixels: Multi-Modal Contrastive Pre-training for Enhanced Tabular Data Analysis](https://mlsanity.com/paper/2501.07304/) (2025)
- [AMF-MedIT: An Efficient Align-Modulation-Fusion Framework for Medical Image-Tabular Data](https://mlsanity.com/paper/2506.19439/) (2025)
- [Disentangled Fine-Grained Prototype Learning for Incomplete Image-Tabular Classification](https://mlsanity.com/paper/2606.05455/) (2026)
- [The Importance of Encoder Choice:A Tabular-Image Study](https://mlsanity.com/paper/2607.07756/) (2026)
- [iStructTab: Structured Feature Sequencing for Multimodal Learning of Image and Tabular Data](https://mlsanity.com/paper/2608.04348/) (2026)

## Abstract (arXiv)

There is a growing interest in product aesthetics analytics and design. However, the lack of available large-scale data that covers various variables and information is one of the biggest challenges faced by analysts and researchers. In this paper, we present our multidisciplinary initiative of developing a comprehensive automotive dataset from different online sources and formats. Specifically, the created dataset contains 1.4 million images from 899 car models and their corresponding model specifications and sales information over more than ten years in the UK market. Our work makes significant contributions to: (i) research and applications in the automotive industry; (ii) big data creation and sharing; (iii) database design; and (iv) data fusion. Apart from our motivation, technical details and data structure, we further present three simple examples to demonstrate how our data can be used in business research and applications.