mlsanityData to 14 Oct 2026

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

arXiv 2109.00881 ↗First posted 10 Aug 2021cs.CVcited by 17

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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.

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

10 of 52 references.

References · 10

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2017Mask R-CNNcited by 7,277
  4. 2014Striving for Simplicity: The All Convolutional Netcited by 990
  5. 2017SphereFace: Deep Hypersphere Embedding for Face Recognitioncited by 737
  6. 2017Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networkscited by 707
  7. 2018The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scalecited by 544
  8. 2015A Large-Scale Car Dataset for Fine-Grained Categorization and Verificationcited by 130
  9. 2016Photo Aesthetics Ranking Network with Attributes and Content Adaptationcited by 125
  10. 2018DeSIGN: Design Inspiration from Generative Networkscited by 20

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 2; 2025: 5; 2026: 6.

17 papers.

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  1. 2023Best of Both Worlds: Multimodal Contrastive Learning with Tabular and Imaging Datacited by 69
  2. 2024TIP: Tabular-Image Pre-training for Multimodal Classification with Incomplete Datacited by 32
  3. 2025STiL: Semi-supervised Tabular-Image Learning for Comprehensive Task-Relevant Information Exploration in Multimodal Classificationcited by 12
  4. 2024Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Datacited by 7
  5. 2025Tables Guide Vision: Learning to See the Heart through Tabular Datacited by 4
  6. 2026Inference-Time Dynamic Modality Selection for Incomplete Multimodal Classificationcited by 4
  7. 2022Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodingscited by 3
  8. 2023Car-Studio: Learning Car Radiance Fields from Single-View and Endless In-the-wild Imagescited by 3
  9. 2023Camera Height Doesn't Change: Unsupervised Training for Metric Monocular Road-Scene Depth Estimationcited by 3
  10. 2026VT-Bench: A Unified Benchmark for Visual-Tabular Multi-Modal Learningcited by 3
  11. 2025Gamma-from-Mono: Road-Relative, Metric, Self-Supervised Monocular Geometry for Vehicular Applicationscited by 1
  12. 2026Harmonized Tabular-Image Fusion via Gradient-Aligned Alternating Learningcited by 1
5 more
  1. 2025Code and Pixels: Multi-Modal Contrastive Pre-training for Enhanced Tabular Data Analysiscited by 0
  2. 2025AMF-MedIT: An Efficient Align-Modulation-Fusion Framework for Medical Image-Tabular Datacited by 0
  3. 2026Disentangled Fine-Grained Prototype Learning for Incomplete Image-Tabular Classificationcited by 0
  4. 2026The Importance of Encoder Choice:A Tabular-Image Studycited by 0
  5. 2026iStructTab: Structured Feature Sequencing for Multimodal Learning of Image and Tabular Datacited by 0