mlsanityData to 7 Oct 2026

The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale

arXiv 1811.00982 ↗First posted 2 Nov 2018cs.CVcited by 542

Not on the map. Why?

Cited by 38 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We present Open Images V4, a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection. The images have a Creative Commons Attribution license that allows to share and adapt the material, and they have been collected from Flickr without a predefined list of class names or tags, leading to natural class statistics and avoiding an initial design bias. Open Images V4 offers large scale across several dimensions: 30.1M image-level labels for 19.8k concepts, 15.4M bounding boxes for 600 object classes, and 375k visual relationship annotations involving 57 classes. For object detection in particular, we provide 15x more bounding boxes than the next largest datasets (15.4M boxes on 1.9M images). The images often show complex scenes with several objects (8 annotated objects per image on average). We annotated visual relationships between them, which support visual relationship detection, an emerging task that requires structured reasoning. We provide in-depth comprehensive statistics about the dataset, we validate the quality of the annotations, we study how the performance of several modern models evolves with increasing amounts of training data, and we demonstrate two applications made possible by having unified annotations of multiple types coexisting in the same images. We hope that the scale, quality, and variety of Open Images V4 will foster further research and innovation even beyond the areas of image classification, object detection, and visual relationship detection.

Key facts. Cited by 542 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Nov 2018.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2020: 1; 2021: 103; 2022: 154; 2023: 126; 2024: 82; 2025: 56; 2026: 20.

542 papers.

Sort:

By area:

  1. 2021Ego4D: Around the World in 3,000 Hours of Egocentric Videocited by 1,530
  2. 2022Objaverse: A Universe of Annotated 3D Objectscited by 1,522
  3. 2021ViLT: Vision-and-Language Transformer Without Convolution or Region Supervisioncited by 1,224
  4. 2022PaLI: A Jointly-Scaled Multilingual Language-Image Modelcited by 762
  5. 2022OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Frameworkcited by 742
  6. 2022Detecting Twenty-thousand Classes using Image-level Supervisioncited by 613
  7. 2021ImageNet-21K Pretraining for the Massescited by 607
  8. 2022Socratic Models: Composing Zero-Shot Multimodal Reasoning with Languagecited by 513
  9. 2022Paint by Example: Exemplar-based Image Editing with Diffusion Modelscited by 489
  10. 2022InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutionscited by 479
  11. 2023Florence-2: Advancing a Unified Representation for a Variety of Vision Taskscited by 403
  12. 2023EVA-02: A Visual Representation for Neon Genesiscited by 377
38 more
  1. 2022ProcTHOR: Large-Scale Embodied AI Using Procedural Generationcited by 369
  2. 2021Pix2seq: A Language Modeling Framework for Object Detectioncited by 345
  3. 2023Scaling Open-Vocabulary Object Detectioncited by 335
  4. 2023OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generationcited by 324
  5. 2023RealFusion: 360deg Reconstruction of Any Object from a Single Imagecited by 305
  6. 2022Simple Open-Vocabulary Object Detection with Vision Transformerscited by 303
  7. 2022VOS: Learning What You Don't Know by Virtual Outlier Synthesiscited by 300
  8. 2021Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Conceptscited by 255
  9. 2021N"UWA: Visual Synthesis Pre-training for Neural visUal World creAtioncited by 253
  10. 2022Imagen Editor and EditBench: Advancing and Evaluating Text-Guided Image Inpaintingcited by 229
  11. 2024MANTIS: Interleaved Multi-Image Instruction Tuningcited by 212
  12. 2023Subject-Diffusion:Open Domain Personalized Text-to-Image Generation without Test-time Fine-tuningcited by 191
  13. 2023Cut and Learn for Unsupervised Object Detection and Instance Segmentationcited by 186
  14. 2022DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detectioncited by 176
  15. 2022Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AIcited by 169
  16. 2023Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuningcited by 168
  17. 2021Reviving Iterative Training with Mask Guidance for Interactive Segmentationcited by 162
  18. 2023InstructDiffusion: A Generalist Modeling Interface for Vision Taskscited by 162
  19. 2022Can Machines Help Us Answering Question 16 in Datasheets, and In Turn Reflecting on Inappropriate Content?cited by 150
  20. 2021Efficient Self-supervised Vision Transformers for Representation Learningcited by 149
  21. 2023PACO: Parts and Attributes of Common Objectscited by 146
  22. 2022Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detectioncited by 145
  23. 2022Seeing Beyond the Brain: Conditional Diffusion Model with Sparse Masked Modeling for Vision Decodingcited by 142
  24. 2024SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language Modelscited by 130
  25. 2021UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language Modelingcited by 122
  26. 2022ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehensioncited by 119
  27. 2021A Survey of Visual Transformerscited by 117
  28. 2022Language Models with Image Descriptors are Strong Few-Shot Video-Language Learnerscited by 117
  29. 2022CLIP Models are Few-shot Learners: Empirical Studies on VQA and Visual Entailmentcited by 113
  30. 2021Guided Integrated Gradients: An Adaptive Path Method for Removing Noisecited by 102
  31. 2022DualCoOp: Fast Adaptation to Multi-Label Recognition with Limited Annotationscited by 102
  32. 2023Contextual Object Detection with Multimodal Large Language Modelscited by 100
  33. 2021A Survey of Modern Deep Learning based Object Detection Modelscited by 97
  34. 2022Towards End-to-End Unified Scene Text Detection and Layout Analysiscited by 96
  35. 2021Spatial-Temporal Transformer for Dynamic Scene Graph Generationcited by 95
  36. 2022RelTR: Relation Transformer for Scene Graph Generationcited by 95
  37. 2022"This is my unicorn, Fluffy": Personalizing frozen vision-language representationscited by 95
  38. 2022High-Quality Entity Segmentationcited by 93

50 most cited of 542 All 542 (JSON)