mlsanityData to 7 Oct 2026

OPA: Object Placement Assessment Dataset

arXiv 2107.01889 ↗First posted 5 Jul 2021cs.CVcited by 28

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Abstract (arXiv)

Image composition aims to generate realistic composite image by inserting an object from one image into another background image, where the placement (e.g., location, size, occlusion) of inserted object may be unreasonable, which would significantly degrade the quality of the composite image. Although some works attempted to learn object placement to create realistic composite images, they did not focus on assessing the plausibility of object placement. In this paper, we focus on object placement assessment task, which verifies whether a composite image is plausible in terms of the object placement. To accomplish this task, we construct the first Object Placement Assessment (OPA) dataset consisting of composite images and their rationality labels. We also propose a simple yet effective baseline for this task. Dataset is available at https://github.com/bcmi/Object-Placement-Assessment-Dataset-OPA.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). Builds on 16 of its 29 references that are in the map. First posted on arXiv 5 Jul 2021.

Builds on

16 of 29 references.

References · 16

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  3. 2020Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentationcited by 595
  4. 2019DoveNet: Deep Image Harmonization via Domain Verificationcited by 134
  5. 2017Deep Image Harmonizationcited by 120
  6. 2018Modeling Visual Context is Key to Augmenting Object Detection Datasetscited by 106
  7. 2019InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pastingcited by 95
  8. 2018ST-GAN: Spatial Transformer Generative Adversarial Networks for Image Compositingcited by 90
  9. 2021Making Images Real Again: A Comprehensive Survey on Deep Image Compositioncited by 63
  10. 2019Learning to Generate Synthetic Data via Compositingcited by 52
  11. 2017Synthesizing Training Data for Object Detection in Indoor Scenescited by 48
  12. 2018Learning to Segment via Cut-and-Pastecited by 42
4 more
  1. 2018Context-Aware Synthesis and Placement of Object Instancescited by 41
  2. 2017Where and Who? Automatic Semantic-Aware Person Compositioncited by 21
  3. 2019Data Augmentation for Object Detection via Progressive and Selective Instance-Switchingcited by 18
  4. 2018What and Where: A Context-based Recommendation System for Object Insertioncited by 14

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 6; 2024: 7; 2025: 6; 2026: 6.

28 papers.

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  1. 2023TF-ICON: Diffusion-Based Training-Free Cross-Domain Image Compositioncited by 182
  2. 2024Paint by Inpaint: Learning to Add Image Objects by Removing Them Firstcited by 39
  3. 2022Learning Object Placement via Dual-path Graph Completioncited by 20
  4. 2023Iterative Multi-granular Image Editing using Diffusion Modelscited by 20
  5. 2024PrimeComposer: Faster Progressively Combined Diffusion for Image Composition with Attention Steeringcited by 20
  6. 2024Thinking Outside the BBox: Unconstrained Generative Object Compositingcited by 19
  7. 2023SmartMask: Context Aware High-Fidelity Mask Generation for Fine-grained Object Insertion and Layout Controlcited by 18
  8. 2023TopNet: Transformer-based Object Placement Network for Image Compositingcited by 16
  9. 2022Fast Object Placement Assessmentcited by 12
  10. 2024Text2Place: Affordance-aware Text Guided Human Placementcited by 11
  11. 2023Scene-aware Human Pose Generation using Transformercited by 9
  12. 2025PlaceIt3D: Language-Guided Object Placement in Real 3D Scenescited by 8
16 more
  1. 2023Foreground Object Search by Distilling Composite Image Featurecited by 6
  2. 2021SAC-GAN: Structure-Aware Image Compositioncited by 5
  3. 2024Imagining the Unseen: Generative Location Modeling for Object Placementcited by 4
  4. 2024DiffPop: Plausibility-Guided Object Placement Diffusion for Image Compositioncited by 3
  5. 2025BOOTPLACE: Bootstrapped Object Placement with Detection Transformerscited by 3
  6. 2025Object Placement for Anythingcited by 3
  7. 2024Deep Image Composition Meets Image Forgerycited by 2
  8. 2025Disharmony: Forensics using Reverse Lighting Harmonizationcited by 1
  9. 2025CAD2DMD-SET: Synthetic Generation Tool of Digital Measurement Device CAD Model Datasets for fine-tuning Large Vision-Language Modelscited by 1
  10. 2025Beyond Realism: Learning the Art of Expressive Composition with StickerNetcited by 0
  11. 2026VSDiffusion: Taming Ill-Posed Shadow Generation via Visibility-Constrained Diffusioncited by 0
  12. 2026CatalogStitch: Dimension-Aware and Occlusion-Preserving Object Compositing for Catalog Image Generationcited by 0
  13. 2026HiddenObjects: Scalable Diffusion-Distilled Spatial Priors for Object Placementcited by 0
  14. 2026Learning to Understand Body Language from Flight through Robust 3D Avatar Placingcited by 0
  15. 2026presto: Efficient, Training-free, and Open-world Object Placement via Imaginary Searchcited by 0
  16. 2026Hybrid Generative-Discriminative Object Placementcited by 0