Toward Intelligent Scene Augmentation for Context-Aware Object Placement and Sponsor-Logo Integration
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Abstract (arXiv)
Intelligent image editing increasingly relies on advances in computer vision, multimodal reasoning, and generative modeling. While vision-language models (VLMs) and diffusion models enable guided visual manipulation, existing work rarely ensures that inserted objects are contextually appropriate. We introduce two new tasks for advertising and digital media: (1) context-aware object insertion, which requires predicting suitable object categories, generating them, and placing them plausibly within the scene; and (2) sponsor-product logo augmentation, which involves detecting products and inserting correct brand logos, even when items are unbranded or incorrectly branded. To support these tasks, we build two new datasets with category annotations, placement regions, and sponsor-product labels.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 27 references that are in the map. First posted on arXiv 25 Dec 2025.
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References · 7
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
- 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,585
- 2022Flamingo: a Visual Language Model for Few-Shot Learningcited by 4,386
- 2023Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detectioncited by 3,332
- 2022InstructPix2Pix: Learning to Follow Image Editing Instructionscited by 2,611
- 2015VQA: Visual Question Answeringcited by 2,506
- 2019Deep Image Blendingcited by 62
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- 2026ProductConsistency: Improving Product Identity Preservation in Instruction-Based Image Editing via SFT and RL
- 2024LogoSticker: Inserting Logos into Diffusion Models for Customized Generation
- 2025Logo-VGR: Visual Grounded Reasoning for Open-world Logo Recognition
- 2023KAFA: Rethinking Image Ad Understanding with Knowledge-Augmented Feature Adaptation of Vision-Language Models
- 2026LU-500: A Logo Benchmark for Concept Unlearning
- 2024Add-it: Training-Free Object Insertion in Images With Pretrained Diffusion Models
- 2026Alterbute: Editing Intrinsic Attributes of Objects in Images
- 2025Preserving Product Fidelity in Large Scale Image Recontextualization with Diffusion Models