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Gligen: Open-set grounded text-to-image genera- tion

3 Pith papers cite this work. Polarity classification is still indexing.

3 Pith papers citing it

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cs.CV 2 cs.AI 1

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representative citing papers

Visual Instruction Tuning

cs.CV · 2023-04-17 · unverdicted · novelty 7.0

LLaVA is trained on GPT-4 generated visual instruction data to achieve 85.1% relative performance to GPT-4 on synthetic multimodal tasks and 92.53% accuracy on Science QA.

PhyDrawGen: Physically Grounded Diagram Generation from Natural Language

cs.AI · 2026-05-28 · unverdicted · novelty 5.0

PhyDrawGen is a neuro-symbolic pipeline that extracts typed scene graphs via LLM, converts them to physically constrained PSLGs via deterministic solver, and refines via fine-tuned Qwen-VL, claiming superior performance over GPT-5-image and Gemini models on 1,449 physics problems.

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Showing 3 of 3 citing papers.

  • Visual Instruction Tuning cs.CV · 2023-04-17 · unverdicted · none · ref 30

    LLaVA is trained on GPT-4 generated visual instruction data to achieve 85.1% relative performance to GPT-4 on synthetic multimodal tasks and 92.53% accuracy on Science QA.

  • MaskAttn-SDXL: Controllable Region-Level Text-To-Image Generation cs.CV · 2025-09-18 · conditional · none · ref 10

    MaskAttn-SDXL adds learned binary token-location gates before softmax in SDXL cross-attention, improving compositional consistency on multi-object prompts.

  • PhyDrawGen: Physically Grounded Diagram Generation from Natural Language cs.AI · 2026-05-28 · unverdicted · none · ref 20

    PhyDrawGen is a neuro-symbolic pipeline that extracts typed scene graphs via LLM, converts them to physically constrained PSLGs via deterministic solver, and refines via fine-tuned Qwen-VL, claiming superior performance over GPT-5-image and Gemini models on 1,449 physics problems.