DOGS: Design-Space Sampling for Prompt-Driven Logo Generation

arXiv:2610.10760 · cs.CV · Submitted 2026-10-07 · Read on arXiv

cs.CV

Submitted: 2026-10-07

Updated: 2026-10-07

Terminology

• Generative Model — 6× in this paper · explained in 2 episodes
• Generative Models — 6× in this paper · explained in 2 episodes
• Prompt Engineering — 4× in this paper · explained in 4 episodes
• Reinforcement Learning — 4× in this paper · explained in 4 episodes
• Action Space — 3× in this paper · explained in 1 episode
• Diffusion Model — 3× in this paper · explained in 8 episodes
• Redundancy — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Actor — 2× in this paper · explained in 3 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Credit assignment — 2× in this paper · explained in 3 episodes
• Deep Reinforcement Learning — 2× in this paper · explained in 2 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Pruning — 2× in this paper · explained in 4 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Action Groups — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Frozen model — 1× in this paper · explained in 3 episodes
• Human-in-the-Loop — 1× in this paper · explained in 3 episodes
• LLM judge — 1× in this paper · explained in 2 episodes
• Nadia — 1× in this paper · explained in 2 episodes
• Text-to-SQL — 1× in this paper · explained in 3 episodes

Sources

• Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation
• Deep reinforcement learning from human preferences
• A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models
• The Large Labelled Logo Dataset (L3D): A Multipurpose and Hand-Labelled Continuously Growing Dataset
• Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
• Optimizing Prompts for Text-to-Image Generation
• LayoutDM: Discrete Diffusion Model for Controllable Layout Generation
• Image Generation from Scene Graphs
• Reward-Agnostic Prompt Optimization for Text-to-Image Diffusion Models
• GRAINS: Generative Recursive Autoencoders for INdoor Scenes
• IDEA-Bench: How Far are Generative Models from Professional Designing?
• Trajectory balance: Improved credit assignment in GFlowNets
• Dynamic Prompt Optimizing for Text-to-Image Generation
• Illuminating search spaces by mapping elites
• Training language models to follow instructions with human feedback
• Grammar-Aligned Decoding
• Flexible and Efficient Grammar-Constrained Decoding
• Best Prompts for Text-to-Image Models and How to Find Them
• Abstract Syntax Networks for Code Generation and Semantic Parsing
• Direct Preference Optimization: Your Language Model is Secretly a Reward Model

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