Summary of Weatherdg: Llm-assisted Diffusion Model For Procedural Weather Generation in Domain-generalized Semantic Segmentation, by Chenghao Qian et al.
WeatherDG: LLM-assisted Diffusion Model for Procedural Weather Generation in Domain-Generalized Semantic Segmentation
by Chenghao Qian, Yuhu Guo, Yuhong Mo, Wenjing Li
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed WeatherDG approach generates realistic and diverse driving-screen images through the collaboration of two foundation models: Stable Diffusion (SD) and Large Language Model (LLM). SD is fine-tuned with source data to align generated samples with real-world driving scenarios. LLM generates procedural prompts that enrich scenario descriptions, enabling SD to create more detailed images. A balanced generation strategy encourages SD to generate high-quality objects under various weather conditions. This method improves the generalization ability of existing models by adapting them with synthetic data. Experiments on three datasets demonstrate significant improvements in segmentation performance for state-of-the-art models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to teach a computer to recognize different things in images, like pedestrians or cars. The problem is that training computers requires lots and lots of examples, but it’s hard to gather all those examples. A team of researchers came up with an idea: what if we can generate fake images that are similar to the real ones? They created a system called WeatherDG that uses two types of models: Stable Diffusion (SD) and Large Language Model (LLM). The SD model is trained on lots of data, then it’s fine-tuned to match the kinds of images you want it to generate. The LLM model helps by generating descriptions of what should be in those images. This system can create lots of different images, which helps train computers to recognize things better. |
Keywords
» Artificial intelligence » Diffusion » Generalization » Large language model » Synthetic data
