# Gradio examples = [ [ "Where can the driver see the car speed in this image? Please output segmentation mask.", "./resources/imgs/example1.jpg", ], [ "Can you segment the food that tastes spicy and hot?", "./resources/imgs/example2.jpg", ], [ "Assuming you are an autonomous driving robot, what part of the diagram would you manipulate to control the direction of travel? Please output segmentation mask and explain why.", "./resources/imgs/example1.jpg", ], [ "What can make the woman stand higher? Please output segmentation mask and explain why.", "./resources/imgs/example3.jpg", ], ] output_labels = ["Segmentation Output"] title = "LISA: Reasoning Segmentation via Large Language Model" description = """ This is the online demo of LISA... \n If multiple users are using it at the same time, they will enter a queue, which may delay some time. \n **Note**: **Different prompts can lead to significantly varied results**. \n **Note**: Please try to **standardize** your input text prompts to **avoid ambiguity**, and also pay attention to whether the **punctuations** of the input are correct. \n **Usage**:
(1) To let LISA **segment something**, input prompt like: "Can you segment xxx in this image?", "What is xxx in this image? Please output segmentation mask.";
(2) To let LISA **output an explanation**, input prompt like: "What is xxx in this image? Please output segmentation mask and explain why.";
(3) To obtain **solely language output**, you can input like what you should do in current multi-modal LLM (e.g., LLaVA).
Hope you can enjoy our work! """ demo_parameters = """## Model configuration parameters\n The demo uses these parameters: """ article = """

"""