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| license: cc-by-nc-4.0 |
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| # 📢 SRefCOCO: A Large-Scale Multi-Modal Visual Grounding Dataset |
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| ## 📖 Introduction |
| Visual grounding aims to locate target regions in an image based on natural language instructions. However, mainstream paradigms are largely restricted to "text-image" bimodal interactions, severely limiting their application flexibility in highly dynamic real-world physical scenarios. |
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| **SRefCOCO** breaks this traditional text-only constraint by introducing a novel **Speech-Text-Image** triplet dataset. It is designed to endow embodied AI models with end-to-end "listen, read, and look" perceptual capabilities. |
| The dataset is built upon the classic visual grounding datasets (e.g., RefCOCO, RefCOCO+, RefCOCOg) and extends them with robust acoustic instructions. |
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| * **Images:** Standard RGB images from the COCO dataset. |
| * **Texts:** Natural language referring expressions. |
| * **Speech (Audio):** 16kHz `.wav` audio files generated via Edge-TTS and heavily augmented to simulate complex real-world physical environments. |
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| ## 🚀 How to Use |
| You can easily load the dataset using the Hugging Face `datasets` library: |
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| ```python |
| from datasets import load_dataset |
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| # Load the entire dataset |
| dataset = load_dataset("xutao2025/SRefcoco") |
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| # Print the first training sample |
| print(dataset['train'][0]) |
