Update README.md

README.md

@@ -1,5 +1,65 @@
----
-license: other
-license_name: license
-license_link: LICENSE
----
+---
+license: other
+license_name: license
+license_link: LICENSE
+---
+
+# Qualcomm Interactive Cooking Dataset
+
+## Description
+
+The Qualcomm Interactive Cooking Dataset is designed to evaluate the ability of multi-modal LLMs to provide step-by-step instructions, focusing on the cooking domain.
+
+## Dataset Details
+
+The Qualcomm Interactive Cooking Dataset includes step-by-step instructions and feedback pairs. The videos are from the [CaptainCook4D dataset](https://captaincook4d.github.io/captain-cook/) - licensed under Apache 2.0.
+
+## Dataset Collection Process
+The text annotations and timestamps are manually annotated.
+
+## Data Format
+
+The dataset consists of six files with the following names:
+`<set>\_<split>.json`
+
+Where, `set = {“simple”, “advanced”}` and `split={“train”, “validation”, “test”}`.
+
+The json files store a list of dictionaries corresponding to a video from the CaptainCook4D dataset. The dictionary contains the following fields:
+
+1. `video\_id`: The identifier of the video from CaptainCook4D.
+2. `activity\_name`: The name of the recipe in the video.
+3. `output\_texts`: The instruction and feedback messages.
+4. `output\_timestamps`: The timestamp corresponding to each instruction and feedback message.
+5. `output\_types`: The type of each message in output\_texts classified as described in Appendix B.
+6. `output\_actions`: The action that the user performed corresponding to each feedback message.
+7. `remaining\_plan`: The remaining step-by-step plan before each instruction or feedback message.
+
+## Dataset license
+
+This dataset is intended for research purposes only.
+
+Data License Agreement - Research Use
+
+## Dataset Citation Instructions
+
+Please cite our paper if you use this dataset in your research.
+```
+@inproceedings{interactivecooking,
+ title      = {Can Multi-Modal LLMs Provide Live Step-by-Step Task Guidance?},
+ author     = {Apratim Bhattacharyya and Bicheng Xu and Sanjay Haresh and Reza Pourreza and Litian Liu and Sunny Panchal and Leonid Sigal and Roland Memisevic},
+ booktitle  = {NeurIPS},
+ year       = {2025}
+}
+```
+
+## Qualcomm AI Research
+
+At Qualcomm AI Research, we are advancing AI to make its core capabilities – perception, reasoning, and action – ubiquitous across devices. Our mission is to make breakthroughs in fundamental AI research and scale them across industries. By bringing together some of the best minds in the field, we’re pushing the boundaries of what’s possible and shaping the future of AI.
+
+Qualcomm AI Research continues to invest in and support deep-learning research in computer vision. The publication of this dataset for use by the AI research community is one of our many initiatives.
+
+Find out more about [Qualcomm AI Research](https://developer.qualcomm.com/forums/software/ai-research-datasets).
+
+For any questions or technical support, please contact us at research.datasets@qti.qualcomm.com
+
+*Qualcomm AI Research is an initiative of Qualcomm Technologies, Inc.*