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README.md

@@ -17,16 +17,18 @@ task_categories:
 - question-answering
 ---
 
-# EPFL Smart Kitchen: Lemonade benchmark
+# 🍋 EPFL-Smart-Kitchen: Lemonade benchmark
 
-## Abstract
+![title](media/title.svg)
+
+## 📚 Introduction
  we introduce Lemonade: **L**anguage models **E**valuation of **MO**tion a**N**d **A**ction-**D**riven **E**nquiries.
  Lemonade consists of 36,521 closed-ended QA pairs linked to egocentric video clips, categorized in three groups and six subcategories.
  18,857 QAs focus on behavior understanding, leveraging the rich ground truth behavior annotations of the EPFL-Smart Kitchen to interrogate models about perceived actions (Perception) and reason over unseen behaviors (Reasoning).
  8,210 QAs involve longer video clips, challenging models in summarization (Summarization) and session-level inference (Session properties).
  The remaining 9,463 QAs leverage the 3D pose estimation data to infer hand shapes, joint angles (Physical attributes), or trajectory velocities (Kinematics) from visual information.
 
-## Content
+## 💾 Content
 The current repository contains all egocentric videos recorded in the EPFL-Smart-Kitchen-30 dataset. You can download the rest of the dataset at ... and ... .
 
 ### Repository structure
@@ -34,10 +36,10 @@ The current repository contains all egocentric videos recorded in the EPFL-Smart
 ```
 Lemonade
 ├── MCQs
-    └── lemonade_benchmark.csv
+|   └── lemonade_benchmark.csv
 ├── videos
-    ├── YH2002_2023_12_04_10_15_23_hololens.mp4
-    └── ..
+|   ├── YH2002_2023_12_04_10_15_23_hololens.mp4
+|   └── ..
 └── README.md
 ```
 
@@ -57,15 +59,19 @@ Lemonade
 
 > We refer the reader to the associated publication for details about data processing and tasks description.
 
+## 📈 Evaluation results
+
 
-## Usage
+## 🌈 Usage
 The evaluation of the benchmark can be done through the following github repository: ... . 
 
-## Publications
+
+
+## 🌟 Citations
 cite arxiv paper
 
-## Acknowledgments
+## ❤️ Acknowledgments
 We thank Andy Bonnetto for the design of the dataset and  Matea Tashkovska for the adaptation of the evaluation platform. </br>
 We thank members of the Mathis Group for Computational Neuroscience \& AI (EPFL) for their feedback throughout the project.
 This work was funded by EPFL, Swiss SNF grant (320030-227871), Microsoft Swiss Joint Research Center and a Boehringer Ingelheim Fonds PhD stipend (H.Q.).
-We are grateful to the Brain Mind Institute for providing funds for hardware and to the Neuro-X Institute for providing funds for services.
+We are grateful to the Brain Mind Institute for providing funds for hardware and to the Neuro-X Institute for providing funds for services.