Update README.md

README.md

@@ -64,11 +64,11 @@ Referring Expression Generation (REG)—the task of producing a concise and unam
 1. **Data leakage in RefCOCO/RefCOCO+**, which raises concerns about evaluation contamination, especially for VLMs trained on MSCOCO.
 2. **Lack of spoken data**, despite the fact that real-world referring is often **real-time** and **spontaneous**, unlike written language, which benefits from planning and revision.
 
-**To address these gaps**, we introduce **RefOI**, a curated dataset built from the [OpenImages V7](https://storage.googleapis.com/openimages/web/index.html) Instance Segmentation validation set.
+To address these gaps, we introduce **RefOI**, a curated dataset built from the  [OpenImages V7](https://storage.googleapis.com/openimages/web/index.html)  Instance Segmentation validation set.
 
 **Key features:**
 - 1,485 real-world object instances, equally distributed across **COCO** (744) and **non-COCO** (741) classes.
-- Includes **single presence** and **co-occurence** images for each class.
+- Includes **single presence** and **co-occurrence** images for each class.
 - Each instance annotated with **3 written** and **2 spoken** human referring expressions.
 
 
@@ -78,12 +78,12 @@ Using RefOI, we evaluate several state-of-the-art VLMs and uncover **three tiers
 - **Redundancy**: Models include excessive or irrelevant details, violating principles of informativeness and efficiency.
 - **Misalignment**: Model preferences diverge from human pragmatics, favoring visual complexity over minimal spatial cues.
 
-Our results also highlight the inadequacy of standard automatic metrics (e.g., BLEU, CIDEr) and listener-based scores (e.g., REC), which fail to capture these pragmatic shortcomings—emphasizing the need for more cognitively grounded evaluation protocols.
-
 ![Overview](vlm-reg.png)
 
-## Dataset Structure
 
+## ## Dataset Schema and Split
+
+### Data Fields
 Each entry in the dataset contains the following fields:
 
 - `image`: The original image file.  
@@ -96,9 +96,12 @@ Each entry in the dataset contains the following fields:
 - `written_descriptions`: Three human‑typed referring expressions.  
 - `spoken_descriptions`: Two human‑spoken expressions (transcribed and optionally corrected by annotators).
 
-**Dataset Split:** 
-`single_presence`: `co_occurence` = 1.
-`co_occurence`: `co_occurence` > 1.
+### Dataset Split
+- `single_presence` (`co_occurrence = 1`):  
+  Only one object of the target class appears (no same‑class distractors in the image).
+
+- `co_occurrence` (`co_occurrence > 1`):  
+  Multiple objects of the same class appear in the image, introducing potential referential ambiguity.
 
 
 ## Usage
@@ -106,13 +109,13 @@ Each entry in the dataset contains the following fields:
 ```python
 from datasets import load_dataset
 
-# COCO class
-ds_coco = load_dataset("Seed42Lab/RefOI", split="single_presence")
-# non-COCO class
-ds_oi = load_dataset("Seed42Lab/RefOI", split="co_occurence")
+# only one object of the class
+ds_single = load_dataset("Seed42Lab/RefOI", split="single_presence")
+# multiple objects of the class
+ds_multi = load_dataset("Seed42Lab/RefOI", split="co_occurrence")
 
-print(ds_coco[0])
-print(ds_oi[0])
+print(ds_single[0])
+print(ds_multi[0])
 ```
 
 
@@ -149,7 +152,7 @@ Model performance under different **Instr.** (Instruction) settings: **Dft.** (D
 
 ## Recommended Use of Our Dataset
 
-The `RefOI` dataset is designed for fine-grained REG/REC analysis. It distinguishes between **COCO** and **non-COCO classes**, and between scenes with **single presence vs. co-occurence** of the same class. 
+The `RefOI` dataset is designed for fine-grained REG/REC analysis. It distinguishes between **COCO** and **non-COCO classes**, and between scenes with **single presence vs. co-occurrence** of the same class. 
 We encourage users to leverage these distinctions for deeper insights and invite community contributions to expand non-COCO annotations.