Update README to align with the Read the Object paper

README.md

@@ -8,6 +8,7 @@ language:
 tags:
 - referring-expression-comprehension
 - visual-grounding
+- text-object-grounding
 - ocr
 - scene-text
 - autonomous-driving
@@ -19,15 +20,34 @@ size_categories:
 
 # Read The Object
 
-**Read The Object** is a Referring Expression Comprehension (REC) dataset that
-grounds *scene text* to the physical object it is written on, built from
-driving-scene imagery. Each sample pairs a natural-language referring
-expression (built from an object's category and the text detected on it)
-with the object's bounding box.
-
-The dataset is organized by source — the first category is **nuScenes**,
-with more (starting with COCO) planned. Each source lives in its own
-top-level folder with the same layout:
+**Read The Object** is the official data release accompanying the paper
+*"Read the Object: A Dataset and Task for Text-Grounded Object Understanding
+in Real-World Images"* (Chowdhury, Park, Le, Chao, Hayat, Porikli, Mahajan —
+The Ohio State University, Qualcomm AI Research, Boston University, York
+University, Vector Institute for AI).
+
+Objects in the real world are often distinguished not by visual appearance
+alone but by the text printed on them — a traffic sign's route number, a
+jersey number, a product label. The paper introduces **text–object
+grounding**: the task of associating a piece of scene text with the exact
+object instance it appears on, and shows that current object detectors,
+OCR systems, and vision-language models all struggle at it despite doing
+well on text recognition or object localization in isolation. The full
+benchmark combines images from **OpenImages V5**, **MS COCO**, and
+**nuScenes** (6,000 images, 25,208 referring-expression samples, 34,085
+object instances, 137,246 text instances) and defines six complementary
+tasks: Referring Expression Comprehension, Text–Object Grounding, OCR,
+Text-Region Grounding, Visual Question Answering, and Multi-Text
+Captioning.
+
+**This repo currently contains only the nuScenes source category** of that
+benchmark (the OpenImages V5 and COCO portions, plus the other five tasks,
+are not yet part of this HF release — more sources will be added
+incrementally). The data here covers the **Referring Expression
+Comprehension** task.
+
+The dataset is organized by source, so future additions slot in as sibling
+folders under the same layout:
 
 ```
 read_the_object/
@@ -58,24 +78,39 @@ CAM_FRONT, CAM_BACK_LEFT, CAM_BACK_RIGHT).
 | `sample_type` | string | `single_text_single_object`, `multi_text_single_object`, or `disambiguated_same_category` |
 | `attribute` | string (optional) | present only for `disambiguated_same_category` samples — a distinguishing visual/spatial attribute added because another object of the *same category* in the same image shares the same text (e.g. `"left blue"` in `"'23' written on left blue gate"`) |
 
-### Sample types
-
-- **`single_text_single_object`** — one object, one piece of text.
-- **`multi_text_single_object`** — one object with multiple pieces of text on
-  it; one sample is generated per count *k* = 1..5 using the *k* spatially
-  closest texts, so the same object contributes several samples of
-  increasing expression detail.
-- **`disambiguated_same_category`** — used only when two or more objects of
-  the **same category** in the same image share the exact same text string
-  (so category + text alone would be ambiguous). A human-annotated
-  distinguishing attribute (color/material/shape and/or left/right/top/center)
-  is folded into the expression: `'{text}' written on {attribute} {category}`.
+### Sample types (paper's scenario taxonomy)
+
+The paper defines four scene-text-to-object scenarios (Sec. 3.1); this
+release covers three of them:
+
+- **`single_text_single_object`** — paper's *Single-text → Single-object*:
+  one object, one piece of text.
+- **`multi_text_single_object`** — paper's *Multi-text → Single-object*:
+  one object with multiple pieces of text on it; one sample is generated
+  per count *k* = 1..5 using the *k* spatially closest texts, so the same
+  object contributes several samples of increasing expression detail.
+- **`disambiguated_same_category`** — paper's *Single-text → Multi-object*:
+  used when two or more objects of the **same category** in the same image
+  share the exact same text string (so category + text alone would be
+  ambiguous). Following the paper's attribute generation and
+  disambiguation process, a human-annotated distinguishing attribute
+  (color, material, shape, and/or left/right/top/center position) is
+  folded into the expression: `'{text}' written on {attribute} {category}`.
   Cross-category sharing (e.g. a gate and a building both labeled `"303"`)
-  is **not** treated as ambiguous, since the category name itself already
-  disambiguates those.
+  is **not** treated as ambiguous here, since the category name itself
+  already disambiguates those.
+- *Multi-instance ↔ Multi-text* (the paper's fourth scenario, where
+  several objects each carry several text instances) is not yet
+  represented as its own sample type in this nuScenes release.
 
 ## Construction pipeline
 
+This mirrors the paper's general data curation pipeline (Fig. 4: Data
+Collection → Annotation Curation → Processing → Attribute Curation →
+Quality Control → Data Taxonomy), instantiated for nuScenes using 3D box
+projection in place of the open-vocabulary detectors/segmentation models
+used for the OpenImages/COCO portions:
+
 1. **OCR**: EasyOCR and PaddleOCR run independently on every camera frame.
    A text box is kept only if both models agree exactly on the recognized
    string, both confidences exceed 0.7, and the IoU between their two boxes
@@ -87,20 +122,22 @@ CAM_FRONT, CAM_BACK_LEFT, CAM_BACK_RIGHT).
 3. **3D object association**: each nuScenes 3D object annotation is
    projected into the camera image (ego/global frame transforms + camera
    intrinsics); a text box is linked to an object if the object's projected
-   silhouette contains it.
+   silhouette contains it — the dataset's analog of the paper's
+   detector/segmentation-based object candidates.
 4. **Manual review**: every image with 2+ text detections was manually
    reviewed and corrected (text/object boxes fixed, text-to-object links
    verified) via a custom annotation tool.
 5. **Categorization**: free-text object names were mapped to a fixed
    category taxonomy (`vehicle.*`, `signage.*`, `structure.*`, `barrier.*`,
    `misc.*`).
-6. **REC sample construction**: samples generated per the rules above, with
-   every `combined_text_box` guaranteed to be fully contained in its
+6. **REC sample construction**: samples generated per the scenarios above,
+   with every `combined_text_box` guaranteed to be fully contained in its
    `obj_box`.
-7. **Ambiguity resolution**: same-category ties (a shared text string across
-   2+ objects of the same category) were manually annotated with a
-   distinguishing attribute so a unique referring expression is always
-   possible.
+7. **Attribute generation and disambiguation**: same-category ties (a
+   shared text string across 2+ objects of the same category) were
+   manually annotated with a distinguishing attribute so a unique
+   referring expression is always possible, per the paper's Sec. 3.1
+   attribute-curation process.
 
 ## Source data & license
 
@@ -113,7 +150,16 @@ is subject to nuScenes' own terms — see https://www.nuscenes.org/terms-of-use.
 
 ## Citation
 
-If you use this dataset, please cite nuScenes:
+If you use this dataset, please cite:
+
+```
+@inproceedings{chowdhury2026readtheobject,
+  title={Read the Object: A Dataset and Task for Text-Grounded Object Understanding in Real-World Images},
+  author={Chowdhury, Arpita and Park, Hyojin and Le, Hoang and Chao, Wei-Lun and Hayat, Munawar and Porikli, Fatih and Mahajan, Shweta},
+}
+```
+
+and, for the underlying imagery, nuScenes:
 
 ```
 @inproceedings{nuscenes2019,