Update README.md

README.md

@@ -1,3 +1,111 @@
----
-license: mit
----
+---
+license: mit
+task_categories:
+- visual-question-answering
+language:
+- en
+tags:
+- spatial
+- multimodal
+size_categories:
+- 1K<n<10K
+---
+# Dataset Card for TOPVIEWRS
+
+<!-- Provide a quick summary of the dataset. -->
+The TOPVIEWRS (Top-View Reasoning in Space) benchmark is a multimodal benchmark intended to evaluate the spatial reasoning ability of current Vision-Language Models. 
+It consists of 11,384 multiple-choice questions with either realistic or semantic top-view map as visual input, across 4 perception and reasoning tasks with different levels of complexity.
+For details, please refer to the [project page](https://topviewrs.github.io/) and the [paper](https://arxiv.org/pdf/2406.02537). 
+
+## Dataset Description
+
+- **Homepage/Repository:** [https://topviewrs.github.io/](https://topviewrs.github.io/)
+- **Paper:** [TOPVIEWRS: Vision-Language Models as Top-View Spatial Reasoners](https://arxiv.org/pdf/2406.02537)
+- **Point of Contact:** [cl917@cam.ac.uk](mailto:cl917@cam.ac.uk)
+
+## Dataset Details
+
+### Dataset Features
+
+<!-- Provide a longer summary of what this dataset is. -->
+- **Multi-Scale Top-View Maps**: Multi-scale top-view maps of single rooms and full houses add divergence in the granularity of the entities (objects or rooms) in spatial reasoning. 
+- **Realistic Environmental Scenarios with Rich Object Sets**: Real-world environments from indoor scenes, with 80 objects per scene on average.
+- **Structured Question Framework**:  Four tasks including 9 sub-tasks in total, allowing for a fine-grained evaluation and analysis of models’ capabilities from various perspectives and levels of granularity.
+
+### Dataset Statistics
+
+The TOPVIEWRS evaluation dataset comprises a total of 11,384 multiple-choice questions after human verification, with
+5,539 questions associated with realistic top-view
+maps, and 5,845 with semantic top-view maps.
+The choices are uniformly distributed over choices A(25.5%), B (24.6%), C (24.5%) and D (25.4%).
+
+The maps are collected from Matterport3D dataset, which includes 90 building-scale scenes with instance-level semantic and room-level region annotations in 3D meshes. 
+We filter these to exclude multi-floor and low-quality scenes, selecting 7 scenes with an average of 80 objects and 12 rooms each.
+
+**Note**: *We only release part of the benchmark (2 different scenarios covering all the tasks of the benchmark) in this dataset card to avoid data contamination. 
+For full access to the benchmark, please get in touch with [Chengzu Li](chengzu-li.github.io) via email: [cl917@cam.ac.uk](mailto:cl917@cam.ac.uk)*
+
+### Uses
+
+```
+data = load_datasets(
+        "cl917/topviewrs",
+        trust_remote_code=True,
+        map_type=MAP_TYPE,
+        task_split=TASK_SPLIT,
+        image_save_dir=IMAGE_SAVE_DIR
+)
+```
+
+To use the dataset, you have to specify several arguments when calling `load_datasets`:
+  - `map_type`: should be one of `['realistic', 'semantic']`
+  - `task_split`: should be one of `['top_view_recognition', 'top_view_localization', 'static_spatial_reasoning', 'dynamic_spatial_reasoning']`
+  - `image_save_dir`: specify the directory where you would like the images to be saved
+
+### Data Instances
+
+For example an instance from the `top_view_recognition` task is:
+
+```
+{
+  'index': 0,
+  'scene_id': '17DRP5sb8fy',
+  'question': 'Which of the following objects are in the room?',
+  'choices': ['shelving', 'bed', 'toilet', 'seating'],
+  'labels': ['bed'],
+  'choice_type': '<OBJECT>',
+  'map_path': '<IMAGE_SAVE_DIR>/data/mp3d/17DRP5sb8fy/semantic/17DRP5sb8fy_0_0.png',
+  'question_ability': 'object_recognition'
+}
+```
+
+### Data Fields
+
+Every example has the following fields
+  - `idx`: an `int` feature
+  - `scene_id`: a `string` feature, unique id for the scene from Matterport3D
+  - `question`: a `string` feature
+  - `choices`: a sequence of `string` feature, choices for multiple-choice question
+  - `labels`: a sequence of `string` feature, answer for multiple-choice question. The label's position in the `choices` can be used to determine whether it is A, B, C, or D.
+  - `choice_type`: a `string` feature
+  - `map_path`: a `string` feature, the path of the input image
+  - `question_ability`: a `string` feature, sub-tasks for fine-grained evaluation and analysis
+
+For `dynamic_spatial_reasoning` task, there would be one more data field: 
+  - `reference_path`: a sequence of `list[int]` feature, the coordinate sequence of the navigation path on the top-view map. 
+
+
+## Citation
+
+```
+@misc{li2024topviewrs,
+      title={TopViewRS: Vision-Language Models as Top-View Spatial Reasoners},
+      author={Chengzu Li and Caiqi Zhang and Han Zhou and Nigel Collier and Anna Korhonen and Ivan Vulić},
+      year={2024},
+      eprint={2406.02537},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->