|
--- |
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language: en |
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license: mit |
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size_categories: |
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- 1K<n<10K |
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task_categories: |
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- image-to-text |
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- visual-question-answering |
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tags: |
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- spatial |
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- dialogue |
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- visual-grounding |
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dataset_info: |
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features: |
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- name: instance_id |
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dtype: int32 |
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- name: scene_key |
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dtype: string |
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- name: listener_view_image |
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dtype: image |
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- name: speaker_view_image |
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dtype: image |
|
- name: human_speaker_message |
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dtype: string |
|
- name: speaker_elapsed_time |
|
dtype: float32 |
|
- name: positions |
|
dtype: string |
|
- name: listener_target_bbox |
|
dtype: string |
|
- name: listener_distractor_0_bbox |
|
dtype: string |
|
- name: listener_distractor_1_bbox |
|
dtype: string |
|
- name: speaker_target_bbox |
|
dtype: string |
|
- name: speaker_distractor_0_bbox |
|
dtype: string |
|
- name: speaker_distractor_1_bbox |
|
dtype: string |
|
- name: human_listener_message |
|
dtype: string |
|
- name: listener_elapsed_time |
|
dtype: float32 |
|
- name: type |
|
dtype: string |
|
splits: |
|
- name: validation |
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num_bytes: 6561385869.0 |
|
num_examples: 2970 |
|
download_size: 6419735381 |
|
dataset_size: 6561385869.0 |
|
configs: |
|
- config_name: default |
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data_files: |
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- split: validation |
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path: data/validation-* |
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--- |
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|
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# Dataset Card for Multi-Agent Referential Communication Dataset |
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|
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<div align="center"> |
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<img src="assets/main.png" alt="Example scene" width="400"/> |
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|
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*Example scene showing the speaker (left) and listener (right) views.* |
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</div> |
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|
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## Dataset Details |
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|
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### Dataset Description |
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|
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This dataset contains spatial dialogue data for multi-agent referential communication tasks in 3D environments. It includes pairs of images showing speaker and listener views within photorealistic indoor scenes, along with natural language descriptions of target object locations. |
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|
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The key feature of this dataset is that it captures communication between two agents with different physical perspectives in a shared 3D space. Each agent has their own unique viewpoint of the scene, requiring them to consider each other's perspectives when generating and interpreting spatial references. |
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|
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### Dataset Summary |
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|
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- **Size**: 2,970 dialogue instances across 1,485 scenes |
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- **Total Scenes Generated**: 27,504 scenes (24,644 train, 1,485 validation, 1,375 test) |
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- **Task Type**: Referential communication between embodied agents |
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- **Language(s)**: English |
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- **License**: MIT |
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- **Curated by**: University of California, Berkeley |
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- **Time per Task**: Median 33.0s for speakers, 10.5s for listeners |
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|
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## Dataset Structure |
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|
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Each instance contains: |
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- Speaker view image (1024x1024 resolution) |
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- Listener view image (1024x1024 resolution) |
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- Natural language referring expression from human speaker |
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- Target object location |
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- Listener object selection |
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- Scene metadata including: |
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- Agent positions and orientations |
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- Referent placement method (random vs adversarial) |
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- Base environment identifier |
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|
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## Dataset Creation |
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|
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1. Base environments from ScanNet++ (450 high-quality 3D indoor environments) |
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2. Scene generation process: |
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- Place two agents with controlled relative orientations (0° to 180°) |
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- Place 3 referent objects using either random or adversarial placement |
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- Render images from each agent's perspective |
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- Apply quality filtering using GPT-4V |
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## Citation |
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**BibTeX:** |
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``` |
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@article{tang2024grounding, |
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title={Grounding Language in Multi-Perspective Referential Communication}, |
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author={Tang, Zineng and Mao, Lingjun and Suhr, Alane}, |
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journal={EMNLP}, |
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year={2024} |
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} |
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``` |
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|
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## Dataset Card Contact |
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|
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Contact the authors at terran@berkeley.edu |
