Create README.md

README.md

@@ -0,0 +1,88 @@
+ ---
+license: apache-2.0
+task_categories:
+  - visual-question-answering
+tags:
+  - video
+  - spatial-intelligence
+  - recall
+  - benchmark
+language:
+  - en
+---
+
+# VSI-SUPER-Recall
+
+**[Website](https://vision-x-nyu.github.io/cambrian-s.github.io/)** | **[Paper](https://arxiv.org/abs/2025)** | **[GitHub](https://github.com/cambrian-mllm/cambrian-s)** | **[Models](https://huggingface.co/collections/nyu-visionx/cambrian-s-models)**
+
+**Authors**: [Shusheng Yang*](https://github.com/vealocia), [Jihan Yang*](https://jihanyang.github.io/), [Pinzhi Huang†](https://pinzhihuang.github.io/), [Ellis Brown†](https://ellisbrown.github.io/), et al.
+
+VSI-SUPER-Recall is a benchmark for testing long-horizon spatial observation and recall in arbitrarily long videos. It evaluates whether models can remember and recall the order in which unusual objects appeared across extended video sequences.
+
+## Overview
+
+VSI-SUPER-Recall challenges models to:
+- Track object appearances across long videos (10-240 minutes)
+- Recall the temporal order of inserted objects
+- Maintain spatial memory over extended periods
+
+This benchmark is part of [VSI-Super](https://huggingface.co/collections/nyu-visionx/vsi-super), which also includes [VSI-SUPER-Count](https://huggingface.co/datasets/nyu-visionx/VSI-SUPER-Count).
+
+## Quick Start
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("nyu-visionx/VSI-SUPER-Recall", split="test")
+
+# Access a sample
+sample = dataset[0]
+print(sample)
+```
+
+## Dataset Structure
+
+Each sample contains:
+
+```python
+{
+    "video_path": "10mins/00000000.mp4",
+    "question": "These are frames of a video.\nWhich of the following correctly represents the order in which the Pikachu appeared in the video?",
+    "options": [
+        "A. Trash can, Bed, Chair, Basket",
+        "B. Trash can, Bed, Basket, Chair",
+        "C. Bed, Chair, Basket, Trash can",
+        "D. Bed, Chair, Trash can, Basket"
+    ],
+    "answer": "A",              # Correct option letter
+    "type": "10mins"            # Video duration
+}
+```
+
+**Key points:**
+- 300 samples total (60 per video duration)
+- Video durations: 10, 30, 60, 120, 240 minutes
+- Videos downsampled to 1 frame per second
+- Multiple choice format with 4 options
+- Questions ask about the order of appearance of inserted objects
+
+## Dataset Details
+
+- **Total samples**: 300
+- **Video durations**: 10mins (60), 30mins (60), 60mins (60), 120mins (60), 240mins (60)
+- **Question format**: Multiple choice about object appearance order
+- **Frame rate**: 1 FPS (downsampled)
+
+
+## Citation
+
+```bibtex
+@article{yang2025cambrian,
+  title={Cambrian-S: Towards Spatial Supersensing in Video},
+  author={Yang, Shusheng and Yang, Jihan and Huang, Pinzhi and Brown, Ellis and others},
+  journal={arXiv preprint arXiv:2025},
+  year={2025}
+}
+```
+