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--- |
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license: cc-by-nc-4.0 |
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task_categories: |
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- image-segmentation |
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tags: |
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- video |
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- multimodal |
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- segmentation |
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- pointing |
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- spatio-temporal-grounding |
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- robotics |
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- autonomous-driving |
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- cell-tracking |
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- egocentric-vision |
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- gui-interaction |
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--- |
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# VPoS-Bench: Video Pointing and Segmentation Benchmark |
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**VPoS-Bench** is a challenging out-of-distribution benchmark designed to evaluate the spatio-temporal pointing and reasoning capabilities of video-language models. It covers a diverse set of five real-world application domains, with fine-grained point-level and segmentation annotations that enable robust evaluation of multimodal models under realistic, temporally complex scenarios. |
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> **Webpage**: [VideoMolmo](https://mbzuai-oryx.github.io/VideoMolmo/) |
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> **Paper**: [VideoMolmo: Spatio-Temporal Grounding meets Pointing](https://arxiv.org/pdf/2506.05336) |
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> **Model**: [VideoMolmo on Hugging Face](https://huggingface.co/ghazishazan/VideoMolmo) |
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> **Code**: [VideoMolmo on Github](https://github.com/mbzuai-oryx/VideoMolmo) |
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--- |
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## 🌍 Benchmark Overview |
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VPoS-Bench tests the **generalization** of models in five diverse real-world scenarios: |
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1. **Cell Tracking** |
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Track the trajectory of biological entities (e.g., nuclei or cells) across microscopy video frames. |
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> Applications: developmental biology, disease modeling |
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2. **Egocentric Vision** |
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Identify and follow objects or hands in first-person camera footage. |
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> Applications: activity recognition, assistive tech |
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3. **Autonomous Driving** |
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Point to traffic participants (pedestrians, vehicles, lights) under varying conditions. |
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> Applications: self-driving systems, urban scene understanding |
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4. **Video-GUI Interaction** |
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Follow on-screen elements (e.g., cursors, buttons) across software interface recordings. |
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> Applications: AI-assisted UI navigation, screen agents |
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5. **Robotics** |
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Track manipulable objects or robotic end-effectors as they interact in structured environments. |
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> Applications: robot learning, manipulation planning |
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--- |
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## 📁 Dataset Structure |
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The dataset is organized by domain. Each domain folder contains three subdirectories: |
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- `frames/` – Extracted video frames. |
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- `masks/` – Segmentation masks corresponding to frames. |
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- `annotations/` – JSON files containing text descriptions and point-level annotations. |
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```text |
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vpos-bench/ |
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├── cell-tracking/ |
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│ ├── frames/ # Extracted video frames (e.g., frame_0001.jpg, ...) |
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│ ├── masks/ # Segmentation masks per frame (optional) |
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│ └── annotations/ # Point coordinates + caption in JSON format |
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│ |
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├── autonomous-driving/ |
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... |
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--- |
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├── |
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``` |
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## 📁 Annotation Format |
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Each annotation is keyed by a unique video ID and consists of: |
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```json |
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{ |
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"video_id": { |
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"caption": "natural language instruction here", |
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"frames": [ |
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{ |
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"frame_path": "domain/frames/video_id/frame_00001.jpg", |
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"mask_path": "domain/masks/video_id/0.png", |
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"points": [[x, y], ...] |
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}, |
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{ |
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"frame_path": "domain/frames/video_id/frame_00002.jpg", |
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"mask_path": "domain/masks/video_id/1.png", |
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"points": [[x, y], ...] |
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} |
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] |
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} |
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} |
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``` |
