---
task_categories:
- video-text-to-text
---

# VisCoP Dataset

This repository contains the training and evaluation data for **VisCoP: Visual Probing for Video Domain Adaptation of Vision Language Models**.

- **Paper:** [VisCoP: Visual Probing for Video Domain Adaptation of Vision Language Models](https://huggingface.co/papers/2510.13808)
- **Repository:** [GitHub - dominickrei/VisCoP](https://github.com/dominickrei/VisCoP)

## Dataset Description

VisCoP is a parameter-efficient adaptation framework designed to adapt Vision Language Models (VLMs) to new domains (e.g., cross-view, cross-modal, and cross-task settings) using a compact set of learnable visual probes. This dataset provides the instruction pairs and videos used for training and evaluating VisCoP across these scenarios.

### Training Data
The training dataset includes:
- **Egocentric Viewpoint:** Instructions and videos.
- **Depth Modality:** Instructions and videos.

### Evaluation Data
We evaluate VisCoP across multiple target domains using the following benchmarks:
- **Egocentric Viewpoint:** Ego-in-Exo PerceptionMCQ, EgoSchema, NeXTQA, VideoMME, ADL-X.
- **Depth Modality:** Exo Depth videos (contained in `depth_videos.zip`).
- **Robot Control:** VIMA-Bench.

For detailed setup and evaluation protocols, please refer to the [GitHub Repository](https://github.com/dominickrei/VisCoP).

## Citation

```bibtex
@inproceedings{reilly2026viscop,
  title     = {VisCoP: Visual Probing for Video Domain Adaptation of Vision Language Models},
  author    = {Dominick Reilly and Manish Kumar Govind and Le Xue and Srijan Das},
  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
  year      = {2026}
}
```