| --- |
| 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} |
| } |
| ``` |
