Add dataset card and link to paper

README.md

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+---
+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}
+}
+```