Add paper and GitHub links to dataset card

This PR improves the dataset card by:
- Adding the `task_categories: other` metadata.
- Linking the dataset to the official paper: [NTIRE 2026 Challenge on Video Saliency Prediction: Methods and Results](https://huggingface.co/papers/2604.14816).
- Adding a link to the [GitHub repository](https://github.com/msu-video-group/NTIRE26_Saliency_Prediction) and the official project page.
- Including environment setup and evaluation instructions from the repository.

README.md

@@ -1,12 +1,20 @@
 ---
 license: cc-by-4.0
+task_categories:
+- other
 size_categories:
 - 1K<n<10K
+tags:
+- video-saliency-prediction
+- ntire-2026
+- computer-vision
 ---
 
 # CVPR-NTIRE Video Saliency Prediction Challenge 2026
 
-[![Page](https://img.shields.io/badge/Challenge-Page-blue)](https://www.codabench.org/competitions/12842/)
+This repository contains the dataset for the NTIRE 2026 Challenge on Video Saliency Prediction, as presented in the paper [NTIRE 2026 Challenge on Video Saliency Prediction: Methods and Results](https://huggingface.co/papers/2604.14816).
+
+[Paper](https://huggingface.co/papers/2604.14816) | [GitHub](https://github.com/msu-video-group/NTIRE26_Saliency_Prediction) | [Project Page](https://cvlai.net/ntire/2026/) | [![Challenge Page](https://img.shields.io/badge/Challenge-Page-blue)](https://www.codabench.org/competitions/12842/)
 
 ## Dataset
 
@@ -18,7 +26,7 @@ We provide a novel audio-visual mouse saliency dataset with the following key-fe
 * Mouse fixations from **>5000** observers (**>70** per video);
 * License: **CC-BY**;
 
-File structure:
+### File structure:
 1) `Videos.zip` — 2000 (1200 Train + 800 Test) .mp4 video (kindly reminder: videos contain an audio stream and users watched the video with the sound turned ON!) 
 
 2) `TrainTestSplit.json` — in this JSON we provide Train/Public Test/Private Test split of all videos 
@@ -40,21 +48,22 @@ File structure:
 
 ### Environment Setup
 
-```
+```bash
 conda create -n saliency python=3.10.19
 conda activate saliency
 pip install numpy==2.2.6 opencv-python-headless==4.12.0.88 tqdm==4.67.1
 conda install ffmpeg=4.4.2 -c conda-forge
 ```
+
 ### Run Evaluation
 Archives with videos were accepted from challenge participants as submissions and scored using the same pipeline as in `bench.py`.
 
 Usage example:
 
 1) Check that your predictions match the structure and names of the baseline SampleSubmission.zip submission.
-2) Install `pip install -r requirments.txt`, `conda install ffmpeg`
-3) Download and extract `SaliencyTest.zip`,  `FixationsTest.zip`, and `TrainTestSplit.json` files from the dataset page
-4) Run `python bench.py` with flags:
+2) Install `pip install -r requirements.txt`, `conda install ffmpeg`
+3) Download and extract `SaliencyTest.zip`, `FixationsTest.zip`, and `TrainTestSplit.json` files from the dataset page.
+4) Run `python bench.py` (found in the GitHub repo) with flags:
 * `--model_video_predictions ./SampleSubmission` — folder with predicted saliency videos
 * `--model_extracted_frames ./SampleSubmission-Frames` — folder to store prediction frames (should not exist at launch time), requires ~170 GB of free space
 * `--gt_video_predictions ./SaliencyTest/Test` — folder from dataset page with gt saliency videos
@@ -63,6 +72,7 @@ Usage example:
 * `--split_json ./TrainTestSplit.json` — JSON from dataset page with names splitting
 * `--results_json ./results.json` — path to the output results json
 * `--mode public_test` — public_test/private_test subsets
-5) The result you get will be available following `results.json` path
+
+5) The result you get will be available following `results.json` path.
 
 [![Challenges](https://img.shields.io/badge/Challenges-NTIRE%202026-orange)](https://www.cvlai.net/ntire/2026/)