| --- |
| 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 |
|
|
| 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/) | [](https://www.codabench.org/competitions/12842/) |
|
|
| ## Dataset |
|
|
| We provide a novel audio-visual mouse saliency dataset with the following key-features: |
| * Diverse content: movie, sports, live, vertical videos, etc.; |
| * Large scale: **2000** videos with mean **18s** duration; |
| * High resolution: all streams are **FullHD**; |
| * **Audio** track saved and played to observers; |
| * Mouse fixations from **>5000** observers (**>70** per video); |
| * License: **CC-BY**; |
|
|
| ### 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 |
|
|
| 3) `SaliencyTrain.zip/SaliencyTest.zip` — almost losslessly (crf 0, 10bit, min-max normalized) compressed continuous saliency maps videos for Train/Test subset |
|
|
| 4) `FixationsTrain.zip/FixationsTest.zip` — contains the following files for Train/Test subset: |
|
|
| * `.../video_name/fixations.json` — per-frame fixations coordinates, from which saliency maps were obtained, this JSON will be used for metrics calculation |
|
|
| * `.../video_name/fixations_maps/` — binary fixation maps in '.png' format (since some fixations could share the same pixel, this is a lossy representation and is NOT used either in calculating metrics or generating Gaussians, however, we provide them for visualization and frames count checks) |
|
|
| 5) `VideoInfo.json` — meta information about each video (e.g. license) |
|
|
| 6) `SampleSubmission.zip` — example submission for the challenge, obtained from fitted Center Prior Gaussian over mean training saliency maps. |
|
|
|
|
| ## Evaluation |
|
|
| ### 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 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 |
| * `--gt_extracted_frames ./SaliencyTest-Frames` — folder to store ground-truth frames (should not exist at launch time), requires ~170 GB of free space |
| * `--gt_fixations_path ./FixationsTest/Test` — folder from dataset page with gt saliency fixations |
| * `--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. |
|
|
| [](https://www.cvlai.net/ntire/2026/) |
