ANDRYHA/VideoSaliencyChallenge

CVPR-NTIRE Video Saliency Prediction Challenge 2026

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)

Evaluation

Environment Setup

conda create -n saliency python=3.8.16
conda activate saliency
pip install numpy==1.24.2 opencv-python==4.7.0.72 tqdm==4.65.0
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. Install pip install -r requirments.txt, conda install ffmpeg
2. Download and extract SaliencyTest.zip, FixationsTest.zip, and TrainTestSplit.json files from the dataset page
3. Run python bench.py with flags:

• --model_video_predictions ./SampleSubmission-CenterPrior — folder with predicted saliency videos
• --model_extracted_frames ./SampleSubmission-CenterPrior-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

4. The result you get will be available following results.json path