| | --- |
| | license: cc-by-4.0 |
| | size_categories: |
| | - 1K<n<10K |
| | --- |
| | |
| | # CVPR-NTIRE Video Saliency Prediction Challenge 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 |
| |
|
| | ``` |
| | 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) 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: |
| | * `--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/) |
