README.md

metadata

license: cc-by-4.0
pretty_name: AViMoS
size_categories:
  - 1K<n<10K

Dataset for ECCV-AIM Video Saliency Prediction Challenge 2024

We provide a novel audio-visual mouse saliency (AViMoS) dataset with the following key-features:

• Diverse content: movie, sports, live, vertical videos, etc.;
• Large scale: 1500 videos with mean 19s 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 — 1500 (1000 Train + 500 Test) .mp4 video (kindly reminder: many 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/ — 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. Check that your predictions match the structure and names of the baseline CenterPrior 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-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

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

Challenge Leaderboard

Please follow the paper to learn about the team's solutions, and challenge page for more results.

Here we only provide the final leaderboard:

Team Name	AUC-Judd	CC	SIM	NSS	Rank	#Params (M)
CV_MM	0.894	0.774	0.635	3.464	1.00	420.5
VistaHL	0.892	0.769	0.623	3.352	2.75	187.7
PeRCeiVe Lab	0.857	0.766	0.610	3.422	3.75	402.9
SJTU-MML	0.858	0.760	0.615	3.356	4.00	1288.7
MVP	0.838	0.749	0.587	3.404	5.00	99.6
ZenithChaser	0.869	0.606	0.517	2.482	5.50	0.19
Exodus	0.861	0.599	0.510	2.491	6.00	69.7
Baseline (CP)	0.833	0.449	0.424	1.659	8.00	-

Citation

Please cite the paper if you find challenge materials useful for your research:

@article{ }