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--- |
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license: cc-by-4.0 |
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pretty_name: AViMoS |
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size_categories: |
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- 1K<n<10K |
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--- |
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# ECCV-AIM Video Saliency Prediction Challenge 2024 |
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[](https://challenges.videoprocessing.ai/challenges/video-saliency-prediction.html) |
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[](https://arxiv.org/) |
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<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/623a3b20fa4890c51b04cba7/nmWH-xSewvOvlicuMPejp.mp4"></video> |
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## Dataset |
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We provide a novel audio-visual mouse saliency (<em>AViMoS</em>) dataset with the following key-features: |
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* Diverse content: movie, sports, live, vertical videos, etc.; |
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* Large scale: **1500** videos with mean **19s** duration; |
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* High resolution: all streams are **FullHD**; |
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* **Audio** track saved and played to observers; |
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* Mouse fixations from **>5000** observers (**>70** per video); |
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* License: **CC-BY**; |
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File structure: |
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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!) |
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2) `TrainTestSplit.json` — in this JSON we provide Train/Public Test/Private Test split of all videos |
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3) `SaliencyTrain.zip/SaliencyTest.zip` — almost losslessly (crf 0, 10bit, min-max normalized) compressed continuous saliency maps videos for Train/Test subset |
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4) `FixationsTrain.zip/FixationsTest.zip` — contains the following files for Train/Test subset: |
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* `.../video_name/fixations.json` — per-frame fixations coordinates, from which saliency maps were obtained, this JSON will be used for metrics calculation |
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* `.../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) |
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5) `VideoInfo.json` — meta information about each video (e.g. license) |
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## Evaluation |
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### Environment setup |
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``` |
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conda create -n saliency python=3.8.16 |
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conda activate saliency |
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pip install numpy==1.24.2 opencv-python==4.7.0.72 tqdm==4.65.0 |
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conda install ffmpeg=4.4.2 -c conda-forge |
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``` |
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### Run evaluation |
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Archives with videos were accepted from challenge participants as submissions and scored using the same pipeline as in `bench.py`. |
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Usage example: |
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1) Check that your predictions match the structure and names of the [baseline CenterPrior submission](https://drive.google.com/file/d/1rPgMdb4L79OD2vvpDQyqWZIDox78rmxG/view) |
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2) Install `pip install -r requirments.txt`, `conda install ffmpeg` |
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3) Download and extract `SaliencyTest.zip`, `FixationsTest.zip`, and `TrainTestSplit.json` files from the dataset page |
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4) Run `python bench.py` with flags: |
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* `--model_video_predictions ./SampleSubmission-CenterPrior` — folder with predicted saliency videos |
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* `--model_extracted_frames ./SampleSubmission-CenterPrior-Frames` — folder to store prediction frames (should not exist at launch time), requires ~170 GB of free space |
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* `--gt_video_predictions ./SaliencyTest/Test` — folder from dataset page with gt saliency videos |
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* `--gt_extracted_frames ./SaliencyTest-Frames` — folder to store ground-truth frames (should not exist at launch time), requires ~170 GB of free space |
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* `--gt_fixations_path ./FixationsTest/Test` — folder from dataset page with gt saliency fixations |
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* `--split_json ./TrainTestSplit.json` — JSON from dataset page with names splitting |
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* `--results_json ./results.json` — path to the output results json |
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* `--mode public_test` — public_test/private_test subsets |
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5) The result you get will be available following `results.json` path |
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## Citation |
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Please cite the paper if you find challenge materials useful for your research: |
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`@article{ |
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} |
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` |
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[](https://cvlai.net/aim/2024/) |
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[](https://videoprocessing.ai/benchmarks/) |
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