CrowdSAL: Crowdsourced Video Saliency Prediction
Dataset and Benchmark
Explore the best video saliency prediction (VSP) dataset
Our dataset is a high-resolution multitype set of videos collected from observers using mouse-saliency. The benchmark of the best video saliency prediction methods for recognition of the most important areas of the video is based on it.
5000 High-Resolution Test Clips
especially movie fragments,
sport streams and live caption clips
Reliable Data Collection
using mouse-saliency
for 90+ observers
An Open Visual Comparison
with source fragments
available for reference
10 Models Tested
in 8 various works
with different weights/architectures
Domain Adaptation
with brightness change
and Center Prior blending
for prediction generalization
Speed/Quality Scatter Plots
and tables with objective metrics
for a comprehensive comparison
What’s New
- March 27th, 2023: Release
Introduction
We use various objective metrics for evaluating video saliency prediction methods. Also, we calculate the average FPS (frames per second) to compare the speed of the algorithms.
To generalize the output of the models, we use the domain adaptation involving such transformations as brightness correction and blending with the Center Prior. Check the “Methodology” section to learn the details.
Scroll below for comparison charts, tables, and interactive visual comparisons of saliency model results.
Visualizations
Leaderboards
Charts
Cite Us
|
@inproceedings{
moskalenko2024aim,
title={AIM 2024 challenge on video saliency prediction: Methods and results},
author={Moskalenko, A and Bryncev, A and Vatolin, D and Timofte, R and Zhan, G and Yang, Li and Tang, Y and Liao, Y and Lin, J and Huang, B and others},
booktitle={The 18th European Conference on Computer Vision ECCV 2024, Advances in Image Manipulation workshop},
year={2024},
}
|
|
@inproceedings{
gitman2014semiautomatic,
title={Semiautomatic visual-attention modeling and its application to video compression},
author={Gitman, Yury and Erofeev, Mikhail and Vatolin, Dmitriy and Andrey, Bolshakov and Alexey,Fedorov},
booktitle={2014 IEEE international conference on image processing (ICIP)},
pages={1105--1109},
year={2014},
organization={IEEE}
}
|
Further Reading
Check the “Methodology” section to learn how we prepare our dataset.
MSU Video Quality Measurement Tool
The tool for performing video/image quality analyses using reference or no-reference metrics
Widest Range of Metrics & Formats
- Modern & Classical Metrics SSIM, MS-SSIM, PSNR, VMAF and 10+ more
- Non-reference analysis & video characteristics
Blurring, Blocking, Noise, Scene change detection, NIQE and more
Fastest Video Quality Measurement
- GPU support
Up to 11.7x faster calculation of metrics with GPU - Real-time measure
- Unlimited file size
Main MSU VQMT page on compression.ru
-
MSU Benchmark Collection
- Super-Resolution Quality Metrics Benchmark
- Super-Resolution Quality Metrics Benchmark
- Video Colorization Benchmark
- Video Saliency Prediction Benchmark
- LEHA-CVQAD Video Quality Metrics Benchmark
- Learning-Based Image Compression Benchmark
- Super-Resolution for Video Compression Benchmark
- Defenses for Image Quality Metrics Benchmark
- Deinterlacer Benchmark
- Metrics Robustness Benchmark
- Video Upscalers Benchmark
- Video Deblurring Benchmark
- Video Frame Interpolation Benchmark
- HDR Video Reconstruction Benchmark
- No-Reference Video Quality Metrics Benchmark
- Full-Reference Video Quality Metrics Benchmark
- Video Alignment and Retrieval Benchmark
- Mobile Video Codecs Benchmark
- Video Super-Resolution Benchmark
- Shot Boundary Detection Benchmark
- The VideoMatting Project
- Video Completion
- Codecs Comparisons & Optimization
- VQMT
- MSU Datasets Collection
- Metrics Research
- Video Quality Measurement Tool 3D
- Video Filters
- Other Projects