Saliency Detection

The PASCAL Context dataset is an extension of the PASCAL VOC 2010 detection challenge, and it contains pixel-wise labels for all training images. It contains more than 400 classes (including the original 20 classes plus backgrounds from PASCAL VOC segmentation), divided into three categories (objects, stuff, and hybrids). Many of the object categories of this dataset are too sparse and; therefore, a subset of 59 frequent classes are usually selected for use. Source: Image Segmentation Using Deep Learning:A Survey Image Source: https://cs.stanford.edu/~roozbeh/pascal-context/

DUTS is a saliency detection dataset containing 10,553 training images and 5,019 test images. All training images are collected from the ImageNet DET training/val sets, while test images are collected from the ImageNet DET test set and the SUN data set. Both the training and test set contain very challenging scenarios for saliency detection. Accurate pixel-level ground truths are manually annotated by 50 subjects. Source: http://saliencydetection.net/duts/ Image Source: https://ieeexplore.ieee.org/document/8099887

PASCAL-S is a dataset for salient object detection consisting of a set of 850 images from PASCAL VOC 2010 validation set with multiple salient objects on the scenes. Source: Structured Modeling of Joint Deep Feature and Prediction Refinement for Salient Object Detection

HKU-IS is a visual saliency prediction dataset which contains 4447 challenging images, most of which have either low contrast or multiple salient objects. Source: Deep Contrast Learning for Salient Object Detection Image Source: https://sites.google.com/site/ligb86/mdfsaliency/

The DUT-OMRON dataset is used for evaluation of Salient Object Detection task and it contains 5,168 high quality images. The images have one or more salient objects and relatively cluttered background. Source: Global Context-Aware Progressive Aggregation Network for Salient Object Detection

The SALIency in CONtext (SALICON) dataset contains 10,000 training images, 5,000 validation images and 5,000 test images for saliency prediction. This dataset has been created by annotating saliency in images from MS COCO. The ground-truth saliency annotations include fixations generated from mouse trajectories. To improve the data quality, isolated fixations with low local density have been excluded. The training and validation sets, provided with ground truth, contain the following data fields: image, resolution and gaze. The testing data contains only the image and resolution fields. Source: DeepFix: A Fully Convolutional Neural Network for predicting Human Eye Fixations Image Source: http://salicon.net/explore/

iSUN is a ground truth of gaze traces on images from the SUN dataset. The collection is partitioned into 6,000 images for training, 926 for validation and 2,000 for test. Source: End-to-end Convolutional Network for Saliency Prediction Image Source: http://turkergaze.cs.princeton.edu/

Includes 950 real-world underwater images, 890 of which have the corresponding reference images. Source: An Underwater Image Enhancement Benchmark Dataset and Beyond

Includes 4000 images; 200 from each of 20 categories covering different types of scenes such as Cartoons, Art, Objects, Low resolution images, Indoor, Outdoor, Jumbled, Random, and Line drawings. Source: CAT2000: A Large Scale Fixation Dataset for Boosting Saliency Research

The Extended Complex Scene Saliency Dataset (ECSSD) is comprised of complex scenes, presenting textures and structures common to real-world images. ECSSD contains 1,000 intricate images and respective ground-truth saliency maps, created as an average of the labeling of five human participants. Source: SAD: Saliency-based Defenses Against Adversarial Examples

The Segmentation of Underwater IMagery (SUIM) dataset contains over 1500 images with pixel annotations for eight object categories: fish (vertebrates), reefs (invertebrates), aquatic plants, wrecks/ruins, human divers, robots, and sea-floor. The images have been rigorously collected during oceanic explorations and human-robot collaborative experiments, and annotated by human participants. Source: Semantic Segmentation of Underwater Imagery: Dataset and Benchmark Image Source: http://irvlab.cs.umn.edu/resources/suim-dataset

Cosal2015 is a large-scale dataset for co-saliency detection which consists of 2,015 images of 50 categories, and each group suffers from various challenging factors such as complex environments, occlusion issues, target appearance variations and background clutters, etc. All these increase the difficulty for accurate co-saliency detection. Source: Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency Detection Image Source: https://arxiv.org/pdf/1604.07090.pdf

DHF1K is a video saliency dataset which contains a ground-truth map of binary pixel-wise gaze fixation points and a continuous map of the fixation points after being blurred by a gaussian filter. DHF1K contains 1000 videos in total. 700 of the videos are annotated, 600 of which are used for training and 100 for validation. The remaining 300 are the testing set which are to be evaluated on a public server. Source: ViP: Video Platform for PyTorch Image Source: https://arxiv.org/pdf/1801.07424.pdf

The dataset presents open high-resolution test clips set with different types of content: movie fragments, sport streams, live caption clips. Used clips of 1920×1080 resolution and with duration from 13 to 38 seconds. And Performed reliable data collection from 50 observers (19–24 y. o.) using 500 Hz SMI iViewXTM Hi-Speed 1250 eye-tracker. Also used cross-fade which ensures the independence of the received fixations between different clips. The final ground-truth saliency map was estimated as a Gaussian mixture with centers at the fixation points. A standard deviation for the Gaussians equal to 120 was chosen (this value matches 8 angular degrees, which is known to be the sector of sharp vision).

ReDWeb-S is a large-scale challenging dataset for Salient Object Detection. It has totally 3179 images with various real-world scenes and high-quality depth maps. The dataset is split into a training set with 2179 RGB-D image pairs and a testing set with the remaining 1000 image pairs. Source: https://github.com/nnizhang/SMAC Image Source: https://github.com/nnizhang/SMAC

HS-SOD is a hyperspectral salient object detection dataset with a collection of 60 hyperspectral images with their respective ground-truth binary images and representative rendered colour images (sRGB). Source: Hyperspectral Image Dataset for Benchmarking on Salient Object Detection

Lytro Illum is a new light field dataset using a Lytro Illum camera. 640 light fields are collected with significant variations in terms of size, textureness, background clutter and illumination, etc. Micro-lens image arrays and central viewing images are generated, and corresponding ground-truth maps are produced. Source: Lytro Illum

COME15K is an RGB-D saliency detection dataset which contains 15,625 image pairs with high quality polygon-/scribble-/object-/instance-/rank-level annotations.

We construct the first large-scale dataset, USIS10K, for the underwater salient instance segmentation task, which contains 10,632 images and pixel-level annotations of 7 categories. As far as we know, this is the largest salient instance segmentation dataset, and includes Class-Agnostic and Multi-Class labels simultaneously. If you use this dataset in your work, please consider to cite: @inproceedings{ lian2024icml, title={Diving into Underwater: Segment Anything Model Guided Underwater Salient Instance Segmentation and A Large-scale Dataset}, author={Shijie Lian and Ziyi Zhang and Hua Li and Wenjie Li and Laurence Tianruo Yang and Sam Kwong and Runmin Cong}, booktitle={Forty-first International Conference on Machine Learning}, year={2024}, url={https://openreview.net/forum?id=snhurpZt63} }

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;

The CapMIT1003 database contains captions and clicks collected for images from the MIT1003 database, for which reference eye scanpath are available. The database is distributed as a single SQLite3 database named capmit1003.db. For convenience, a lightweight Python class to access the database is provided in the official repository

CAT is a specialized dataset for co-saliency detection - one of the core tasks in the field of computer vision. This dataset is intended for both helping to assess the performance of vision algorithms and supporting research that aims to exploit large volumes of annotated data, e.g., for training deep neural networks. CAT consists of 33,500 images

CAT is a specialized dataset for co-saliency detection. This dataset is intended for both helping to assess the performance of vision algorithms and supporting research that aims to exploit large volumes of annotated data, e.g., for training deep neural networks. Scale & Features - A total number of 33500 image samples. - 280 semantic groups affiliated to 15 superclasses. - High-quality mask annotations. - Diverse visual context with multiple foreground objects.

Salient-KITTI is a saliency map prediction dataset based on KITTI. Source: https://arxiv.org/pdf/2012.11863.pdf Image Source: https://github.com/Saixiaoma/SBA-SLAM