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 STARE (Structured Analysis of the Retina) dataset is a dataset for retinal vessel segmentation. It contains 20 equal-sized (700×605) color fundus images. For each image, two groups of annotations are provided.. Source: DPN: Detail-Preserving Network with High Resolution Representation for Efficient Segmentation of Retinal Vessels Image Source: https://www.researchgate.net/figure/Results-of-the-different-methods-applied-to-the-STARE-dataset-a-original-image-b_fig4_279215756

The Freiburg-Berkeley Motion Segmentation Dataset (FBMS-59) is an extension of the BMS dataset with 33 additional video sequences. A total of 720 frames is annotated. It has pixel-accurate segmentation annotations of moving objects. FBMS-59 comes with a split into a training set and a test set. Source: https://lmb.informatik.uni-freiburg.de/resources/datasets/ Image Source: https://lmb.informatik.uni-freiburg.de/resources/datasets/

SegTrack v2 is a video segmentation dataset with full pixel-level annotations on multiple objects at each frame within each video. Source: https://web.engr.oregonstate.edu/~lif/SegTrack2/dataset.html Image Source: https://www.researchgate.net/publication/325842926_Semantic_Video_Segmentation_A_Review_on_Recent_Approaches

The Image Shadow Triplets dataset (ISTD) is a dataset for shadow understanding that contains 1870 image triplets of shadow image, shadow mask, and shadow-free image. Source: ST-CGAN: "Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal" Image Source: Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal

To build the highly accurate Dichotomous Image Segmentation dataset (DIS5K), we first manually collected over 12,000 images from Flickr1 based on our pre-designed keywords. Then, we obtained 5,470 images of 22 groups and 225 categories from the 12,000 images according to the structural complexities of the objects. Each image is then manually labeled with pixel-wise accuracy using GIMP. The labeled targets in DIS5K mainly focus on the “objects of the images defined by the pre-designed keywords (categories)” regardless of their characteristics e.g., salient, common, camouflaged, meticulous, etc. The average per-image labeling time is ∼30 minutes and some images cost up to 10 hours.

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

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

SOC (Salient Objects in Clutter) is a dataset for Salient Object Detection (SOD). It includes images with salient and non-salient objects from daily object categories. Beyond object category annotations, each salient image is accompanied by attributes that reflect common challenges in real-world scenes.

Includes 5000 spatially aligned RGBT image pairs with ground truth annotations. VT5000 has 11 challenges collected in different scenes and environments for exploring the robustness of algorithms. Source: RGBT Salient Object Detection: A Large-scale Dataset and Benchmark

SBU-Kinect-Interaction dataset version 2.0 comprises of RGB-D video sequences of humans performing interaction activities that are recording using the Microsoft Kinect sensor. This dataset was originally recorded for a class project, and it must be used only for the purposes of research. If you use this dataset in your work, please cite the following paper. Kiwon Yun, Jean Honorio, Debaleena Chattopadhyay, Tamara L. Berg, and Dimitris Samaras, The 2nd International Workshop on Human Activity Understanding from 3D Data at Conference on Computer Vision and Pattern Recognition (HAU3D-CVPRW), CVPR 2012 SBU-Refine: SBU-Refine relabels the test set manually and refines the noise labels in training set by algorithm. H. Yang, T. Wang, X. Hu, and C.-W. Fu, “SILT: Shadow-aware iterative label tuning for learning to detect shadows from noisy labels,” in ICCV, 2023, pp. 12 687–12 698.

There exist several datasets for saliency detection, but none of them is specifically designed for high-resolution salient object detection. High-Resolution Salient Object Detection (HRSOD) dataset, containing 1610 training images and 400 test images. The total 2010 images are collected from the website of Flickr with the license of all creative commons. Pixel-level ground truths are manually annotated by 40 subjects. The shortest edge of each image in HRSOD is more than 1200 pixels.

The Freiburg-Berkeley Motion Segmentation Dataset (FBMS-59) is a dataset for motion segmentation, which extends the BMS-26 dataset with 33 additional video sequences. A total of 720 frames is annotated. FBMS-59 comes with a split into a training set and a test set. Typical challenges appear in both sets. Source: https://lmb.informatik.uni-freiburg.de/resources/datasets/moseg.en.html Image Source: https://lmb.informatik.uni-freiburg.de/resources/datasets/moseg.en.html

To enrich the diversity, we also collect 92 images which are suitable for saliency detection from DAVIS [27], a densely annotated high-resolution video segmentation dataset. Im- ages in this dataset are precisely annotated and have very high resolutions (i.e.,1920!1080). We ignore the categories of the objects and generate saliency ground truth masks for this dataset. For convenience, the collected dataset is denot- ed as DAVIS-S.

DataViSal.rar (including the ground truth data) is our new collected dataset for the following paper. =========================================================================== W. Wang, J. Shen, and L. Shao, Consistent video saliency using local gradient flow optimization and global refinement, IEEE Trans. on Image Processing, 24(11):4185-4196, 2015 =========================================================================== The related source code can be downloaded from: https://github.com/shenjianbing/videosal =========================================================================== Note: =========================================================================== The data and code files are free to use for research purposes. If you use them for research purposes, you should cite above paper in any resulting publication. This code also uses some publicly available functions. =========================================================================== Contact Information =========================================================================== Email: wenguanwang@bit.edu.cn shenjianbing@bit.edu.cn shenjianbingcg@gmail.com

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

Aiming Detect small obstacles, like lost and found. # frames 3000+ picture. 3000+ claimed labelled. 1600 actually labelled.

Recent salient object detection (SOD) methods based on deep neural network have achieved remarkable performance. However, most of existing SOD models designed for low-resolution input perform poorly on high-resolution images due to the contradiction between the sampling depth and the receptive field size. Aiming at resolving this contradiction, we propose a novel one-stage framework called Pyramid Grafting Network (PGNet), using transformer and CNN backbone to extract features from different resolution images independently and then graft the features from transformer branch to CNN branch. An attention-based Cross-Model Grafting Module (CMGM) is proposed to enable CNN branch to combine broken detailed information more holistically, guided by different source feature during decoding process. Moreover, we design an Attention Guided Loss (AGL) to explicitly supervise the attention matrix generated by CMGM to help the network better interact with the attention from different models. We contribute a new Ultra-High-Resolution Saliency Detection dataset UHRSD, containing 5,920 images at 4K-8K resolutions. To our knowledge, it is the largest dataset in both quantity and resolution for high-resolution SOD task, which can be used for training and testing in future research. Sufficient experiments on UHRSD and widely-used SOD datasets demonstrate that our method achieves superior performance compared to the state-of-the-art methods.

360-SOD contains 500 high-resolution equirectangular images. Source: Distortion-adaptive Salient Object Detection in 360$^\circ$ Omnidirectional Images Image Source: http://cvteam.net/projects/JSTSP20_DDS/DDS.html

A salient object subitizing image dataset of about 14K everyday images which are annotated using an online crowdsourcing marketplace. Source: Salient Object Subitizing

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.

A small-scale training set, which only contains 4K images. Source: A Deeper Look at Salient Object Detection: Bi-stream Network with a Small Training Dataset