ContrastPrior 
 Salient Object Detector
 A toolbox for detecting salient objects in RGBD images using a combination of contrast and pyramid integration techniques
The Code of Contrast Prior and Fluid Pyramid Integration for RGBD Salient Object Detection(CVPR2019)
51 stars
 5 watching
 8 forks
 
Language: Jupyter Notebook 
last commit: over 5 years ago  Related projects:
| Repository | Description | Stars | 
|---|---|---|
|    |  A Python implementation of a depth-induced multi-scale recurrent attention network for RGB-D saliency detection | 105 | 
|    |  A collection of resources and tools for RGBT salient object detection in image processing | 63 | 
|    |  An implementation of RGB-D saliency detection using a cascaded mutual information minimization approach | 59 | 
|    |  This software implementation contains pre-computed saliency maps and source code for a high-resolution salient object detection algorithm. | 69 | 
|    |  An implementation of instance-level salient object segmentation using the MSRNet architecture | 9 | 
|    |  A collection of RGB-D Saliency Datasets and evaluation metrics for salient object detection | 64 | 
|    |  Develops a deep neural network model for detecting salient objects in RGBT images using correlation information from other colors. | 13 | 
|    |  A MATLAB implementation of a salient object detection algorithm | 50 | 
|    |  Provides code and datasets for a saliency object detection method that leverages captioning to improve accuracy | 50 | 
|    |  A Python-based object detection framework utilizing transformers and computer vision techniques to detect salient objects in RGB-thermal images | 16 | 
|    |  A library for finding interest points in images using integral histograms and entropy-based saliency | 7 | 
|    |  A software framework implementing an alignment-free RGBT salient object detection algorithm using a semantics-guided asymmetric correlation network | 10 | 
|    |  A Caffe-based implementation of A-Fast-RCNN, a method for object detection using adversarial networks. | 482 | 
|    |  An approach to improving pedestrian detection from thermal images using saliency maps and deep learning techniques. | 38 | 
|    |  An approach to reduce object hallucinations in large vision-language models by contrasting output distributions derived from original and distorted visual inputs | 222 |