RGB Salient Object Detection
97 papers with code • 13 benchmarks • 17 datasets
RGB Salient object detection is a task-based on a visual attention mechanism, in which algorithms aim to explore objects or regions more attentive than the surrounding areas on the scene or RGB images.
( Image credit: Attentive Feedback Network for Boundary-Aware Salient Object Detection )
Libraries
Use these libraries to find RGB Salient Object Detection models and implementationsLatest papers with no code
Attention-based Assisted Excitation for Salient Object Detection
In this paper, object-based attention in human visual cortex inspires us to introduce a mechanism for modification of activations in feature maps of CNNs.
Cross-layer Feature Pyramid Network for Salient Object Detection
Feature pyramid network (FPN) based models, which fuse the semantics and salient details in a progressive manner, have been proven highly effective in salient object detection.
Saliency Enhancement using Gradient Domain Edges Merging
In recent years, there has been a rapid progress in solving the binary problems in computer vision, such as edge detection which finds the boundaries of an image and salient object detection which finds the important object in an image.
Depthwise Non-local Module for Fast Salient Object Detection Using a Single Thread
The proposed algorithm for the first time achieves competitive accuracy and high inference efficiency simultaneously with a single CPU thread.
Neural ODEs for Image Segmentation with Level Sets
We propose a novel approach for image segmentation that combines Neural Ordinary Differential Equations (NODEs) and the Level Set method.
Boundary-Aware Salient Object Detection via Recurrent Two-Stream Guided Refinement Network
Recent deep learning based salient object detection methods which utilize both saliency and boundary features have achieved remarkable performance.
DeepUSPS: Deep Robust Unsupervised Saliency Prediction via Self-supervision
Alternative unsupervised approaches rely on careful selection of multiple handcrafted saliency methods to generate noisy pseudo-ground-truth labels.
DeepUSPS: Deep Robust Unsupervised Saliency Prediction With Self-Supervision
Alternative unsupervised approaches rely on careful selection of multiple handcrafted saliency methods to generate noisy pseudo-ground-truth labels.
CNN-based RGB-D Salient Object Detection: Learn, Select and Fuse
The goal of this work is to present a systematic solution for RGB-D salient object detection, which addresses the following three aspects with a unified framework: modal-specific representation learning, complementary cue selection and cross-modal complement fusion.
Exploring Reciprocal Attention for Salient Object Detection by Cooperative Learning
Typically, objects with the same semantics are not always prominent in images containing different backgrounds.