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 )
Benchmarks
These leaderboards are used to track progress in RGB Salient Object Detection
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Libraries
Use these libraries to find RGB Salient Object Detection models and implementations
DUTS
PASCAL-S
HKU-IS
DUT-OMRON
ISTD
SBU
ECSSD
SOC
VT5000
HRSOD
Latest papers with no code
Super Diffusion for Salient Object Detection
Following this analysis, we propose super diffusion, a novel inclusive learning-based framework for salient object detection, which makes the optimum and robust performance by integrating a large pool of feature spaces, scales and even features originally computed for non-diffusion-based salient object detection.
Salient Object Detection in Video using Deep Non-Local Neural Networks
Detection of salient objects in image and video is of great importance in many computer vision applications.
Relative Saliency and Ranking: Models, Metrics, Data, and Benchmarks
Further to this, we present data, analysis and baseline benchmark results towards addressing the problem of salient object ranking.
Boundary-guided Feature Aggregation Network for Salient Object Detection
Fully convolutional networks (FCN) has significantly improved the performance of many pixel-labeling tasks, such as semantic segmentation and depth estimation.
Sequential Clique Optimization for Video Object Segmentation
A novel algorithm to segment out objects in a video sequence is proposed in this work.
Object Detection with Deep Learning: A Review
Traditional object detection methods are built on handcrafted features and shallow trainable architectures.
Video Saliency Detection by 3D Convolutional Neural Networks
Different from salient object detection methods for still images, a key challenging for video saliency detection is how to extract and combine spatial and temporal features.
An Integration of Bottom-up and Top-Down Salient Cues on RGB-D Data: Saliency from Objectness vs. Non-Objectness
Bottom-up and top-down visual cues are two types of information that helps the visual saliency models.
Context Proposals for Saliency Detection
Therefore, we extend object proposal methods with context proposals, which allow to incorporate the immediate context in the saliency computation.
A Bi-Directional Message Passing Model for Salient Object Detection
In this paper, we propose a novel bi-directional message passing model to integrate multi-level features for salient object detection.
