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
PASCAL-S
DUTS
HKU-IS
DUT-OMRON
ISTD
SBU
ECSSD
SOC
VT5000
HRSOD
Latest papers with no code
ROSA: Robust Salient Object Detection against Adversarial Attacks
To our knowledge, this paper is the first one that mounts successful adversarial attacks on salient object detection models and verifies that adversarial samples are effective on a wide range of existing methods.
Salient Object Detection: A Distinctive Feature Integration Model
We propose a novel method for salient object detection in different images.
DSAL-GAN: Denoising based Saliency Prediction with Generative Adversarial Networks
In this paper, we present a novel end-to-end coupled Denoising based Saliency Prediction with Generative Adversarial Network (DSAL-GAN) framework to address the problem of salient object detection in noisy images.
SAC-Net: Spatial Attenuation Context for Salient Object Detection
This paper presents a new deep neural network design for salient object detection by maximizing the integration of local and global image context within, around, and beyond the salient objects.
Deep Reasoning with Multi-Scale Context for Salient Object Detection
However, the saliency inference module that performs saliency prediction from the fused features receives much less attention on its architecture design and typically adopts only a few fully convolutional layers.
Salient Object Detection with Lossless Feature Reflection and Weighted Structural Loss
Inspired by the intrinsic reflection of natural images, in this paper we propose a novel feature learning framework for large-scale salient object detection.
Richer and Deeper Supervision Network for Salient Object Detection
Recent Salient Object Detection (SOD) systems are mostly based on Convolutional Neural Networks (CNNs).
Salient Object Detection via High-to-Low Hierarchical Context Aggregation
In this paper, we observe that the contexts of a natural image can be well expressed by a high-to-low self-learning of side-output convolutional features.
Selectivity or Invariance: Boundary-aware Salient Object Detection
In this network, the feature selectivity at boundaries is enhanced by incorporating a boundary localization stream, while the feature invariance at interiors is guaranteed with a complex interior perception stream.
Quality-Aware Multimodal Saliency Detection via Deep Reinforcement Learning
In this paper, we propose an efficient quality-aware deep neural network to model the weight of data from each domain using deep reinforcement learning (DRL).
