RGB Salient Object Detection
86 papers with code • 11 benchmarks • 12 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 )
We evaluate the Res2Net block on all these models and demonstrate consistent performance gains over baseline models on widely-used datasets, e. g., CIFAR-100 and ImageNet.
Ranked #6 on RGB Salient Object Detection on PASCAL-S
In this paper, we design a simple yet powerful deep network architecture, U$^2$-Net, for salient object detection (SOD).
Ranked #1 on Salient Object Detection on HKU-IS
In this paper, we propose a predict-refine architecture, BASNet, and a new hybrid loss for Boundary-Aware Salient object detection.
Ranked #1 on Salient Object Detection on DUTS-TE
We further design a feature aggregation module (FAM) to make the coarse-level semantic information well fused with the fine-level features from the top-down pathway.
Ranked #1 on RGB Salient Object Detection on SOD
In the second step, we integrate the local edge information and global location information to obtain the salient edge features.
Ranked #2 on Co-Salient Object Detection on CoSOD3k
In this paper, we propose a novel Cascaded Partial Decoder (CPD) framework for fast and accurate salient object detection.
Ranked #1 on RGB Salient Object Detection on ISTD
Recent progress on saliency detection is substantial, benefiting mostly from the explosive development of Convolutional Neural Networks (CNNs).
Ranked #4 on RGB Salient Object Detection on SBU
Salient object detection models often demand a considerable amount of computation cost to make precise prediction for each pixel, making them hardly applicable on low-power devices.
To obtain more efficient multi-scale features from the integrated features, the self-interaction modules are embedded in each decoder unit.