Salient Object Detection
235 papers with code • 6 benchmarks • 16 datasets
Libraries
Use these libraries to find Salient Object Detection models and implementations
PASCAL-S
DUTS
HKU-IS
DUT-OMRON
ECSSD
SOC
VT5000
HRSOD
EORSSD
Latest papers
Unified Unsupervised Salient Object Detection via Knowledge Transfer
Firstly, we propose a Progressive Curriculum Learning-based Saliency Distilling (PCL-SD) mechanism to extract saliency cues from a pre-trained deep network.
DMSSN: Distilled Mixed Spectral-Spatial Network for Hyperspectral Salient Object Detection
To address these challenges, we propose a novel approach termed the Distilled Mixed Spectral-Spatial Network (DMSSN), comprising a Distilled Spectral Encoding process and a Mixed Spectral-Spatial Transformer (MSST) feature extraction network.
Self-supervised co-salient object detection via feature correspondence at multiple scales
Extensive experiments on three CoSOD benchmark datasets show that our self-supervised model outperforms the corresponding state-of-the-art models by a huge margin (e. g. on the CoCA dataset, our model has a 13. 7% F-measure gain over the SOTA unsupervised CoSOD model).
LF Tracy: A Unified Single-Pipeline Approach for Salient Object Detection in Light Field Cameras
Previous approaches predominantly employ a custom two-stream design to discover the implicit angular feature within light field cameras, leading to significant information isolation between different LF representations.
Rethinking Object Saliency Ranking: A Novel Whole-flow Processing Paradigm
mengkesong/saliency-ranking-paradigm
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To conquer, this paper proposes a new paradigm for saliency ranking, which aims to completely focus on ranking salient objects by their "importance order".
Texture-Semantic Collaboration Network for ORSI Salient Object Detection
mathlee/tscnet
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In the TSCM, we first enhance the position of potential salient regions using semantic features.
Spectrum-driven Mixed-frequency Network for Hyperspectral Salient Object Detection
laprf/smn
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The Spectral Saliency approximates the region of salient objects, while the Spectral Edge captures edge information of salient objects.
Efficient Multimodal Semantic Segmentation via Dual-Prompt Learning
Existing approaches often fully fine-tune a dual-branch encoder-decoder framework with a complicated feature fusion strategy for achieving multimodal semantic segmentation, which is training-costly due to the massive parameter updates in feature extraction and fusion.
VSCode: General Visual Salient and Camouflaged Object Detection with 2D Prompt Learning
Salient object detection (SOD) and camouflaged object detection (COD) are related yet distinct binary mapping tasks.
SODAWideNet -- Salient Object Detection with an Attention augmented Wide Encoder Decoder network without ImageNet pre-training
To achieve a shallower network, we increase the receptive field from the beginning of the network using a combination of dilated convolutions and self-attention.
