Salient Object Detection
234 papers with code • 6 benchmarks • 16 datasets
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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
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
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
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.
Salient Object Detection in RGB-D Videos
Ablation experiments were performed on both pseudo and realistic RGB-D video datasets to demonstrate the advantages of individual modules as well as the necessity of introducing realistic depth.