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Found 15 papers, 3 papers with code
Towards Unbalanced Motion: Part-Decoupling Network for Video Portrait Segmentation
That is, motion of different parts of the portraits is unbalanced.
Rethinking Lightweight Salient Object Detection via Network Depth-Width Tradeoff
To this end, we design a lightweight framework while maintaining satisfying competitive accuracy.
Pyramid Grafting Network for One-Stage High Resolution Saliency Detection
An attention-based Cross-Model Grafting Module (CMGM) is proposed to enable CNN branch to combine broken detailed information more holistically, guided by different source feature during decoding process.
Ranked #5 on
RGB Salient Object Detection
on UHRSD
(using extra training data)
Receptive Field Broadening and Boosting for Salient Object Detection
Moreover, Unlike multi-path parallel training, MHB randomly selects one branch each time for gradient back propagation in a boosting way.
Exploring Driving-aware Salient Object Detection via Knowledge Transfer
In this network, we construct an attentionbased knowledge transfer module to make up the knowledge difference.
Salient Object Detection with Purificatory Mechanism and Structural Similarity Loss
Through these two attentions, we use the Purificatory Mechanism to impose strict weights with different regions of the whole salient objects and purify results from hard-to-distinguish regions, thus accurately predicting the locations and details of salient objects.
Exploring Reciprocal Attention for Salient Object Detection by Cooperative Learning
Typically, objects with the same semantics are not always prominent in images containing different backgrounds.
Distortion-adaptive Salient Object Detection in 360$^\circ$ Omnidirectional Images
Moreover, benchmarking results of the proposed baseline approach and other methods on 360$^\circ$ SOD dataset show the proposed dataset is very challenging, which also validate the usefulness of the proposed dataset and approach to boost the development of SOD on 360$^\circ$ omnidirectional scenes.
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.
Learning a Saliency Evaluation Metric Using Crowdsourced Perceptual Judgments
Due to the effectiveness of the learned metric, it also can be used to facilitate the development of new models for fixation prediction.
What Is and What Is Not a Salient Object? Learning Salient Object Detector by Ensembling Linear Exemplar Regressors
Finding what is and what is not a salient object can be helpful in developing better features and models in salient object detection (SOD).
A Benchmark Dataset and Saliency-guided Stacked Autoencoders for Video-based Salient Object Detection
Based on this dataset, this paper proposes an unsupervised baseline approach for video-based SOD by using saliency-guided stacked autoencoders.
A Data-Driven Metric for Comprehensive Evaluation of Saliency Models
To address this problem, we propose a data-driven metric for comprehensive evaluation of saliency models.
Semantic Object Segmentation via Detection in Weakly Labeled Video
Semantic object segmentation in video is an important step for large-scale multimedia analysis.
