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Found 3 papers, 2 papers with code
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.
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.
