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Found 4 papers, 2 papers with code
A Visual Representation-guided Framework with Global Affinity for Weakly Supervised Salient Object Detection
Fully supervised salient object detection (SOD) methods have made considerable progress in performance, yet these models rely heavily on expensive pixel-wise labels.
Synthesize Boundaries: A Boundary-aware Self-consistent Framework for Weakly Supervised Salient Object Detection
BAB aims to help predict accurate boundaries, whose input is the synthetic image.
Motion-aware Memory Network for Fast Video Salient Object Detection
Moreover, inspired by the boundary supervision commonly used in image salient object detection (ISOD), we design a motion-aware loss for predicting object boundary motion and simultaneously perform multitask learning for VSOD and object motion prediction, which can further facilitate the model to extract spatiotemporal features accurately and maintain the object integrity.
Learning Video Salient Object Detection Progressively from Unlabeled Videos
Recent deep learning-based video salient object detection (VSOD) has achieved some breakthrough, but these methods rely on expensive annotated videos with pixel-wise annotations, weak annotations, or part of the pixel-wise annotations.
