Saliency Detection is a preprocessing step in computer vision which aims at finding salient objects in an image.
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It consists of two building blocks: first, the encoder network extracts low-resolution spatiotemporal features from an input clip of several consecutive frames, and then the following prediction network decodes the encoded features spatially while aggregating all the temporal information.
Ranked #1 on Video Saliency Detection on DHF1K
To this end, we propose a unified framework to train saliency detection models with diverse weak supervision sources.
Recent advances in saliency detection have utilized deep learning to obtain high level features to detect salient regions in a scene.
Specifically, our decoder consists of two branches, a saliency branch and a contour branch.
Considering the reliability of the other modality's attention, we further propose a selection attention to weight the newly added attention term.
Ranked #10 on RGB-D Salient Object Detection on NJU2K
Many works have been done on salient object detection using supervised or unsupervised approaches on colour images.