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To solve this problem, we propose Pyramid Feature Attention network to focus on effective high-level context features and low-level spatial structural features.
SOTA for Saliency Detection on DUTS-test
We formulate the proposed PiCANet in both global and local forms to attend to global and local contexts, respectively.
The latent spaces of typical GAN models often have semantically meaningful directions.
In this work we develop a fast saliency detection method that can be applied to any differentiable image classifier.
In this paper, we develop a multi-task motion guided video salient object detection network, which learns to accomplish two sub-tasks using two sub-networks, one sub-network for salient object detection in still images and the other for motion saliency detection in optical flow images.
Recent advances in saliency detection have utilized deep learning to obtain high level features to detect salient regions in a scene.
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.
To this end, we propose a unified framework to train saliency detection models with diverse weak supervision sources.