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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
The latent spaces of GAN models often have semantically meaningful directions.
We formulate the proposed PiCANet in both global and local forms to attend to global and local contexts, respectively.
#4 best model for Salient Object Detection on DUTS-TE
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.
However, since generative models are known to be unstable and sensitive to hyperparameters, the training of these methods can be challenging and time-consuming.
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.
Recent advances in saliency detection have utilized deep learning to obtain high level features to detect salient regions in a scene.
To this end, we propose a unified framework to train saliency detection models with diverse weak supervision sources.