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Fast and Efficient Once-For-All Networks for Diverse Hardware Deployment
In this work, we continue the effort to reduce the training cost of OFA methods.
SALICON: Reducing the Semantic Gap in Saliency Prediction by Adapting Deep Neural Networks
Saliency in Context (SALICON) is an ongoing effort that aims at understanding and predicting visual attention.
Learning of Proto-object Representations via Fixations on Low Resolution
Visualizations also show that these features are selective to potential objects in the scene and the responses of these features work well in predicting eye fixations on the images when combined with learned weights.
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