  Learning computational models for visual attention (saliency estimation) is
an effort to inch machines/robots closer to human visual cognitive abilities.
Data-driven efforts have dominated the landscape since the introduction of deep
neural network architectures. In deep learning research, the choices in
architecture design are often empirical and frequently lead to more complex
models than necessary. The complexity, in turn, hinders the application
requirements. In this paper, we identify four key components of saliency
models, i.e., input features, multi-level integration, readout architecture,
and loss functions. We review the existing state of the art models on these
four components and propose novel and simpler alternatives. As a result, we
propose two novel end-to-end architectures called SimpleNet and MDNSal, which
are neater, minimal, more interpretable and achieve state of the art
performance on public saliency benchmarks. SimpleNet is an optimized
encoder-decoder architecture and brings notable performance gains on the
SALICON dataset (the largest saliency benchmark). MDNSal is a parametric model
that directly predicts parameters of a GMM distribution and is aimed to bring
more interpretability to the prediction maps. The proposed saliency models can
be inferred at 25fps, making them suitable for real-time applications. Code and
pre-trained models are available at https://github.com/samyak0210/saliency.
