# A Dilated Inception Network for Visual Saliency Prediction

arXiv: https://arxiv.org/abs/1904.03571 · HTML: https://mlsanity.com/paper/1904.03571/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2019.

## Built on by (most cited first, 22 of 22)

- [DeepGaze IIE: Calibrated prediction in and out-of-domain for state-of-the-art saliency modeling](https://mlsanity.com/paper/2105.12441/) (2021)
- [In the Eye of Transformer: Global-Local Correlation for Egocentric Gaze Estimation](https://mlsanity.com/paper/2208.04464/) (2022)
- [TempSAL -- Uncovering Temporal Information for Deep Saliency Prediction](https://mlsanity.com/paper/2301.02315/) (2023)
- [Spatio-Temporal Self-Attention Network for Video Saliency Prediction](https://mlsanity.com/paper/2108.10696/) (2021)
- [Progressive Self-Guided Loss for Salient Object Detection](https://mlsanity.com/paper/2101.02412/) (2021)
- [SalFBNet: Learning Pseudo-Saliency Distribution via Feedback Convolutional Networks](https://mlsanity.com/paper/2112.03731/) (2021)
- [UniAR: A Unified model for predicting human Attention and Responses on visual content](https://mlsanity.com/paper/2312.10175/) (2023)
- [How is Visual Attention Influenced by Text Guidance? Database and Model](https://mlsanity.com/paper/2404.07537/) (2024)
- [Data Augmentation via Latent Diffusion for Saliency Prediction](https://mlsanity.com/paper/2409.07307/) (2024)
- [What Do Deep Saliency Models Learn about Visual Attention?](https://mlsanity.com/paper/2310.09679/) (2023)
- [How Does Audio Influence Visual Attention in Omnidirectional Videos? Database and Model](https://mlsanity.com/paper/2408.05411/) (2024)
- [Predicting Visual Attention in Graphic Design Documents](https://mlsanity.com/paper/2407.02439/) (2024)
- [Pluralistic Salient Object Detection](https://mlsanity.com/paper/2409.02368/) (2024)
- [Modeling Object Dissimilarity for Deep Saliency Prediction](https://mlsanity.com/paper/2104.03864/) (2021)
- [SPQE: Structure-and-Perception-Based Quality Evaluation for Image Super-Resolution](https://mlsanity.com/paper/2205.03584/) (2022)
- [Context-empowered Visual Attention Prediction in Pedestrian Scenarios](https://mlsanity.com/paper/2210.16933/) (2022)
- [Dynamic Graph Modeling of Simultaneous EEG and Eye-tracking Data for Reading Task Identification](https://mlsanity.com/paper/2102.11922/) (2021)
- [SalNAS: Efficient Saliency-prediction Neural Architecture Search with self-knowledge distillation](https://mlsanity.com/paper/2407.20062/) (2024)
- [Deriving Explanation of Deep Visual Saliency Models](https://mlsanity.com/paper/2109.03575/) (2021)
- [Pixel-by-pixel Mean Opinion Score (pMOS) for No-Reference Image Quality Assessment](https://mlsanity.com/paper/2206.06541/) (2022)
- [EMC2A-Net: An Efficient Multibranch Cross-channel Attention Network for SAR Target Classification](https://mlsanity.com/paper/2208.01836/) (2022)
- [Semantic Segmentation Enhanced Transformer Model for Human Attention Prediction](https://mlsanity.com/paper/2301.11022/) (2023)

## Abstract (arXiv)

Recently, with the advent of deep convolutional neural networks (DCNN), the improvements in visual saliency prediction research are impressive. One possible direction to approach the next improvement is to fully characterize the multi-scale saliency-influential factors with a computationally-friendly module in DCNN architectures. In this work, we proposed an end-to-end dilated inception network (DINet) for visual saliency prediction. It captures multi-scale contextual features effectively with very limited extra parameters. Instead of utilizing parallel standard convolutions with different kernel sizes as the existing inception module, our proposed dilated inception module (DIM) uses parallel dilated convolutions with different dilation rates which can significantly reduce the computation load while enriching the diversity of receptive fields in feature maps. Moreover, the performance of our saliency model is further improved by using a set of linear normalization-based probability distribution distance metrics as loss functions. As such, we can formulate saliency prediction as a probability distribution prediction task for global saliency inference instead of a typical pixel-wise regression problem. Experimental results on several challenging saliency benchmark datasets demonstrate that our DINet with proposed loss functions can achieve state-of-the-art performance with shorter inference time.