# Modeling Object Dissimilarity for Deep Saliency Prediction

arXiv: https://arxiv.org/abs/2104.03864 · HTML: https://mlsanity.com/paper/2104.03864/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 78 references that are in the map.
- First posted on arXiv 8 Apr 2021.

## Builds on (most cited first, 23 of 23)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Focal Loss for Dense Object Detection](https://mlsanity.com/paper/1708.02002/) (2017)
- [What do different evaluation metrics tell us about saliency models?](https://mlsanity.com/paper/1604.03605/) (2016)
- [Salient Object Detection in the Deep Learning Era: An In-Depth Survey](https://mlsanity.com/paper/1904.09146/) (2019)
- [Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model](https://mlsanity.com/paper/1611.09571/) (2016)
- [Unified Image and Video Saliency Modeling](https://mlsanity.com/paper/2003.05477/) (2020)
- [Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet](https://mlsanity.com/paper/1411.1045/) (2014)
- [Unsupervised Attention-guided Image to Image Translation](https://mlsanity.com/paper/1806.02311/) (2018)
- [DeepGaze II: Reading fixations from deep features trained on object recognition](https://mlsanity.com/paper/1610.01563/) (2016)
- [A Deep Multi-Level Network for Saliency Prediction](https://mlsanity.com/paper/1609.01064/) (2016)
- [CAT2000: A Large Scale Fixation Dataset for Boosting Saliency Research](https://mlsanity.com/paper/1505.03581/) (2015)
- [EML-NET:An Expandable Multi-Layer NETwork for Saliency Prediction](https://mlsanity.com/paper/1805.01047/) (2018)
- [DeepFix: A Fully Convolutional Neural Network for predicting Human Eye Fixations](https://mlsanity.com/paper/1510.02927/) (2015)
- [Contextual Encoder-Decoder Network for Visual Saliency Prediction](https://mlsanity.com/paper/1902.06634/) (2019)
- [Shallow and Deep Convolutional Networks for Saliency Prediction](https://mlsanity.com/paper/1603.00845/) (2016)
- [Recurrent Transformer Networks for Semantic Correspondence](https://mlsanity.com/paper/1810.12155/) (2018)
- [A Deep Spatial Contextual Long-term Recurrent Convolutional Network for Saliency Detection](https://mlsanity.com/paper/1610.01708/) (2016)
- [A Dilated Inception Network for Visual Saliency Prediction](https://mlsanity.com/paper/1904.03571/) (2019)
- [End-to-End Saliency Mapping via Probability Distribution Prediction](https://mlsanity.com/paper/1804.01793/) (2018)
- [Saliency Prediction with External Knowledge](https://mlsanity.com/paper/2007.13839/) (2020)
- [Evaluating Salient Object Detection in Natural Images with Multiple Objects having Multi-level Saliency](https://mlsanity.com/paper/2003.08514/) (2020)

## Built on by (most cited first, 2 of 2)

- [TempSAL -- Uncovering Temporal Information for Deep Saliency Prediction](https://mlsanity.com/paper/2301.02315/) (2023)
- [Data Augmentation via Latent Diffusion for Saliency Prediction](https://mlsanity.com/paper/2409.07307/) (2024)

## Abstract (arXiv)

Saliency prediction has made great strides over the past two decades, with current techniques modeling low-level information, such as color, intensity and size contrasts, and high-level ones, such as attention and gaze direction for entire objects. Despite this, these methods fail to account for the dissimilarity between objects, which affects human visual attention. In this paper, we introduce a detection-guided saliency prediction network that explicitly models the differences between multiple objects, such as their appearance and size dissimilarities. Our approach allows us to fuse our object dissimilarities with features extracted by any deep saliency prediction network. As evidenced by our experiments, this consistently boosts the accuracy of the baseline networks, enabling us to outperform the state-of-the-art models on three saliency benchmarks, namely SALICON, MIT300 and CAT2000. Our project page is at https://github.com/IVRL/DisSal.