mlsanityData to 7 Oct 2026

Modeling Object Dissimilarity for Deep Saliency Prediction

arXiv 2104.03864 ↗First posted 8 Apr 2021cs.CVcited by 2

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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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 23 of its 78 references that are in the map. First posted on arXiv 8 Apr 2021.

Builds on

23 of 78 references.

References · 23

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
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  3. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  4. 2017Focal Loss for Dense Object Detectioncited by 749
  5. 2016What do different evaluation metrics tell us about saliency models?cited by 184
  6. 2019Salient Object Detection in the Deep Learning Era: An In-Depth Surveycited by 163
  7. 2016Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Modelcited by 91
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  12. 2016DeepGaze II: Reading fixations from deep features trained on object recognitioncited by 54
11 more
  1. 2015CAT2000: A Large Scale Fixation Dataset for Boosting Saliency Researchcited by 52
  2. 2018EML-NET:An Expandable Multi-Layer NETwork for Saliency Predictioncited by 51
  3. 2015DeepFix: A Fully Convolutional Neural Network for predicting Human Eye Fixationscited by 47
  4. 2019Contextual Encoder-Decoder Network for Visual Saliency Predictioncited by 44
  5. 2016Shallow and Deep Convolutional Networks for Saliency Predictioncited by 42
  6. 2018Recurrent Transformer Networks for Semantic Correspondencecited by 32
  7. 2016A Deep Spatial Contextual Long-term Recurrent Convolutional Network for Saliency Detectioncited by 23
  8. 2019A Dilated Inception Network for Visual Saliency Predictioncited by 22
  9. 2018End-to-End Saliency Mapping via Probability Distribution Predictioncited by 17
  10. 2020Saliency Prediction with External Knowledgecited by 5
  11. 2020Evaluating Salient Object Detection in Natural Images with Multiple Objects having Multi-level Saliencycited by 4

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Citing papers per year: 2023: 1; 2024: 1.

2 papers.

  1. 2023TempSAL -- Uncovering Temporal Information for Deep Saliency Predictioncited by 25
  2. 2024Data Augmentation via Latent Diffusion for Saliency Predictioncited by 5