# DeepUSPS: Deep Robust Unsupervised Saliency Prediction With Self-Supervision

arXiv: https://arxiv.org/abs/1909.13055 · HTML: https://mlsanity.com/paper/1909.13055/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2019.

## Built on by (most cited first, 30 of 38)

- [Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localization](https://mlsanity.com/paper/2205.07839/) (2022)
- [ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation](https://mlsanity.com/paper/2111.10265/) (2021)
- [Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?](https://mlsanity.com/paper/2201.05119/) (2022)
- [Unsupervised Object Localization: Observing the Background to Discover Objects](https://mlsanity.com/paper/2212.07834/) (2022)
- [Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations](https://mlsanity.com/paper/2207.05027/) (2022)
- [DiffusionSeg: Adapting Diffusion Towards Unsupervised Object Discovery](https://mlsanity.com/paper/2303.09813/) (2023)
- [The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos](https://mlsanity.com/paper/2111.06394/) (2021)
- [Finding an Unsupervised Image Segmenter in Each of Your Deep Generative Models](https://mlsanity.com/paper/2105.08127/) (2021)
- [Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation](https://mlsanity.com/paper/2112.09645/) (2021)
- [Object-aware Contrastive Learning for Debiased Scene Representation](https://mlsanity.com/paper/2108.00049/) (2021)
- [Texture-guided Saliency Distilling for Unsupervised Salient Object Detection](https://mlsanity.com/paper/2207.05921/) (2022)
- [Salient Objects in Clutter](https://mlsanity.com/paper/2105.03053/) (2021)
- [Generative Transformer for Accurate and Reliable Salient Object Detection](https://mlsanity.com/paper/2104.10127/) (2021)
- [Few-Cost Salient Object Detection with Adversarial-Paced Learning](https://mlsanity.com/paper/2104.01928/) (2021)
- [Unsupervised Domain Adaptive Salient Object Detection Through Uncertainty-Aware Pseudo-Label Learning](https://mlsanity.com/paper/2202.13170/) (2022)
- [MOST: Multiple Object localization with Self-supervised Transformers for object discovery](https://mlsanity.com/paper/2304.05387/) (2023)
- [Object-centric Learning with Cyclic Walks between Parts and Whole](https://mlsanity.com/paper/2302.08023/) (2023)
- [Learning Co-segmentation by Segment Swapping for Retrieval and Discovery](https://mlsanity.com/paper/2110.15904/) (2021)
- [Visual Dependency Transformers: Dependency Tree Emerges from Reversed Attention](https://mlsanity.com/paper/2304.03282/) (2023)
- [ConCL: Concept Contrastive Learning for Dense Prediction Pre-training in Pathology Images](https://mlsanity.com/paper/2207.06733/) (2022)
- [SalFBNet: Learning Pseudo-Saliency Distribution via Feedback Convolutional Networks](https://mlsanity.com/paper/2112.03731/) (2021)
- [Characterizing and Improving the Robustness of Self-Supervised Learning through Background Augmentations](https://mlsanity.com/paper/2103.12719/) (2021)
- [Energy-Based Generative Cooperative Saliency Prediction](https://mlsanity.com/paper/2106.13389/) (2021)
- [Adaptive Low Rank Adaptation of Segment Anything to Salient Object Detection](https://mlsanity.com/paper/2308.05426/) (2023)
- [MaskSplit: Self-supervised Meta-learning for Few-shot Semantic Segmentation](https://mlsanity.com/paper/2110.12207/) (2021)
- [3SD: Self-Supervised Saliency Detection With No Labels](https://mlsanity.com/paper/2203.04478/) (2022)
- [Learning from Pixel-Level Noisy Label : A New Perspective for Light Field Saliency Detection](https://mlsanity.com/paper/2204.13456/) (2022)
- [Unsupervised Object Discovery: A Comprehensive Survey and Unified Taxonomy](https://mlsanity.com/paper/2411.00868/) (2024)
- [Bootstrap Representation Learning for Segmentation on Medical Volumes and Sequences](https://mlsanity.com/paper/2106.12153/) (2021)
- [Towards End-to-End Unsupervised Saliency Detection with Self-Supervised Top-Down Context](https://mlsanity.com/paper/2310.09533/) (2023)

## Abstract (arXiv)

Deep neural network (DNN) based salient object detection in images based on high-quality labels is expensive. Alternative unsupervised approaches rely on careful selection of multiple handcrafted saliency methods to generate noisy pseudo-ground-truth labels. In this work, we propose a two-stage mechanism for robust unsupervised object saliency prediction, where the first stage involves refinement of the noisy pseudo labels generated from different handcrafted methods. Each handcrafted method is substituted by a deep network that learns to generate the pseudo labels. These labels are refined incrementally in multiple iterations via our proposed self-supervision technique. In the second stage, the refined labels produced from multiple networks representing multiple saliency methods are used to train the actual saliency detection network. We show that this self-learning procedure outperforms all the existing unsupervised methods over different datasets. Results are even comparable to those of fully-supervised state-of-the-art approaches. The code is available at https://tinyurl.com/wtlhgo3 .