# Texture-guided Saliency Distilling for Unsupervised Salient Object Detection

arXiv: https://arxiv.org/abs/2207.05921 · HTML: https://mlsanity.com/paper/2207.05921/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 85 references that are in the map.
- First posted on arXiv 13 Jul 2022.

## Builds on (most cited first, 30 of 38)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Enhanced-alignment Measure for Binary Foreground Map Evaluation](https://mlsanity.com/paper/1805.10421/) (2018)
- [EGNet:Edge Guidance Network for Salient Object Detection](https://mlsanity.com/paper/1908.08297/) (2019)
- [Cascaded Partial Decoder for Fast and Accurate Salient Object Detection](https://mlsanity.com/paper/1904.08739/) (2019)
- [Multi-scale Interactive Network for Salient Object Detection](https://mlsanity.com/paper/2007.09062/) (2020)
- [The Secrets of Salient Object Segmentation](https://mlsanity.com/paper/1406.2807/) (2014)
- [Deeply supervised salient object detection with short connections](https://mlsanity.com/paper/1611.04849/) (2016)
- [Visual Saliency Based on Multiscale Deep Features](https://mlsanity.com/paper/1503.08663/) (2015)
- [Salient Object Detection in the Deep Learning Era: An In-Depth Survey](https://mlsanity.com/paper/1904.09146/) (2019)
- [Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks](https://mlsanity.com/paper/1907.06781/) (2019)
- [Railroad is not a Train: Saliency as Pseudo-pixel Supervision for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2105.08965/) (2021)
- [Suppress and Balance: A Simple Gated Network for Salient Object Detection](https://mlsanity.com/paper/2007.08074/) (2020)
- [Label Decoupling Framework for Salient Object Detection](https://mlsanity.com/paper/2008.11048/) (2020)
- [Weakly-Supervised Salient Object Detection via Scribble Annotations](https://mlsanity.com/paper/2003.07685/) (2020)
- [Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection](https://mlsanity.com/paper/1708.02001/) (2017)
- [Specificity-preserving RGB-D Saliency Detection](https://mlsanity.com/paper/2108.08162/) (2021)
- [RGBT Salient Object Detection: A Large-scale Dataset and Benchmark](https://mlsanity.com/paper/2007.03262/) (2020)
- [RGB-T Image Saliency Detection via Collaborative Graph Learning](https://mlsanity.com/paper/1905.06741/) (2019)
- [Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion](https://mlsanity.com/paper/2103.11832/) (2021)
- [Unsupervised Salient Object Detection with Spectral Cluster Voting](https://mlsanity.com/paper/2203.12614/) (2022)
- [Structure-Consistent Weakly Supervised Salient Object Detection with Local Saliency Coherence](https://mlsanity.com/paper/2012.04404/) (2020)
- [A Unified RGB-T Saliency Detection Benchmark: Dataset, Baselines, Analysis and A Novel Approach](https://mlsanity.com/paper/1701.02829/) (2017)
- [Deep Unsupervised Saliency Detection: A Multiple Noisy Labeling Perspective](https://mlsanity.com/paper/1803.10910/) (2018)
- [Learning Uncertain Convolutional Features for Accurate Saliency Detection](https://mlsanity.com/paper/1708.02031/) (2017)
- [Multi-interactive Dual-decoder for RGB-thermal Salient Object Detection](https://mlsanity.com/paper/2005.02315/) (2020)
- [DeepUSPS: Deep Robust Unsupervised Saliency Prediction With Self-Supervision](https://mlsanity.com/paper/1909.13055/) (2019)

## Built on by (most cited first, 24 of 24)

- [ZoomNeXt: A Unified Collaborative Pyramid Network for Camouflaged Object Detection](https://mlsanity.com/paper/2310.20208/) (2023)
- [Seg-R1: Segmentation Can Be Surprisingly Simple with Reinforcement Learning](https://mlsanity.com/paper/2506.22624/) (2025)
- [A Simple yet Effective Network based on Vision Transformer for Camouflaged Object and Salient Object Detection](https://mlsanity.com/paper/2402.18922/) (2024)
- [Towards Training-free Open-world Segmentation via Image Prompt Foundation Models](https://mlsanity.com/paper/2310.10912/) (2023)
- [Multimodal Data Integration for Precision Oncology: Challenges and Future Directions](https://mlsanity.com/paper/2406.19611/) (2024)
- [Unified-modal Salient Object Detection via Adaptive Prompt Learning](https://mlsanity.com/paper/2311.16835/) (2023)
- [Unsupervised Skin Lesion Segmentation via Structural Entropy Minimization on Multi-Scale Superpixel Graphs](https://mlsanity.com/paper/2309.01899/) (2023)
- [SSFam: Scribble Supervised Salient Object Detection Family](https://mlsanity.com/paper/2409.04817/) (2024)
- [Unified Unsupervised Salient Object Detection via Knowledge Transfer](https://mlsanity.com/paper/2404.14759/) (2024)
- [Zero-Shot Co-salient Object Detection Framework](https://mlsanity.com/paper/2309.05499/) (2023)
- [Reframe Anything: LLM Agent for Open World Video Reframing](https://mlsanity.com/paper/2403.06070/) (2024)
- [From Sight to Insight: Unleashing Eye-Tracking in Weakly Supervised Video Salient Object Detection](https://mlsanity.com/paper/2506.23519/) (2025)
- [Distribution-Specific Learning for Joint Salient and Camouflaged Object Detection](https://mlsanity.com/paper/2508.06063/) (2025)
- [USL-Net: Uncertainty Self-Learning Network for Unsupervised Skin Lesion Segmentation](https://mlsanity.com/paper/2309.13289/) (2023)
- [Towards End-to-End Unsupervised Saliency Detection with Self-Supervised Top-Down Context](https://mlsanity.com/paper/2310.09533/) (2023)
- [Deep Fourier-embedded Network for RGB and Thermal Salient Object Detection](https://mlsanity.com/paper/2411.18409/) (2024)
- [Marine Saliency Segmenter: Object-Focused Conditional Diffusion with Region-Level Semantic Knowledge Distillation](https://mlsanity.com/paper/2504.02391/) (2025)
- [HyPSAM: Hybrid Prompt-driven Segment Anything Model for RGB-Thermal Salient Object Detection](https://mlsanity.com/paper/2509.18738/) (2025)
- [EReCu: Pseudo-label Evolution Fusion and Refinement with Multi-Cue Learning for Unsupervised Camouflage Detection](https://mlsanity.com/paper/2603.11521/) (2026)
- [Unsupervised Segmentation of Colonoscopy Images](https://mlsanity.com/paper/2312.12599/) (2023)
- [On-Road Object Importance Estimation: A New Dataset and A Model with Multi-Fold Top-Down Guidance](https://mlsanity.com/paper/2411.17152/) (2024)
- [Count2Density: Crowd Density Estimation without Location-level Annotations](https://mlsanity.com/paper/2509.03170/) (2025)
- [Aesthetic Image Captioning with Saliency Enhanced MLLMs](https://mlsanity.com/paper/2509.04378/) (2025)
- [Can Frozen Hyperspherical Features Guide the Selection of Pseudo Masks?](https://mlsanity.com/paper/2609.30080/) (2026)

## Abstract (arXiv)

Deep Learning-based Unsupervised Salient Object Detection (USOD) mainly relies on the noisy saliency pseudo labels that have been generated from traditional handcraft methods or pre-trained networks. To cope with the noisy labels problem, a class of methods focus on only easy samples with reliable labels but ignore valuable knowledge in hard samples. In this paper, we propose a novel USOD method to mine rich and accurate saliency knowledge from both easy and hard samples. First, we propose a Confidence-aware Saliency Distilling (CSD) strategy that scores samples conditioned on samples' confidences, which guides the model to distill saliency knowledge from easy samples to hard samples progressively. Second, we propose a Boundary-aware Texture Matching (BTM) strategy to refine the boundaries of noisy labels by matching the textures around the predicted boundary. Extensive experiments on RGB, RGB-D, RGB-T, and video SOD benchmarks prove that our method achieves state-of-the-art USOD performance.