# Progressive Self-Guided Loss for Salient Object Detection

arXiv: https://arxiv.org/abs/2101.02412 · HTML: https://mlsanity.com/paper/2101.02412/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 57 references that are in the map.
- First posted on arXiv 7 Jan 2021.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [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)
- [A Simple Pooling-Based Design for Real-Time Salient Object Detection](https://mlsanity.com/paper/1904.09569/) (2019)
- [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)
- [Salient Object Detection: A Benchmark](https://mlsanity.com/paper/1501.02741/) (2015)
- [Salient Object Detection: A Survey](https://mlsanity.com/paper/1411.5878/) (2014)
- [Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection](https://mlsanity.com/paper/1708.02001/) (2017)
- [Salient Object Detection: A Discriminative Regional Feature Integration Approach](https://mlsanity.com/paper/1410.5926/) (2014)
- [STC: A Simple to Complex Framework for Weakly-supervised Semantic Segmentation](https://mlsanity.com/paper/1509.03150/) (2015)
- [Dense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images](https://mlsanity.com/paper/2011.13144/) (2020)
- [Review of Visual Saliency Detection with Comprehensive Information](https://mlsanity.com/paper/1803.03391/) (2018)
- [Deep Contrast Learning for Salient Object Detection](https://mlsanity.com/paper/1603.01976/) (2016)
- [DPANet: Depth Potentiality-Aware Gated Attention Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2003.08608/) (2020)
- [Learning Uncertain Convolutional Features for Accurate Saliency Detection](https://mlsanity.com/paper/1708.02031/) (2017)
- [A Dilated Inception Network for Visual Saliency Prediction](https://mlsanity.com/paper/1904.03571/) (2019)
- [Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation using Holistic Convolutional Networks](https://mlsanity.com/paper/1707.00478/) (2017)
- [Optimizing the F-measure for Threshold-free Salient Object Detection](https://mlsanity.com/paper/1805.07567/) (2018)
- [Bridging Saliency Detection to Weakly Supervised Object Detection Based on Self-paced Curriculum Learning](https://mlsanity.com/paper/1703.01290/) (2017)
- [What is a salient object? A dataset and a baseline model for salient object detection](https://mlsanity.com/paper/1412.5027/) (2014)
- [PISA: Pixelwise Image Saliency by Aggregating Complementary Appearance Contrast Measures with Edge-Preserving Coherence](https://mlsanity.com/paper/1505.03227/) (2015)

## Built on by (most cited first, 14 of 14)

- [Fantastic Animals and Where to Find Them: Segment Any Marine Animal with Dual SAM](https://mlsanity.com/paper/2404.04996/) (2024)
- [How is Visual Attention Influenced by Text Guidance? Database and Model](https://mlsanity.com/paper/2404.07537/) (2024)
- [View-aware Salient Object Detection for 360deg Omnidirectional Image](https://mlsanity.com/paper/2209.13222/) (2022)
- [Salient Object Detection for Point Clouds](https://mlsanity.com/paper/2207.11889/) (2022)
- [Salient Object Detection via Dynamic Scale Routing](https://mlsanity.com/paper/2210.13821/) (2022)
- [Rethinking Lightweight Salient Object Detection via Network Depth-Width Tradeoff](https://mlsanity.com/paper/2301.06679/) (2023)
- [Salient Object Detection From Arbitrary Modalities](https://mlsanity.com/paper/2405.03352/) (2024)
- [SODAWideNet -- Salient Object Detection with an Attention augmented Wide Encoder Decoder network without ImageNet pre-training](https://mlsanity.com/paper/2311.04828/) (2023)
- [SODAWideNet++: Combining Attention and Convolutions for Salient Object Detection](https://mlsanity.com/paper/2408.16645/) (2024)
- [Attention Guided Network for Salient Object Detection in Optical Remote Sensing Images](https://mlsanity.com/paper/2207.01755/) (2022)
- [A positive feedback method based on F-measure value for Salient Object Detection](https://mlsanity.com/paper/2304.14619/) (2023)
- [Robust Salient Object Detection on Compressed Images Using Convolutional Neural Networks](https://mlsanity.com/paper/2409.13464/) (2024)
- [Learning Spatially Adaptive Structural Coordination for Underwater Salient Object Detection](https://mlsanity.com/paper/2605.15535/) (2026)
- [MIMONet: Multi-scale Input and Multi-scale Output Network for Salient Object Detection](https://mlsanity.com/paper/2608.25733/) (2026)

## Abstract (arXiv)

We present a simple yet effective progressive self-guided loss function to facilitate deep learning-based salient object detection (SOD) in images. The saliency maps produced by the most relevant works still suffer from incomplete predictions due to the internal complexity of salient objects. Our proposed progressive self-guided loss simulates a morphological closing operation on the model predictions for progressively creating auxiliary training supervisions to step-wisely guide the training process. We demonstrate that this new loss function can guide the SOD model to highlight more complete salient objects step-by-step and meanwhile help to uncover the spatial dependencies of the salient object pixels in a region growing manner. Moreover, a new feature aggregation module is proposed to capture multi-scale features and aggregate them adaptively by a branch-wise attention mechanism. Benefiting from this module, our SOD framework takes advantage of adaptively aggregated multi-scale features to locate and detect salient objects effectively. Experimental results on several benchmark datasets show that our loss function not only advances the performance of existing SOD models without architecture modification but also helps our proposed framework to achieve state-of-the-art performance.