# Few-Cost Salient Object Detection with Adversarial-Paced Learning

arXiv: https://arxiv.org/abs/2104.01928 · HTML: https://mlsanity.com/paper/2104.01928/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 60 references that are in the map.
- First posted on arXiv 5 Apr 2021.

## Builds on (most cited first, 20 of 20)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [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)
- [Visual Saliency Based on Multiscale Deep Features](https://mlsanity.com/paper/1503.08663/) (2015)
- [Adversarial Learning for Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/1802.07934/) (2018)
- [Weakly-Supervised Salient Object Detection via Scribble Annotations](https://mlsanity.com/paper/2003.07685/) (2020)
- [Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning](https://mlsanity.com/paper/1910.02224/) (2019)
- [Semi-Supervised Video Salient Object Detection Using Pseudo-Labels](https://mlsanity.com/paper/1908.04051/) (2019)
- [Re-thinking Co-Salient Object Detection](https://mlsanity.com/paper/2007.03380/) (2020)
- [Deep Unsupervised Saliency Detection: A Multiple Noisy Labeling Perspective](https://mlsanity.com/paper/1803.10910/) (2018)
- [Multi-source weak supervision for saliency detection](https://mlsanity.com/paper/1904.00566/) (2019)
- [DeepUSPS: Deep Robust Unsupervised Saliency Prediction With Self-Supervision](https://mlsanity.com/paper/1909.13055/) (2019)
- [Weakly Supervised Salient Object Detection Using Image Labels](https://mlsanity.com/paper/1803.06503/) (2018)
- [Self-Paced Multi-Task Learning](https://mlsanity.com/paper/1604.01474/) (2016)
- [What Objective Does Self-paced Learning Indeed Optimize?](https://mlsanity.com/paper/1511.06049/) (2015)
- [Global Sum Pooling: A Generalization Trick for Object Counting with Small Datasets of Large Images](https://mlsanity.com/paper/1805.11123/) (2018)

## Built on by (most cited first, 17 of 17)

- [Global-and-Local Collaborative Learning for Co-Salient Object Detection](https://mlsanity.com/paper/2204.08917/) (2022)
- [Fantastic Animals and Where to Find Them: Segment Any Marine Animal with Dual SAM](https://mlsanity.com/paper/2404.04996/) (2024)
- [Synthetic Data Supervised Salient Object Detection](https://mlsanity.com/paper/2210.13835/) (2022)
- [Multi-Projection Fusion and Refinement Network for Salient Object Detection in 360deg Omnidirectional Image](https://mlsanity.com/paper/2212.12378/) (2022)
- [Adversarial Dual-Student with Differentiable Spatial Warping for Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2203.02792/) (2022)
- [Self-Ensembling GAN for Cross-Domain Semantic Segmentation](https://mlsanity.com/paper/2112.07999/) (2021)
- [PLGAN: Generative Adversarial Networks for Power-Line Segmentation in Aerial Images](https://mlsanity.com/paper/2204.07243/) (2022)
- [Unsupervised and semi-supervised co-salient object detection via segmentation frequency statistics](https://mlsanity.com/paper/2311.06654/) (2023)
- [Middle-level Fusion for Lightweight RGB-D Salient Object Detection](https://mlsanity.com/paper/2104.11543/) (2021)
- [Semi-supervised Salient Object Detection with Effective Confidence Estimation](https://mlsanity.com/paper/2112.14019/) (2021)
- [Self-directed Machine Learning](https://mlsanity.com/paper/2201.01289/) (2022)
- [Dual Swin-Transformer based Mutual Interactive Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2206.03105/) (2022)
- [Perception-and-Regulation Network for Salient Object Detection](https://mlsanity.com/paper/2107.12560/) (2021)
- [Pixel is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detection](https://mlsanity.com/paper/2212.06493/) (2022)
- [Break The Spell Of Total Correlation In betaTCVAE](https://mlsanity.com/paper/2210.08794/) (2022)
- [Confidence-Aware Active Feedback for Interactive Instance Search](https://mlsanity.com/paper/2110.12255/) (2021)
- [Learning Spatially Adaptive Structural Coordination for Underwater Salient Object Detection](https://mlsanity.com/paper/2605.15535/) (2026)

## Abstract (arXiv)

Detecting and segmenting salient objects from given image scenes has received great attention in recent years. A fundamental challenge in training the existing deep saliency detection models is the requirement of large amounts of annotated data. While gathering large quantities of training data becomes cheap and easy, annotating the data is an expensive process in terms of time, labor and human expertise. To address this problem, this paper proposes to learn the effective salient object detection model based on the manual annotation on a few training images only, thus dramatically alleviating human labor in training models. To this end, we name this task as the few-cost salient object detection and propose an adversarial-paced learning (APL)-based framework to facilitate the few-cost learning scenario. Essentially, APL is derived from the self-paced learning (SPL) regime but it infers the robust learning pace through the data-driven adversarial learning mechanism rather than the heuristic design of the learning regularizer. Comprehensive experiments on four widely-used benchmark datasets demonstrate that the proposed method can effectively approach to the existing supervised deep salient object detection models with only 1k human-annotated training images. The project page is available at https://github.com/hb-stone/FC-SOD.