# A Novel Long-term Iterative Mining Scheme for Video Salient Object Detection

arXiv: https://arxiv.org/abs/2206.09564 · HTML: https://mlsanity.com/paper/2206.09564/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 95 references that are in the map.
- First posted on arXiv 20 Jun 2022.

## Builds on (most cited first, 30 of 37)

- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017)
- [Structure-measure: A New Way to Evaluate Foreground Maps](https://mlsanity.com/paper/1708.00786/) (2017)
- [U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection](https://mlsanity.com/paper/2005.09007/) (2020)
- [Exploring Cross-Image Pixel Contrast for Semantic Segmentation](https://mlsanity.com/paper/2101.11939/) (2021)
- [Cascaded Partial Decoder for Fast and Accurate Salient Object Detection](https://mlsanity.com/paper/1904.08739/) (2019)
- [Deeply supervised salient object detection with short connections](https://mlsanity.com/paper/1611.04849/) (2016)
- [Salient Object Detection in the Deep Learning Era: An In-Depth Survey](https://mlsanity.com/paper/1904.09146/) (2019)
- [See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks](https://mlsanity.com/paper/2001.06810/) (2020)
- [A Survey on Deep Learning Technique for Video Segmentation](https://mlsanity.com/paper/2107.01153/) (2021)
- [Video Object Segmentation with Episodic Graph Memory Networks](https://mlsanity.com/paper/2007.07020/) (2020)
- [Online Adaptation of Convolutional Neural Networks for Video Object Segmentation](https://mlsanity.com/paper/1706.09364/) (2017)
- [SG-Net: Spatial Granularity Network for One-Stage Video Instance Segmentation](https://mlsanity.com/paper/2103.10284/) (2021)
- [Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks](https://mlsanity.com/paper/2001.06807/) (2020)
- [Motion Guided Attention for Video Salient Object Detection](https://mlsanity.com/paper/1909.07061/) (2019)
- [Learning Video Object Segmentation with Visual Memory](https://mlsanity.com/paper/1704.05737/) (2017)
- [Motion-Attentive Transition for Zero-Shot Video Object Segmentation](https://mlsanity.com/paper/2003.04253/) (2020)
- [Review of Visual Saliency Detection with Comprehensive Information](https://mlsanity.com/paper/1803.03391/) (2018)
- [Video Salient Object Detection via Fully Convolutional Networks](https://mlsanity.com/paper/1702.00871/) (2017)
- [Learning Video Object Segmentation from Unlabeled Videos](https://mlsanity.com/paper/2003.05020/) (2020)
- [TF-Blender: Temporal Feature Blender for Video Object Detection](https://mlsanity.com/paper/2108.05821/) (2021)
- [Weakly Supervised Video Salient Object Detection](https://mlsanity.com/paper/2104.02391/) (2021)
- [Iteratively Selecting an Easy Reference Frame Makes Unsupervised Video Object Segmentation Easier](https://mlsanity.com/paper/2112.12402/) (2021)
- [TENet: Triple Excitation Network for Video Salient Object Detection](https://mlsanity.com/paper/2007.09943/) (2020)
- [Target-Aware Object Discovery and Association for Unsupervised Video Multi-Object Segmentation](https://mlsanity.com/paper/2104.04782/) (2021)
- [A Benchmark Dataset and Saliency-guided Stacked Autoencoders for Video-based Salient Object Detection](https://mlsanity.com/paper/1611.00135/) (2016)
- [Exploring Rich and Efficient Spatial Temporal Interactions for Real Time Video Salient Object Detection](https://mlsanity.com/paper/2008.02973/) (2020)
- [Depth Quality Aware Salient Object Detection](https://mlsanity.com/paper/2008.04159/) (2020)
- [Data-Level Recombination and Lightweight Fusion Scheme for RGB-D Salient Object Detection](https://mlsanity.com/paper/2009.05102/) (2020)

## Built on by (most cited first, 4 of 4)

- [Rapid Salient Object Detection with Difference Convolutional Neural Networks](https://mlsanity.com/paper/2507.01182/) (2025)
- [Treating Motion as Option with Output Selection for Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2309.14786/) (2023)
- [A Spatial-Temporal Dual-Mode Mixed Flow Network for Panoramic Video Salient Object Detection](https://mlsanity.com/paper/2310.09016/) (2023)
- [G2HFNet: GeoGran-Aware Hierarchical Feature Fusion Network for Salient Object Detection in Optical Remote Sensing Images](https://mlsanity.com/paper/2603.12680/) (2026)

## Abstract (arXiv)

The existing state-of-the-art (SOTA) video salient object detection (VSOD) models have widely followed short-term methodology, which dynamically determines the balance between spatial and temporal saliency fusion by solely considering the current consecutive limited frames. However, the short-term methodology has one critical limitation, which conflicts with the real mechanism of our visual system -- a typical long-term methodology. As a result, failure cases keep showing up in the results of the current SOTA models, and the short-term methodology becomes the major technical bottleneck. To solve this problem, this paper proposes a novel VSOD approach, which performs VSOD in a complete long-term way. Our approach converts the sequential VSOD, a sequential task, to a data mining problem, i.e., decomposing the input video sequence to object proposals in advance and then mining salient object proposals as much as possible in an easy-to-hard way. Since all object proposals are simultaneously available, the proposed approach is a complete long-term approach, which can alleviate some difficulties rooted in conventional short-term approaches. In addition, we devised an online updating scheme that can grasp the most representative and trustworthy pattern profile of the salient objects, outputting framewise saliency maps with rich details and smoothing both spatially and temporally. The proposed approach outperforms almost all SOTA models on five widely used benchmark datasets.