# Video Salient Object Detection via Adaptive Local-Global Refinement

arXiv: https://arxiv.org/abs/2104.14360 · HTML: https://mlsanity.com/paper/2104.14360/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 26 references that are in the map.
- First posted on arXiv 29 Apr 2021.

## Builds on (most cited first, 13 of 13)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://mlsanity.com/paper/1612.01925/) (2016)
- [Cascaded Partial Decoder for Fast and Accurate Salient Object Detection](https://mlsanity.com/paper/1904.08739/) (2019)
- [Semantic Graph Convolutional Networks for 3D Human Pose Regression](https://mlsanity.com/paper/1904.03345/) (2019)
- [F3Net: Fusion, Feedback and Focus for Salient Object Detection](https://mlsanity.com/paper/1911.11445/) (2019)
- [Video Object Segmentation with Episodic Graph Memory Networks](https://mlsanity.com/paper/2007.07020/) (2020)
- [Motion Guided Attention for Video Salient Object Detection](https://mlsanity.com/paper/1909.07061/) (2019)
- [Video Salient Object Detection via Fully Convolutional Networks](https://mlsanity.com/paper/1702.00871/) (2017)
- [Multi-Modal Graph Neural Network for Joint Reasoning on Vision and Scene Text](https://mlsanity.com/paper/2003.13962/) (2020)
- [Cascade Graph Neural Networks for RGB-D Salient Object Detection](https://mlsanity.com/paper/2008.03087/) (2020)
- [Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency Detection](https://mlsanity.com/paper/2003.06167/) (2020)
- [Visual Saliency Detection Based on Multiscale Deep CNN Features](https://mlsanity.com/paper/1609.02077/) (2016)
- [TENet: Triple Excitation Network for Video Salient Object Detection](https://mlsanity.com/paper/2007.09943/) (2020)

## Built on by (most cited first, 3 of 3)

- [A Comprehensive Survey on Video Saliency Detection with Auditory Information: the Audio-visual Consistency Perceptual is the Key!](https://mlsanity.com/paper/2206.13390/) (2022)
- [A Novel Long-term Iterative Mining Scheme for Video Salient Object Detection](https://mlsanity.com/paper/2206.09564/) (2022)
- [A Spatial-Temporal Dual-Mode Mixed Flow Network for Panoramic Video Salient Object Detection](https://mlsanity.com/paper/2310.09016/) (2023)

## Abstract (arXiv)

Video salient object detection (VSOD) is an important task in many vision applications. Reliable VSOD requires to simultaneously exploit the information from both the spatial domain and the temporal domain. Most of the existing algorithms merely utilize simple fusion strategies, such as addition and concatenation, to merge the information from different domains. Despite their simplicity, such fusion strategies may introduce feature redundancy, and also fail to fully exploit the relationship between multi-level features extracted from both spatial and temporal domains. In this paper, we suggest an adaptive local-global refinement framework for VSOD. Different from previous approaches, we propose a local refinement architecture and a global one to refine the simply fused features with different scopes, which can fully explore the local dependence and the global dependence of multi-level features. In addition, to emphasize the effective information and suppress the useless one, an adaptive weighting mechanism is designed based on graph convolutional neural network (GCN). We show that our weighting methodology can further exploit the feature correlations, thus driving the network to learn more discriminative feature representation. Extensive experimental results on public video datasets demonstrate the superiority of our method over the existing ones.