# S4Net: Single Stage Salient-Instance Segmentation

arXiv: https://arxiv.org/abs/1711.07618 · HTML: https://mlsanity.com/paper/1711.07618/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2017.

## Built on by (most cited first, 23 of 23)

- [Affinity Attention Graph Neural Network for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2106.04054/) (2021)
- [Advances in Deep Concealed Scene Understanding](https://mlsanity.com/paper/2304.11234/) (2023)
- [Unveiling the Potential of Structure Preserving for Weakly Supervised Object Localization](https://mlsanity.com/paper/2103.04523/) (2021)
- [Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic Segmentation](https://mlsanity.com/paper/2104.00905/) (2021)
- [Diving into Underwater: Segment Anything Model Guided Underwater Salient Instance Segmentation and A Large-scale Dataset](https://mlsanity.com/paper/2406.06039/) (2024)
- [Beyond Semantic to Instance Segmentation: Weakly-Supervised Instance Segmentation via Semantic Knowledge Transfer and Self-Refinement](https://mlsanity.com/paper/2109.09477/) (2021)
- [Weakly-supervised segmentation of referring expressions](https://mlsanity.com/paper/2205.04725/) (2022)
- [Salient Objects in Clutter](https://mlsanity.com/paper/2105.03053/) (2021)
- [Towards Open-World Segmentation of Parts](https://mlsanity.com/paper/2305.16804/) (2023)
- [Learning to Detect Instance-level Salient Objects Using Complementary Image Labels](https://mlsanity.com/paper/2111.10137/) (2021)
- [CalibNet: Dual-branch Cross-modal Calibration for RGB-D Salient Instance Segmentation](https://mlsanity.com/paper/2307.08098/) (2023)
- [Salient Object Ranking with Position-Preserved Attention](https://mlsanity.com/paper/2106.05047/) (2021)
- [Instance-Level Relative Saliency Ranking with Graph Reasoning](https://mlsanity.com/paper/2107.03824/) (2021)
- [Empowering DINO Representations for Underwater Instance Segmentation via Aligner and Prompter](https://mlsanity.com/paper/2511.08334/) (2025)
- [Bi-directional Object-context Prioritization Learning for Saliency Ranking](https://mlsanity.com/paper/2203.09416/) (2022)
- [UIS-Mamba: Exploring Mamba for Underwater Instance Segmentation via Dynamic Tree Scan and Hidden State Weaken](https://mlsanity.com/paper/2508.00421/) (2025)
- [Evaluation Study on SAM 2 for Class-agnostic Instance-level Segmentation](https://mlsanity.com/paper/2409.02567/) (2024)
- [CarcassFormer: An End-to-end Transformer-based Framework for Simultaneous Localization, Segmentation and Classification of Poultry Carcass Defect](https://mlsanity.com/paper/2404.11429/) (2024)
- [USIS-PGM: Photometric Gaussian Mixtures for Underwater Salient Instance Segmentation](https://mlsanity.com/paper/2603.13961/) (2026)
- [Semantic Segmentation Enhanced Transformer Model for Human Attention Prediction](https://mlsanity.com/paper/2301.11022/) (2023)
- [Partitioned Saliency Ranking with Dense Pyramid Transformers](https://mlsanity.com/paper/2308.00236/) (2023)
- [P2Seg: Pointly-supervised Segmentation via Mutual Distillation](https://mlsanity.com/paper/2401.09709/) (2024)
- [SPDA-SAM: A Self-prompted Depth-Aware Segment Anything Model for Instance Segmentation](https://mlsanity.com/paper/2602.06335/) (2026)

## Abstract (arXiv)

We consider an interesting problem-salient instance segmentation in this paper. Other than producing bounding boxes, our network also outputs high-quality instance-level segments. Taking into account the category-independent property of each target, we design a single stage salient instance segmentation framework, with a novel segmentation branch. Our new branch regards not only local context inside each detection window but also its surrounding context, enabling us to distinguish the instances in the same scope even with obstruction. Our network is end-to-end trainable and runs at a fast speed (40 fps when processing an image with resolution 320x320). We evaluate our approach on a publicly available benchmark and show that it outperforms other alternative solutions. We also provide a thorough analysis of the design choices to help readers better understand the functions of each part of our network. The source code can be found at urlhttps://github.com/RuochenFan/S4Net.