# Selectivity or Invariance: Boundary-aware Salient Object Detection

arXiv: https://arxiv.org/abs/1812.10066 · HTML: https://mlsanity.com/paper/1812.10066/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Dec 2018.

## Built on by (most cited first, 30 of 48)

- [Camouflaged Object Segmentation with Distraction Mining](https://mlsanity.com/paper/2104.10475/) (2021)
- [Salient Object Detection via Integrity Learning](https://mlsanity.com/paper/2101.07663/) (2021)
- [Pyramid Grafting Network for One-Stage High Resolution Saliency Detection](https://mlsanity.com/paper/2204.05041/) (2022)
- [UACANet: Uncertainty Augmented Context Attention for Polyp Segmentation](https://mlsanity.com/paper/2107.02368/) (2021)
- [Advances in Deep Concealed Scene Understanding](https://mlsanity.com/paper/2304.11234/) (2023)
- [Boundary-Aware Segmentation Network for Mobile and Web Applications](https://mlsanity.com/paper/2101.04704/) (2021)
- [Active Boundary Loss for Semantic Segmentation](https://mlsanity.com/paper/2102.02696/) (2021)
- [Multi-Scale and Detail-Enhanced Segment Anything Model for Salient Object Detection](https://mlsanity.com/paper/2408.04326/) (2024)
- [MFNet: Multi-filter Directive Network for Weakly Supervised Salient Object Detection](https://mlsanity.com/paper/2112.01732/) (2021)
- [Densely Nested Top-Down Flows for Salient Object Detection](https://mlsanity.com/paper/2102.09133/) (2021)
- [Salient Objects in Clutter](https://mlsanity.com/paper/2105.03053/) (2021)
- [Towards Diverse Binary Segmentation via A Simple yet General Gated Network](https://mlsanity.com/paper/2303.10396/) (2023)
- [Frequency-Guided Spatial Adaptation for Camouflaged Object Detection](https://mlsanity.com/paper/2409.12421/) (2024)
- [Learning to Detect Instance-level Salient Objects Using Complementary Image Labels](https://mlsanity.com/paper/2111.10137/) (2021)
- [RGB-D Salient Object Detection with Ubiquitous Target Awareness](https://mlsanity.com/paper/2109.03425/) (2021)
- [Motion-aware Memory Network for Fast Video Salient Object Detection](https://mlsanity.com/paper/2208.00946/) (2022)
- [MFFN: Multi-view Feature Fusion Network for Camouflaged Object Detection](https://mlsanity.com/paper/2210.06361/) (2022)
- [DC-Net: Divide-and-Conquer for Salient Object Detection](https://mlsanity.com/paper/2305.14955/) (2023)
- [Boosting Salient Object Detection with Transformer-based Asymmetric Bilateral U-Net](https://mlsanity.com/paper/2108.07851/) (2021)
- [Large-Field Contextual Feature Learning for Glass Detection](https://mlsanity.com/paper/2209.04639/) (2022)
- [E3NeRF: Efficient Event-Enhanced Neural Radiance Fields from Blurry Images](https://mlsanity.com/paper/2408.01840/) (2024)
- [GroupTransNet: Group Transformer Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2203.10785/) (2022)
- [Towards Complex Backgrounds: A Unified Difference-Aware Decoder for Binary Segmentation](https://mlsanity.com/paper/2210.15156/) (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)
- [Distortion-aware Transformer in 360deg Salient Object Detection](https://mlsanity.com/paper/2308.03359/) (2023)
- [Pre-train, Adapt and Detect: Multi-Task Adapter Tuning for Camouflaged Object Detection](https://mlsanity.com/paper/2307.10685/) (2023)
- [M3Net: Multilevel, Mixed and Multistage Attention Network for Salient Object Detection](https://mlsanity.com/paper/2309.08365/) (2023)
- [Uncertainty Guided Refinement for Fine-Grained Salient Object Detection](https://mlsanity.com/paper/2504.09666/) (2025)
- [Saliency Detection via Global Context Enhanced Feature Fusion and Edge Weighted Loss](https://mlsanity.com/paper/2110.06550/) (2021)

## Abstract (arXiv)

Typically, a salient object detection (SOD) model faces opposite requirements in processing object interiors and boundaries. The features of interiors should be invariant to strong appearance change so as to pop-out the salient object as a whole, while the features of boundaries should be selective to slight appearance change to distinguish salient objects and background. To address this selectivity-invariance dilemma, we propose a novel boundary-aware network with successive dilation for image-based SOD. In this network, the feature selectivity at boundaries is enhanced by incorporating a boundary localization stream, while the feature invariance at interiors is guaranteed with a complex interior perception stream. Moreover, a transition compensation stream is adopted to amend the probable failures in transitional regions between interiors and boundaries. In particular, an integrated successive dilation module is proposed to enhance the feature invariance at interiors and transitional regions. Extensive experiments on six datasets show that the proposed approach outperforms 16 state-of-the-art methods.