# Multi-view Aggregation Network for Dichotomous Image Segmentation

arXiv: https://arxiv.org/abs/2404.07445 · HTML: https://mlsanity.com/paper/2404.07445/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 48 references that are in the map.
- First posted on arXiv 11 Apr 2024.

## Builds on (most cited first, 30 of 33)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021)
- [Multi-view Convolutional Neural Networks for 3D Shape Recognition](https://mlsanity.com/paper/1505.00880/) (2015)
- [Structure-measure: A New Way to Evaluate Foreground Maps](https://mlsanity.com/paper/1708.00786/) (2017)
- [Res2Net: A New Multi-scale Backbone Architecture](https://mlsanity.com/paper/1904.01169/) (2019)
- [Neuralangelo: High-Fidelity Neural Surface Reconstruction](https://mlsanity.com/paper/2306.03092/) (2023)
- [Enhanced-alignment Measure for Binary Foreground Map Evaluation](https://mlsanity.com/paper/1805.10421/) (2018)
- [Anabranch Network for Camouflaged Object Segmentation](https://mlsanity.com/paper/2105.09451/) (2021)
- [Multi-scale Interactive Network for Salient Object Detection](https://mlsanity.com/paper/2007.09062/) (2020)
- [Asymmetric Non-local Neural Networks for Semantic Segmentation](https://mlsanity.com/paper/1908.07678/) (2019)
- [Real-Time High-Resolution Background Matting](https://mlsanity.com/paper/2012.07810/) (2020)
- [Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images](https://mlsanity.com/paper/1901.11153/) (2019)
- [Camouflaged Object Segmentation with Distraction Mining](https://mlsanity.com/paper/2104.10475/) (2021)
- [Suppress and Balance: A Simple Gated Network for Salient Object Detection](https://mlsanity.com/paper/2007.08074/) (2020)
- [Epipolar Transformers](https://mlsanity.com/paper/2005.04551/) (2020)
- [Highly Accurate Dichotomous Image Segmentation](https://mlsanity.com/paper/2203.03041/) (2022)
- [Global Context-Aware Progressive Aggregation Network for Salient Object Detection](https://mlsanity.com/paper/2003.00651/) (2020)
- [P2T: Pyramid Pooling Transformer for Scene Understanding](https://mlsanity.com/paper/2106.12011/) (2021)
- [Pyramid Grafting Network for One-Stage High Resolution Saliency Detection](https://mlsanity.com/paper/2204.05041/) (2022)
- [Multi-view 3D Reconstruction with Transformer](https://mlsanity.com/paper/2103.12957/) (2021)
- [Boundary-Aware Segmentation Network for Mobile and Web Applications](https://mlsanity.com/paper/2101.04704/) (2021)
- [Revisiting Image Pyramid Structure for High Resolution Salient Object Detection](https://mlsanity.com/paper/2209.09475/) (2022)
- [STRPM: A Spatiotemporal Residual Predictive Model for High-Resolution Video Prediction](https://mlsanity.com/paper/2203.16084/) (2022)
- [Learning Implicit Feature Alignment Function for Semantic Segmentation](https://mlsanity.com/paper/2206.08655/) (2022)
- [Self-Supervised Pretraining for RGB-D Salient Object Detection](https://mlsanity.com/paper/2101.12482/) (2021)
- [Unite-Divide-Unite: Joint Boosting Trunk and Structure for High-accuracy Dichotomous Image Segmentation](https://mlsanity.com/paper/2307.14052/) (2023)
- [Fully Understanding Generic Objects: Modeling, Segmentation, and Reconstruction](https://mlsanity.com/paper/2104.00858/) (2021)

## Built on by (most cited first, 28 of 28)

- [DICEPTION: A Generalist Diffusion Model for Visual Perceptual Tasks](https://mlsanity.com/paper/2502.17157/) (2025)
- [Mask Factory: Towards High-quality Synthetic Data Generation for Dichotomous Image Segmentation](https://mlsanity.com/paper/2412.19080/) (2024)
- [Diorama: Unleashing Zero-shot Single-view 3D Indoor Scene Modeling](https://mlsanity.com/paper/2411.19492/) (2024)
- [High-Precision Dichotomous Image Segmentation via Probing Diffusion Capacity](https://mlsanity.com/paper/2410.10105/) (2024)
- [CGCOD: Class-Guided Camouflaged Object Detection](https://mlsanity.com/paper/2412.18977/) (2024)
- [BEN: Using Confidence-Guided Matting for Dichotomous Image Segmentation](https://mlsanity.com/paper/2501.06230/) (2025)
- [S3OD: Towards Generalizable Salient Object Detection with Synthetic Data](https://mlsanity.com/paper/2510.21605/) (2025)
- [Evaluation Study on SAM 2 for Class-agnostic Instance-level Segmentation](https://mlsanity.com/paper/2409.02567/) (2024)
- [Multi-Flow: Multi-View-Enriched Normalizing Flows for Industrial Anomaly Detection](https://mlsanity.com/paper/2504.03306/) (2025)
- [UniMRSeg: Unified Modality-Relax Segmentation via Hierarchical Self-Supervised Compensation](https://mlsanity.com/paper/2509.16170/) (2025)
- [Towards Fine-grained Interactive Segmentation in Images and Videos](https://mlsanity.com/paper/2502.09660/) (2025)
- [High-Precision Dichotomous Image Segmentation via Depth Integrity-Prior and Fine-Grained Patch Strategy](https://mlsanity.com/paper/2503.06100/) (2025)
- [HRVVS: A High-resolution Video Vasculature Segmentation Network via Hierarchical Autoregressive Residual Priors](https://mlsanity.com/paper/2507.22530/) (2025)
- [LawDIS: Language-Window-based Controllable Dichotomous Image Segmentation](https://mlsanity.com/paper/2508.01152/) (2025)
- [Minimal High-Resolution Patches Are Sufficient for Whole Slide Image Representation via Cascaded Dual-Scale Reconstruction](https://mlsanity.com/paper/2508.01641/) (2025)
- [Power Battery Detection](https://mlsanity.com/paper/2508.07797/) (2025)
- [Fix False Transparency by Noise Guided Splatting](https://mlsanity.com/paper/2510.15736/) (2025)
- [Visual Bridge: Universal Visual Perception Representations Generating](https://mlsanity.com/paper/2511.07877/) (2025)
- [RealCamo: Boosting Real Camouflage Synthesis with Layout Controls and Textual-Visual Guidance](https://mlsanity.com/paper/2512.22974/) (2025)
- [Beyond Pixel Overlap: A Framework for Decomposing Segmentation Evaluation Metrics](https://mlsanity.com/paper/2607.00886/) (2026)
- [Adapting Segment Anything Model for Power Transmission Corridor Hazard Segmentation](https://mlsanity.com/paper/2505.22105/) (2025)
- [Neovascularization Segmentation via a Multilateral Interaction-Enhanced Graph Convolutional Network](https://mlsanity.com/paper/2508.03197/) (2025)
- [ToonOut: Fine-tuned Background-Removal for Anime Characters](https://mlsanity.com/paper/2509.06839/) (2025)
- [SPG-CDENet: Spatial Prior-Guided Cross Dual Encoder Network for Multi-Organ Segmentation](https://mlsanity.com/paper/2510.26390/) (2025)
- [Toward Real-World High-Precision Image Matting and Segmentation](https://mlsanity.com/paper/2601.12080/) (2026)
- [Segment and Matte Anything in a Unified Model](https://mlsanity.com/paper/2601.12147/) (2026)
- [FlowDIS: Language-Guided Dichotomous Image Segmentation with Flow Matching](https://mlsanity.com/paper/2605.05077/) (2026)
- [Cross-Species Animal Re-Identification with Semantic Consistency Learning](https://mlsanity.com/paper/2609.09705/) (2026)

## Abstract (arXiv)

Dichotomous Image Segmentation (DIS) has recently emerged towards high-precision object segmentation from high-resolution natural images. When designing an effective DIS model, the main challenge is how to balance the semantic dispersion of high-resolution targets in the small receptive field and the loss of high-precision details in the large receptive field. Existing methods rely on tedious multiple encoder-decoder streams and stages to gradually complete the global localization and local refinement. Human visual system captures regions of interest by observing them from multiple views. Inspired by it, we model DIS as a multi-view object perception problem and provide a parsimonious multi-view aggregation network (MVANet), which unifies the feature fusion of the distant view and close-up view into a single stream with one encoder-decoder structure. With the help of the proposed multi-view complementary localization and refinement modules, our approach established long-range, profound visual interactions across multiple views, allowing the features of the detailed close-up view to focus on highly slender structures.Experiments on the popular DIS-5K dataset show that our MVANet significantly outperforms state-of-the-art methods in both accuracy and speed. The source code and datasets will be publicly available at hrefhttps://github.com/qianyu-dlut/MVANetMVANet.