# CycleSegNet: Object Co-segmentation with Cycle Refinement and Region Correspondence

arXiv: https://arxiv.org/abs/2101.01308 · HTML: https://mlsanity.com/paper/2101.01308/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 71 references that are in the map.
- First posted on arXiv 5 Jan 2021.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1604.05144/) (2016)
- [CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning](https://mlsanity.com/paper/1903.02351/) (2019)
- [Learning a Discriminative Feature Network for Semantic Segmentation](https://mlsanity.com/paper/1804.09337/) (2018)
- [CRNet: Cross-Reference Networks for Few-Shot Segmentation](https://mlsanity.com/paper/2003.10658/) (2020)
- [Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks](https://mlsanity.com/paper/2001.06807/) (2020)
- [Weakly-Supervised Semantic Segmentation by Iteratively Mining Common Object Features](https://mlsanity.com/paper/1806.04659/) (2018)
- [Re-thinking Co-Salient Object Detection](https://mlsanity.com/paper/2007.03380/) (2020)
- [DeepEMD: Differentiable Earth Mover's Distance for Few-Shot Learning](https://mlsanity.com/paper/2003.06777/) (2020)
- [Deep Object Co-Segmentation](https://mlsanity.com/paper/1804.06423/) (2018)
- [Interactive segmentation of medical images through fully convolutional neural networks](https://mlsanity.com/paper/1903.08205/) (2019)
- [Coarse-to-fine Semantic Segmentation from Image-level Labels](https://mlsanity.com/paper/1812.10885/) (2018)
- [Show, Match and Segment: Joint Weakly Supervised Learning of Semantic Matching and Object Co-segmentation](https://mlsanity.com/paper/1906.05857/) (2019)
- [Stacked Deconvolutional Network for Semantic Segmentation](https://mlsanity.com/paper/1708.04943/) (2017)
- [Group-wise Deep Co-saliency Detection](https://mlsanity.com/paper/1707.07381/) (2017)
- [Semantic Aware Attention Based Deep Object Co-segmentation](https://mlsanity.com/paper/1810.06859/) (2018)
- [Deep Object Co-segmentation via Spatial-Semantic Network Modulation](https://mlsanity.com/paper/1911.12950/) (2019)
- [Compositional Prototype Network with Multi-view Comparision for Few-Shot Point Cloud Semantic Segmentation](https://mlsanity.com/paper/2012.14255/) (2020)
- [Recurrent Segmentation for Variable Computational Budgets](https://mlsanity.com/paper/1711.10151/) (2017)

## Built on by (most cited first, 19 of 19)

- [Deep ViT Features as Dense Visual Descriptors](https://mlsanity.com/paper/2112.05814/) (2021)
- [Few-Shot Incremental Learning with Continually Evolved Classifiers](https://mlsanity.com/paper/2104.03047/) (2021)
- [ReCo: Retrieve and Co-segment for Zero-shot Transfer](https://mlsanity.com/paper/2206.07045/) (2022)
- [Jack of All Tasks, Master of Many: Designing General-purpose Coarse-to-Fine Vision-Language Model](https://mlsanity.com/paper/2312.12423/) (2023)
- [Hierarchical Normalization for Robust Monocular Depth Estimation](https://mlsanity.com/paper/2210.09670/) (2022)
- [A Unified Transformer Framework for Group-based Segmentation: Co-Segmentation, Co-Saliency Detection and Video Salient Object Detection](https://mlsanity.com/paper/2203.04708/) (2022)
- [Few-shot Segmentation with Optimal Transport Matching and Message Flow](https://mlsanity.com/paper/2108.08518/) (2021)
- [Meta Navigator: Search for a Good Adaptation Policy for Few-shot Learning](https://mlsanity.com/paper/2109.05749/) (2021)
- [GCoNet+: A Stronger Group Collaborative Co-Salient Object Detector](https://mlsanity.com/paper/2205.15469/) (2022)
- [Few-shot Open-set Recognition Using Background as Unknowns](https://mlsanity.com/paper/2207.09059/) (2022)
- [Towards Stable Co-saliency Detection and Object Co-segmentation](https://mlsanity.com/paper/2209.12138/) (2022)
- [CALICO: Part-Focused Semantic Co-Segmentation with Large Vision-Language Models](https://mlsanity.com/paper/2412.19331/) (2024)
- [Surface-SOS: Self-Supervised Object Segmentation via Neural Surface Representation](https://mlsanity.com/paper/2501.09947/) (2025)
- [Unsupervised Object Localization with Representer Point Selection](https://mlsanity.com/paper/2309.04172/) (2023)
- [LCCo: Lending CLIP to Co-Segmentation](https://mlsanity.com/paper/2308.11506/) (2023)
- [Co-Segmentation without any Pixel-level Supervision with Application to Large-Scale Sketch Classification](https://mlsanity.com/paper/2410.13582/) (2024)
- [Calibrating Class Activation Maps for Long-Tailed Visual Recognition](https://mlsanity.com/paper/2108.12757/) (2021)
- [APES: Articulated Part Extraction from Sprite Sheets](https://mlsanity.com/paper/2206.02015/) (2022)
- [Discriminative Consensus Mining with A Thousand Groups for More Accurate Co-Salient Object Detection](https://mlsanity.com/paper/2403.12057/) (2024)

## Abstract (arXiv)

Image co-segmentation is an active computer vision task that aims to segment the common objects from a set of images. Recently, researchers design various learning-based algorithms to undertake the co-segmentation task. The main difficulty in this task is how to effectively transfer information between images to make conditional predictions. In this paper, we present CycleSegNet, a novel framework for the co-segmentation task. Our network design has two key components: a region correspondence module which is the basic operation for exchanging information between local image regions, and a cycle refinement module, which utilizes ConvLSTMs to progressively update image representations and exchange information in a cycle and iterative manner. Extensive experiments demonstrate that our proposed method significantly outperforms the state-of-the-art methods on four popular benchmark datasets -- PASCAL VOC dataset, MSRC dataset, Internet dataset, and iCoseg dataset, by 2.6%, 7.7%, 2.2%, and 2.9%, respectively.