# Deep Object Co-Segmentation

arXiv: https://arxiv.org/abs/1804.06423 · HTML: https://mlsanity.com/paper/1804.06423/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2018.

## Built on by (most cited first, 30 of 35)

- [Emergent Correspondence from Image Diffusion](https://mlsanity.com/paper/2306.03881/) (2023)
- [Deep ViT Features as Dense Visual Descriptors](https://mlsanity.com/paper/2112.05814/) (2021)
- [ReCo: Retrieve and Co-segment for Zero-shot Transfer](https://mlsanity.com/paper/2206.07045/) (2022)
- [DreamScene360: Unconstrained Text-to-3D Scene Generation with Panoramic Gaussian Splatting](https://mlsanity.com/paper/2404.06903/) (2024)
- [NeRF-SOS: Any-View Self-supervised Object Segmentation on Complex Scenes](https://mlsanity.com/paper/2209.08776/) (2022)
- [Jack of All Tasks, Master of Many: Designing General-purpose Coarse-to-Fine Vision-Language Model](https://mlsanity.com/paper/2312.12423/) (2023)
- [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)
- [Learning to Detect Mobile Objects from LiDAR Scans Without Labels](https://mlsanity.com/paper/2203.15882/) (2022)
- [Towards Open-Set Object Detection and Discovery](https://mlsanity.com/paper/2204.05604/) (2022)
- [CycleSegNet: Object Co-segmentation with Cycle Refinement and Region Correspondence](https://mlsanity.com/paper/2101.01308/) (2021)
- [Blind Image Decomposition](https://mlsanity.com/paper/2108.11364/) (2021)
- [Learning Co-segmentation by Segment Swapping for Retrieval and Discovery](https://mlsanity.com/paper/2110.15904/) (2021)
- [GCoNet+: A Stronger Group Collaborative Co-Salient Object Detector](https://mlsanity.com/paper/2205.15469/) (2022)
- [Co-Salient Object Detection with Co-Representation Purification](https://mlsanity.com/paper/2303.07670/) (2023)
- [Generating Masks from Boxes by Mining Spatio-Temporal Consistencies in Videos](https://mlsanity.com/paper/2101.02196/) (2021)
- [Collaborative Camouflaged Object Detection: A Large-Scale Dataset and Benchmark](https://mlsanity.com/paper/2310.04253/) (2023)
- [SmokeBench: Evaluating Multimodal Large Language Models for Wildfire Smoke Detection](https://mlsanity.com/paper/2512.11215/) (2025)
- [Few-shot Weakly-Supervised Object Detection via Directional Statistics](https://mlsanity.com/paper/2103.14162/) (2021)
- [Towards Stable Co-saliency Detection and Object Co-segmentation](https://mlsanity.com/paper/2209.12138/) (2022)
- [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)
- [Pairwise Matching of Intermediate Representations for Fine-grained Explainability](https://mlsanity.com/paper/2503.22881/) (2025)
- [Co-segmentation Inspired Attention Module for Video-based Computer Vision Tasks](https://mlsanity.com/paper/2111.07370/) (2021)
- [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)
- [Improving Image co-segmentation via Deep Metric Learning](https://mlsanity.com/paper/2103.10670/) (2021)
- [Single Stage Class Agnostic Common Object Detection: A Simple Baseline](https://mlsanity.com/paper/2104.12245/) (2021)
- [A Dense Siamese U-Net trained with Edge Enhanced 3D IOU Loss for Image Co-segmentation](https://mlsanity.com/paper/2108.07491/) (2021)
- [ASOC: Adaptive Self-aware Object Co-localization](https://mlsanity.com/paper/2201.11547/) (2022)
- [Comprehensive Saliency Fusion for Object Co-segmentation](https://mlsanity.com/paper/2201.12828/) (2022)

## Abstract (arXiv)

This work presents a deep object co-segmentation (DOCS) approach for segmenting common objects of the same class within a pair of images. This means that the method learns to ignore common, or uncommon, background stuff and focuses on objects. If multiple object classes are presented in the image pair, they are jointly extracted as foreground. To address this task, we propose a CNN-based Siamese encoder-decoder architecture. The encoder extracts high-level semantic features of the foreground objects, a mutual correlation layer detects the common objects, and finally, the decoder generates the output foreground masks for each image. To train our model, we compile a large object co-segmentation dataset consisting of image pairs from the PASCAL VOC dataset with common objects masks. We evaluate our approach on commonly used datasets for co-segmentation tasks and observe that our approach consistently outperforms competing methods, for both seen and unseen object classes.