# Object cosegmentation using deep Siamese network

arXiv: https://arxiv.org/abs/1803.02555 · HTML: https://mlsanity.com/paper/1803.02555/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2018.

## Built on by (most cited first, 3 of 3)

- [Co-Salient Object Detection with Co-Representation Purification](https://mlsanity.com/paper/2303.07670/) (2023)
- [Contrastively Learning Visual Attention as Affordance Cues from Demonstrations for Robotic Grasping](https://mlsanity.com/paper/2104.00878/) (2021)
- [CRCNet: Few-shot Segmentation with Cross-Reference and Region-Global Conditional Networks](https://mlsanity.com/paper/2208.10761/) (2022)

## Abstract (arXiv)

Object cosegmentation addresses the problem of discovering similar objects from multiple images and segmenting them as foreground simultaneously. In this paper, we propose a novel end-to-end pipeline to segment the similar objects simultaneously from relevant set of images using supervised learning via deep-learning framework. We experiment with multiple set of object proposal generation techniques and perform extensive numerical evaluations by training the Siamese network with generated object proposals. Similar objects proposals for the test images are retrieved using the ANNOY (Approximate Nearest Neighbor) library and deep semantic segmentation is performed on them. Finally, we form a collage from the segmented similar objects based on the relative importance of the objects.