# Semantic Instance Segmentation via Deep Metric Learning

arXiv: https://arxiv.org/abs/1703.10277 · HTML: https://mlsanity.com/paper/1703.10277/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2017.

## Built on by (most cited first, 30 of 37)

- [Exploring Cross-Image Pixel Contrast for Semantic Segmentation](https://mlsanity.com/paper/2101.11939/) (2021)
- [A Survey on Deep Learning Technique for Video Segmentation](https://mlsanity.com/paper/2107.01153/) (2021)
- [Hierarchical Aggregation for 3D Instance Segmentation](https://mlsanity.com/paper/2108.02350/) (2021)
- [GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinement](https://mlsanity.com/paper/2104.09958/) (2021)
- [SG-Net: Spatial Granularity Network for One-Stage Video Instance Segmentation](https://mlsanity.com/paper/2103.10284/) (2021)
- [Contrastive Lift: 3D Object Instance Segmentation by Slow-Fast Contrastive Fusion](https://mlsanity.com/paper/2306.04633/) (2023)
- [Unsupervised Discovery of the Long-Tail in Instance Segmentation Using Hierarchical Self-Supervision](https://mlsanity.com/paper/2104.01257/) (2021)
- [A Weakly Supervised Amodal Segmenter with Boundary Uncertainty Estimation](https://mlsanity.com/paper/2108.09897/) (2021)
- [Instance and Panoptic Segmentation Using Conditional Convolutions](https://mlsanity.com/paper/2102.03026/) (2021)
- [FAPIS: A Few-shot Anchor-free Part-based Instance Segmenter](https://mlsanity.com/paper/2104.00073/) (2021)
- [A^2-FPN: Attention Aggregation based Feature Pyramid Network for Instance Segmentation](https://mlsanity.com/paper/2105.03186/) (2021)
- [Towards Neural Diarization for Unlimited Numbers of Speakers Using Global and Local Attractors](https://mlsanity.com/paper/2107.01545/) (2021)
- [3D Instance Segmentation of MVS Buildings](https://mlsanity.com/paper/2112.09902/) (2021)
- [DeepSportLab: a Unified Framework for Ball Detection, Player Instance Segmentation and Pose Estimation in Team Sports Scenes](https://mlsanity.com/paper/2112.00627/) (2021)
- [Sparse Object-level Supervision for Instance Segmentation with Pixel Embeddings](https://mlsanity.com/paper/2103.14572/) (2021)
- [Dimensions of Motion: Monocular Prediction through Flow Subspaces](https://mlsanity.com/paper/2112.01502/) (2021)
- [Indoor Panorama Planar 3D Reconstruction via Divide and Conquer](https://mlsanity.com/paper/2106.14166/) (2021)
- [VideoClick: Video Object Segmentation with a Single Click](https://mlsanity.com/paper/2101.06545/) (2021)
- [Online Neural Diarization of Unlimited Numbers of Speakers Using Global and Local Attractors](https://mlsanity.com/paper/2206.02432/) (2022)
- [Human-centric Relation Segmentation: Dataset and Solution](https://mlsanity.com/paper/2105.11168/) (2021)
- [Semantic Attention and Scale Complementary Network for Instance Segmentation in Remote Sensing Images](https://mlsanity.com/paper/2107.11758/) (2021)
- [ClusteringSDF: Self-Organized Neural Implicit Surfaces for 3D Decomposition](https://mlsanity.com/paper/2403.14619/) (2024)
- [UniParser: Multi-Human Parsing with Unified Correlation Representation Learning](https://mlsanity.com/paper/2310.08984/) (2023)
- [Attend to Who You Are: Supervising Self-Attention for Keypoint Detection and Instance-Aware Association](https://mlsanity.com/paper/2111.12892/) (2021)
- [Fast Pixel-Matching for Video Object Segmentation](https://mlsanity.com/paper/2107.04279/) (2021)
- [Instance Segmentation with Cross-Modal Consistency](https://mlsanity.com/paper/2210.08113/) (2022)
- [CarcassFormer: An End-to-end Transformer-based Framework for Simultaneous Localization, Segmentation and Classification of Poultry Carcass Defect](https://mlsanity.com/paper/2404.11429/) (2024)
- [Learning Multimodal Affinities for Textual Editing in Images](https://mlsanity.com/paper/2103.10139/) (2021)
- [Improving Image co-segmentation via Deep Metric Learning](https://mlsanity.com/paper/2103.10670/) (2021)
- [Semantic Segmentation and Object Detection Towards Instance Segmentation: Breast Tumor Identification](https://mlsanity.com/paper/2108.03287/) (2021)

## Abstract (arXiv)

We propose a new method for semantic instance segmentation, by first computing how likely two pixels are to belong to the same object, and then by grouping similar pixels together. Our similarity metric is based on a deep, fully convolutional embedding model. Our grouping method is based on selecting all points that are sufficiently similar to a set of "seed points", chosen from a deep, fully convolutional scoring model. We show competitive results on the Pascal VOC instance segmentation benchmark.