# Instance-sensitive Fully Convolutional Networks

arXiv: https://arxiv.org/abs/1603.08678 · HTML: https://mlsanity.com/paper/1603.08678/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2016.

## Built on by (most cited first, 30 of 36)

- [Hierarchical Aggregation for 3D Instance Segmentation](https://mlsanity.com/paper/2108.02350/) (2021)
- [SG-Net: Spatial Granularity Network for One-Stage Video Instance Segmentation](https://mlsanity.com/paper/2103.10284/) (2021)
- [MOST: A Multi-Oriented Scene Text Detector with Localization Refinement](https://mlsanity.com/paper/2104.01070/) (2021)
- [Instance-wise Occlusion and Depth Orders in Natural Scenes](https://mlsanity.com/paper/2111.14562/) (2021)
- [Semantic Image Segmentation: Two Decades of Research](https://mlsanity.com/paper/2302.06378/) (2023)
- [BoxeR: Box-Attention for 2D and 3D Transformers](https://mlsanity.com/paper/2111.13087/) (2021)
- [Target-Aware Object Discovery and Association for Unsupervised Video Multi-Object Segmentation](https://mlsanity.com/paper/2104.04782/) (2021)
- [Instance and Panoptic Segmentation Using Conditional Convolutions](https://mlsanity.com/paper/2102.03026/) (2021)
- [Learning Equivariant Segmentation with Instance-Unique Querying](https://mlsanity.com/paper/2210.00911/) (2022)
- [Fashionformer: A simple, Effective and Unified Baseline for Human Fashion Segmentation and Recognition](https://mlsanity.com/paper/2204.04654/) (2022)
- [Panoramic Panoptic Segmentation: Insights Into Surrounding Parsing for Mobile Agents via Unsupervised Contrastive Learning](https://mlsanity.com/paper/2206.10711/) (2022)
- [Improving Video Instance Segmentation via Temporal Pyramid Routing](https://mlsanity.com/paper/2107.13155/) (2021)
- [Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review](https://mlsanity.com/paper/2203.10939/) (2022)
- [Solve the Puzzle of Instance Segmentation in Videos: A Weakly Supervised Framework with Spatio-Temporal Collaboration](https://mlsanity.com/paper/2212.07592/) (2022)
- [Boosting Low-Data Instance Segmentation by Unsupervised Pre-training with Saliency Prompt](https://mlsanity.com/paper/2302.01171/) (2023)
- [INSTA-YOLO: Real-Time Instance Segmentation](https://mlsanity.com/paper/2102.06777/) (2021)
- [A Survey of Computer Vision Technologies In Urban and Controlled-environment Agriculture](https://mlsanity.com/paper/2210.11318/) (2022)
- [Test-time Adaptation vs. Training-time Generalization: A Case Study in Human Instance Segmentation using Keypoints Estimation](https://mlsanity.com/paper/2212.06242/) (2022)
- [[CLS] Token is All You Need for Zero-Shot Semantic Segmentation](https://mlsanity.com/paper/2304.06212/) (2023)
- [BoundarySqueeze: Image Segmentation as Boundary Squeezing](https://mlsanity.com/paper/2105.11668/) (2021)
- [Unsupervised Pre-training with Language-Vision Prompts for Low-Data Instance Segmentation](https://mlsanity.com/paper/2405.13388/) (2024)
- [Learn Fast, Segment Well: Fast Object Segmentation Learning on the iCub Robot](https://mlsanity.com/paper/2206.13462/) (2022)
- [2D Human Pose Estimation with Explicit Anatomical Keypoints Structure Constraints](https://mlsanity.com/paper/2212.02163/) (2022)
- [Understanding Pixel-level 2D Image Semantics with 3D Keypoint Knowledge Engine](https://mlsanity.com/paper/2111.10817/) (2021)
- [Semantic Segmentation-Assisted Instance Feature Fusion for Multi-Level 3D Part Instance Segmentation](https://mlsanity.com/paper/2208.04766/) (2022)
- [Towards Robust Video Instance Segmentation with Temporal-Aware Transformer](https://mlsanity.com/paper/2301.09416/) (2023)
- [Single-stage Multi-human Parsing via Point Sets and Center-based Offsets](https://mlsanity.com/paper/2304.11356/) (2023)
- [A foundation model enpowered by a multi-modal prompt engine for universal seismic geobody interpretation across surveys](https://mlsanity.com/paper/2409.04962/) (2024)
- [Contour Loss for Instance Segmentation via k-step Distance Transformation Image](https://mlsanity.com/paper/2102.10854/) (2021)
- [SODAR: Segmenting Objects by DynamicallyAggregating Neighboring Mask Representations](https://mlsanity.com/paper/2202.07402/) (2022)

## Abstract (arXiv)

Fully convolutional networks (FCNs) have been proven very successful for semantic segmentation, but the FCN outputs are unaware of object instances. In this paper, we develop FCNs that are capable of proposing instance-level segment candidates. In contrast to the previous FCN that generates one score map, our FCN is designed to compute a small set of instance-sensitive score maps, each of which is the outcome of a pixel-wise classifier of a relative position to instances. On top of these instance-sensitive score maps, a simple assembling module is able to output instance candidate at each position. In contrast to the recent DeepMask method for segmenting instances, our method does not have any high-dimensional layer related to the mask resolution, but instead exploits image local coherence for estimating instances. We present competitive results of instance segment proposal on both PASCAL VOC and MS COCO.