Instance Segmentation
964 papers with code • 25 benchmarks • 82 datasets
Instance Segmentation is a computer vision task that involves identifying and separating individual objects within an image, including detecting the boundaries of each object and assigning a unique label to each object. The goal of instance segmentation is to produce a pixel-wise segmentation map of the image, where each pixel is assigned to a specific object instance.
Image Credit: Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers, CVPR'21
Benchmarks
These leaderboards are used to track progress in Instance Segmentation
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Libraries
Use these libraries to find Instance Segmentation models and implementationsDatasets
MS COCO
Cityscapes
nuScenes
ADE20K
NYUv2
LVIS
BDD100K
KITTI-360
YouTube-VIS 2019
Objects365
Subtasks
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Referring Expression Segmentation
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3D Instance Segmentation
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Real-time Instance Segmentation
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Unsupervised Object Segmentation
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Unsupervised Object Segmentation
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Amodal Instance Segmentation
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Box-supervised Instance Segmentation
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Image-level Supervised Instance Segmentation
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Unseen Object Instance Segmentation
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3D Semantic Instance Segmentation
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Open-World Instance Segmentation
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Human Instance Segmentation
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One-Shot Instance Segmentation
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Semi-Supervised Person Instance Segmentation
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Point-Supervised Instance Segmentation
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Solar Cell Segmentation
Latest papers
MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining
However, transferring the pretrained models to downstream tasks may encounter task discrepancy due to their formulation of pretraining as image classification or object discrimination tasks.
CLIP-VIS: Adapting CLIP for Open-Vocabulary Video Instance Segmentation
zwq456/clip-vis
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Given a set of initial queries, class-agnostic mask generation employs a transformer decoder to predict query masks and corresponding object scores and mask IoU scores.
Aerial Lifting: Neural Urban Semantic and Building Instance Lifting from Aerial Imagery
We then introduce a novel cross-view instance label grouping strategy based on the 3D scene representation to mitigate the multi-view inconsistency problem in the 2D instance labels.
Circle Representation for Medical Instance Object Segmentation
Recently, circle representation has been introduced for medical imaging, designed specifically to enhance the detection of instance objects that are spherically shaped (e. g., cells, glomeruli, and nuclei).
When Semantic Segmentation Meets Frequency Aliasing
While positively correlated with the proposed aliasing score, three types of hard pixels exhibit different patterns.
StainFuser: Controlling Diffusion for Faster Neural Style Transfer in Multi-Gigapixel Histology Images
r-j96/stainfuser
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Stain normalization algorithms aim to transform the color and intensity characteristics of a source multi-gigapixel histology image to match those of a target image, mitigating inconsistencies in the appearance of stains used to highlight cellular components in the images.
SAM-PD: How Far Can SAM Take Us in Tracking and Segmenting Anything in Videos by Prompt Denoising
infzhou/sam-pd
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Recently, promptable segmentation models, such as the Segment Anything Model (SAM), have demonstrated robust zero-shot generalization capabilities on static images.
CenterDisks: Real-time instance segmentation with disk covering
In the learning phase, we consider the radius as proportional to a standard deviation in order to compute the error to propagate on a set of two-dimensional Gaussian functions rather than disks.
End-to-End Human Instance Matting
qlyoo/e2e-him
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Finally, an instance matting network decodes the image features and united semantics guidance to predict all instance-level alpha mattes.
FusionVision: A comprehensive approach of 3D object reconstruction and segmentation from RGB-D cameras using YOLO and fast segment anything
Therefore, this paper introduces FusionVision, an exhaustive pipeline adapted for the robust 3D segmentation of objects in RGB-D imagery.
