Instance Segmentation
972 papers with code • 25 benchmarks • 83 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 with no code
Structured Model Pruning for Efficient Inference in Computational Pathology
In this work, we demonstrate that model pruning, as a model compression technique, can effectively reduce inference cost for computational and digital pathology based analysis with a negligible loss of analysis performance.
Automated National Urban Map Extraction
Developing countries usually lack the proper governance means to generate and regularly update a national rooftop map.
Panoptic Perception: A Novel Task and Fine-grained Dataset for Universal Remote Sensing Image Interpretation
Experimental results on FineGrip demonstrate the feasibility of the panoptic perception task and the beneficial effect of multi-task joint optimization on individual tasks.
OW-VISCap: Open-World Video Instance Segmentation and Captioning
To address these issues, we propose Open-World Video Instance Segmentation and Captioning (OW-VISCap), an approach to jointly segment, track, and caption previously seen or unseen objects in a video.
CORP: A Multi-Modal Dataset for Campus-Oriented Roadside Perception Tasks
Numerous roadside perception datasets have been introduced to propel advancements in autonomous driving and intelligent transportation systems research and development.
Language-Guided Instance-Aware Domain-Adaptive Panoptic Segmentation
A key challenge in panoptic UDA is reducing the domain gap between a labeled source and an unlabeled target domain while harmonizing the subtasks of semantic and instance segmentation to limit catastrophic interference.
Segment Any 3D Object with Language
In addition, to align the 3D segmentation model with various language instructions and enhance the mask quality, we introduce three types of multimodal associations as supervision.
Teeth-SEG: An Efficient Instance Segmentation Framework for Orthodontic Treatment based on Anthropic Prior Knowledge
Teeth localization, segmentation, and labeling in 2D images have great potential in modern dentistry to enhance dental diagnostics, treatment planning, and population-based studies on oral health.
SUGAR: Pre-training 3D Visual Representations for Robotics
SUGAR employs a versatile transformer-based model to jointly address five pre-training tasks, namely cross-modal knowledge distillation for semantic learning, masked point modeling to understand geometry structures, grasping pose synthesis for object affordance, 3D instance segmentation and referring expression grounding to analyze cluttered scenes.
What is Point Supervision Worth in Video Instance Segmentation?
Video instance segmentation (VIS) is a challenging vision task that aims to detect, segment, and track objects in videos.
