Instance Segmentation
975 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
Criteria for Uncertainty-based Corner Cases Detection in Instance Segmentation
We also present our first results of an iterative training cycle that outperforms the baseline and where the data added to the training dataset is selected based on the corner case decision function.
Benchmarking the Cell Image Segmentation Models Robustness under the Microscope Optical Aberrations
Overall, this research aims to guide researchers in effectively utilizing cell segmentation models in the presence of minor optical aberrations.
Let It Flow: Simultaneous Optimization of 3D Flow and Object Clustering
We identified the structural constraints and the use of large and strict rigid clusters as the main pitfall of the current approaches and we propose a novel clustering approach that allows for combination of overlapping soft clusters as well as non-overlapping rigid clusters representation.
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.
