Instance Segmentation
971 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
Most implemented papers
Is Heuristic Sampling Necessary in Training Deep Object Detectors?
facebookresearch/maskrcnn-benchmark
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In this paper, we challenge the necessity of such hard/soft sampling methods for training accurate deep object detectors.
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
tensorflow/models
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CVPR 2020
We propose SpineNet, a backbone with scale-permuted intermediate features and cross-scale connections that is learned on an object detection task by Neural Architecture Search.
Bottleneck Transformers for Visual Recognition
rwightman/pytorch-image-models
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CVPR 2021
Finally, we present a simple adaptation of the BoTNet design for image classification, resulting in models that achieve a strong performance of 84. 7% top-1 accuracy on the ImageNet benchmark while being up to 1. 64x faster in compute time than the popular EfficientNet models on TPU-v3 hardware.
Efficient Attention: Attention with Linear Complexities
cmsflash/efficient-attention
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Dot-product attention has wide applications in computer vision and natural language processing.
Panoptic Feature Pyramid Networks
facebookresearch/detectron2
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CVPR 2019
In this work, we perform a detailed study of this minimally extended version of Mask R-CNN with FPN, which we refer to as Panoptic FPN, and show it is a robust and accurate baseline for both tasks.
ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
By dissecting the channel attention module in SENet, we empirically show avoiding dimensionality reduction is important for learning channel attention, and appropriate cross-channel interaction can preserve performance while significantly decreasing model complexity.
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN).
XCiT: Cross-Covariance Image Transformers
rwightman/pytorch-image-models
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NeurIPS 2021
We propose a "transposed" version of self-attention that operates across feature channels rather than tokens, where the interactions are based on the cross-covariance matrix between keys and queries.
Path Aggregation Network for Instance Segmentation
ShuLiu1993/PANet
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CVPR 2018
The way that information propagates in neural networks is of great importance.
Key Points Estimation and Point Instance Segmentation Approach for Lane Detection
koyeongmin/PINet
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In the case of traffic line detection, an essential perception module, many condition should be considered, such as number of traffic lines and computing power of the target system.
