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
Surgical-DeSAM: Decoupling SAM for Instrument Segmentation in Robotic Surgery
We utilise a commonly used detection architecture, DETR, and fine-tuned it to obtain bounding box prompt for the instruments.
The devil is in the object boundary: towards annotation-free instance segmentation using Foundation Models
Foundation models, pre-trained on a large amount of data have demonstrated impressive zero-shot capabilities in various downstream tasks.
Mushroom Segmentation and 3D Pose Estimation from Point Clouds using Fully Convolutional Geometric Features and Implicit Pose Encoding
We have validated the effectiveness of the proposed implicit-based approach for a synthetic test set, as well as provided qualitative results for a small set of real acquired point clouds with depth sensors.
NOISe: Nuclei-Aware Osteoclast Instance Segmentation for Mouse-to-Human Domain Transfer
In the last few years, a handful of machine learning approaches for osteoclast image analysis have been developed, but none have addressed the full instance segmentation task required to produce the same output as that of the human expert led process.
SEVD: Synthetic Event-based Vision Dataset for Ego and Fixed Traffic Perception
In response to this gap, we present SEVD, a first-of-its-kind multi-view ego, and fixed perception synthetic event-based dataset using multiple dynamic vision sensors within the CARLA simulator.
AdaContour: Adaptive Contour Descriptor with Hierarchical Representation
Existing angle-based contour descriptors suffer from lossy representation for non-starconvex shapes.
ViM-UNet: Vision Mamba for Biomedical Segmentation
Here, we introduce ViM-UNet, a novel segmentation architecture based on it and compare it to UNet and UNETR for two challenging microscopy instance segmentation tasks.
ECLIPSE: Efficient Continual Learning in Panoptic Segmentation with Visual Prompt Tuning
Panoptic segmentation, combining semantic and instance segmentation, stands as a cutting-edge computer vision task.
DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs
naver-ai/rdnet
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This paper revives Densely Connected Convolutional Networks (DenseNets) and reveals the underrated effectiveness over predominant ResNet-style architectures.
PlainMamba: Improving Non-Hierarchical Mamba in Visual Recognition
In this paper, we further adapt the selective scanning process of Mamba to the visual domain, enhancing its ability to learn features from two-dimensional images by (i) a continuous 2D scanning process that improves spatial continuity by ensuring adjacency of tokens in the scanning sequence, and (ii) direction-aware updating which enables the model to discern the spatial relations of tokens by encoding directional information.
