SOTAVerified

Instance Segmentation

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

Papers

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Showing 1151–1200 of 2262 papers

TitleStatusHype
Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes—0
Pruning-as-Search: Efficient Neural Architecture Search via Channel Pruning and Structural ReparameterizationCode1
2D Amodal Instance Segmentation Guided by 3D Shape Prior—0
Efficient Self-supervised Vision Pretraining with Local Masked ReconstructionCode1
iFS-RCNN: An Incremental Few-shot Instance SegmenterCode0
Two-Dimensional Quantum Material Identification via Self-Attention and Soft-labeling in Deep Learning—0
Self-Supervised Visual Representation Learning with Semantic GroupingCode1
EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense PredictionCode4
Contrastive Learning Rivals Masked Image Modeling in Fine-tuning via Feature DistillationCode2
Architecture-Agnostic Masked Image Modeling -- From ViT back to CNNCode4
UniInst: Unique Representation for End-to-End Instance SegmentationCode1
UNet#: A UNet-like Redesigning Skip Connections for Medical Image Segmentation—0
Accelerating the creation of instance segmentation training sets through bounding box annotation—0
Human Instance Matting via Mutual Guidance and Multi-Instance RefinementCode1
HCFormer: Unified Image Segmentation with Hierarchical ClusteringCode1
Masked Image Modeling with Denoising ContrastCode1
Plane Geometry Diagram ParsingCode1
Vision Transformer Adapter for Dense PredictionsCode3
Panoptic-PHNet: Towards Real-Time and High-Precision LiDAR Panoptic Segmentation via Clustering Pseudo Heatmap—0
Evaluation of Video Coding for Machines without Ground Truth—0
Masked Generative DistillationCode2
RangeSeg: Range-Aware Real Time Segmentation of 3D LiDAR Point Clouds—0
Birds' Eye View: Measuring Behavior and Posture of Chickens as a Metric for Their Well-Being—0
GRIT: General Robust Image Task BenchmarkCode1
PolyLoss: A Polynomial Expansion Perspective of Classification Loss FunctionsCode1
Instance-Specific Feature Propagation for Referring Segmentation—0
DArch: Dental Arch Prior-assisted 3D Tooth Instance Segmentation—0
PointInst3D: Segmenting 3D Instances by Points—0
Tag-Based Attention Guided Bottom-Up Approach for Video Instance Segmentation—0
Transfer Learning from Synthetic In-vitro Soybean Pods Dataset for In-situ Segmentation of On-branch Soybean Pod—0
Monocular Depth Estimation Using Cues Inspired by Biological Vision SystemsCode0
Unseen Object Instance Segmentation with Fully Test-time RGB-D Embeddings Adaptation—0
Shape-Aware Monocular 3D Object Detection—0
Modeling Missing Annotations for Incremental Learning in Object DetectionCode1
Less than Few: Self-Shot Video Instance Segmentation—0
Temporally Efficient Vision Transformer for Video Instance SegmentationCode2
VSA: Learning Varied-Size Window Attention in Vision TransformersCode2
An Extendable, Efficient and Effective Transformer-based Object Detector—0
Transfer Learning for Instance Segmentation of Waste Bottles using Mask R-CNN Algorithm—0
Invisible-to-Visible: Privacy-Aware Human Instance Segmentation using Airborne Ultrasound via Collaborative Learning Variational Autoencoder—0
Interactive Object Segmentation in 3D Point CloudsCode1
Deep learning based automatic detection of offshore oil slicks using SAR data and contextual information—0
Open-World Instance Segmentation: Exploiting Pseudo Ground Truth From Learned Pairwise AffinityCode1
Video K-Net: A Simple, Strong, and Unified Baseline for Video SegmentationCode1
DaViT: Dual Attention Vision TransformersCode2
Instance Segmentation of Unlabeled Modalities via Cyclic Segmentation GAN—0
Unleashing Vanilla Vision Transformer with Masked Image Modeling for Object DetectionCode2
End-to-End Instance Edge Detection—0
Ship Segmentation and Georeferencing from Static Oblique View Images—0
GrowliFlower: An image time series dataset for GROWth analysis of cauLIFLOWER—0
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All datasetsCOCO test-devCOCO minivalLVIS v1.0 valCityscapes valADE20K valARMBenchOccluded COCOOoDISSeparated COCOTBBRBDD100K valCOCO val (panoptic labels)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1InternImage-HAP5080.8—Unverified
2ResNeSt-200 (multi-scale)AP5070.2—Unverified
3CenterMask + VoVNetV2-99 (multi-scale)AP5066.2—Unverified
4CenterMask + VoVNetV2-57 (single-scale)AP5060.8—Unverified
5Co-DETRmask AP57.1—Unverified
6CBNetV2 (EVA02, single-scale)mask AP56.1—Unverified
7ISDA (ResNet-50)APL55.7—Unverified
8EVAmask AP55.5—Unverified
9FD-SwinV2-Gmask AP55.4—Unverified
10Mask Frozen-DETRmask AP55.3—Unverified
#ModelMetricClaimedVerifiedStatus
1InternImage-BGFLOPs501—Unverified
2Co-DETRmask AP56.6—Unverified
3ViT-CoMer-L (Mask RCNN, DINOv2)mask AP55.9—Unverified
4InternImage-Hmask AP55.4—Unverified
5EVAmask AP55—Unverified
6Mask Frozen-DETRmask AP54.9—Unverified
7MasK DINO (SwinL, multi-scale)mask AP54.5—Unverified
8ViT-Adapter-L (HTC++, BEiTv2, O365, multi-scale)mask AP54.2—Unverified
9GLEE-Promask AP54.2—Unverified
10SwinV2-G (HTC++)mask AP53.7—Unverified