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 1626–1650 of 2262 papers

TitleStatusHype
Hyb-KAN ViT: Hybrid Kolmogorov-Arnold Networks Augmented Vision Transformer—0
Hybrid Instance-aware Temporal Fusion for Online Video Instance Segmentation—0
ICM-3D: Instantiated Category Modeling for 3D Instance Segmentation—0
iConFormer: Dynamic Parameter-Efficient Tuning with Input-Conditioned Adaptation—0
Identifying Unknown Instances for Autonomous Driving—0
Image BERT Pre-training with Online Tokenizer—0
Image coding for machines: an end-to-end learned approach—0
Immature Green Apple Detection and Sizing in Commercial Orchards using YOLOv8 and Shape Fitting Techniques—0
Impact of imperfect annotations on CNN training and performance for instance segmentation and classification in digital pathology—0
IMP: Instance Mask Projection for High Accuracy Semantic Segmentation of Things—0
Improved Block Merging for 3D Point Cloud Instance Segmentation—0
Improved-Mask R-CNN: Towards an Accurate Generic MSK MRI instance segmentation platform (Data from the Osteoarthritis Initiative)—0
Improving Aerial Instance Segmentation in the Dark with Self-Supervised Low Light Enhancement—0
Improving Generalization Capability of Deep Learning-Based Nuclei Instance Segmentation by Non-deterministic Train Time and Deterministic Test Time Stain Normalization—0
Improving Pallet Detection Using Synthetic Data—0
Improving Pixel Embedding Learning through Intermediate Distance Regression Supervision for Instance Segmentation—0
Improving the Explain-Any-Concept by Introducing Nonlinearity to the Trainable Surrogate Model—0
Incremental Learning on Food Instance Segmentation—0
Independent Component Alignment for Multi-Task Learning—0
Indoor Scene Parsing With Instance Segmentation, Semantic Labeling and Support Relationship Inference—0
Industrial Synthetic Segment Pre-training—0
INoD: Injected Noise Discriminator for Self-Supervised Representation Learning in Agricultural Fields—0
InsCon:Instance Consistency Feature Representation via Self-Supervised Learning—0
INS-Conv: Incremental Sparse Convolution for Online 3D Segmentation—0
InSeGAN: A Generative Approach to Segmenting Identical Instances in Depth Images—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