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 901–950 of 2262 papers

TitleStatusHype
Human Extraction and Scene Transition utilizing Mask R-CNN—0
Human De-occlusion: Invisible Perception and Recovery for Humans—0
Human Body Shape Classification Based on a Single Image—0
Contextual Graph Reasoning Networks—0
Context-Preserving Instance-Level Augmentation and Deformable Convolution Networks for SAR Ship Detection—0
hSDB-instrument: Instrument Localization Database for Laparoscopic and Robotic Surgeries—0
HR-RCNN: Hierarchical Relational Reasoning for Object Detection—0
How Object Information Improves Skeleton-based Human Action Recognition in Assembly Tasks—0
AttentionShift: Iteratively Estimated Part-Based Attention Map for Pointly Supervised Instance Segmentation—0
3D Feature Distillation with Object-Centric Priors—0
Hyb-KAN ViT: Hybrid Kolmogorov-Arnold Networks Augmented Vision Transformer—0
Hybrid Instance-aware Temporal Fusion for Online Video Instance Segmentation—0
How do Cross-View and Cross-Modal Alignment Affect Representations in Contrastive Learning?—0
How can we learn (more) from challenges? A statistical approach to driving future algorithm development—0
Content-Aware Multi-Level Guidance for Interactive Instance Segmentation—0
1% VS 100%: Parameter-Efficient Low Rank Adapter for Dense Predictions—0
Learning Cascaded Detection Tasks with Weakly-Supervised Domain Adaptation—0
iConFormer: Dynamic Parameter-Efficient Tuning with Input-Conditioned Adaptation—0
Learning Nuclei Representations with Masked Image Modelling—0
Learning Stixel-based Instance Segmentation—0
HoughCL: Finding Better Positive Pairs in Dense Self-supervised Learning—0
Holistic Understanding of 3D Scenes as Universal Scene Description—0
ContourRender: Detecting Arbitrary Contour Shape For Instance Segmentation In One Pass—0
Leader360V: The Large-scale, Real-world 360 Video Dataset for Multi-task Learning in Diverse Environment—0
HiT: Building Mapping with Hierarchical Transformers—0
Image BERT Pre-training with Online Tokenizer—0
Consistent Video Instance Segmentation with Inter-Frame Recurrent Attention—0
HiRes-FusedMIM: A High-Resolution RGB-DSM Pre-trained Model for Building-Level Remote Sensing Applications—0
Instance Scale Normalization for image understanding—0
Impact of imperfect annotations on CNN training and performance for instance segmentation and classification in digital pathology—0
3D Concept Grounding on Neural Fields—0
High-throughput Phenotyping of Nematode Cysts—0
Hierarchical Side-Tuning for Vision Transformers—0
Improved-Mask R-CNN: Towards an Accurate Generic MSK MRI instance segmentation platform (Data from the Osteoarthritis Initiative)—0
UniDAformer: Unified Domain Adaptive Panoptic Segmentation Transformer via Hierarchical Mask Calibration—0
ConfLUNet: Multiple sclerosis lesion instance segmentation in presence of confluent lesions—0
Hierarchical Lovasz Embeddings for Proposal-Free Panoptic Segmentation—0
Hierarchical Lovász Embeddings for Proposal-free Panoptic Segmentation—0
Improving Generalization Capability of Deep Learning-Based Nuclei Instance Segmentation by Non-deterministic Train Time and Deterministic Test Time Stain Normalization—0
Confidence Calibration for Object Detection and Segmentation—0
Improving Pallet Detection Using Synthetic Data—0
Improving Pixel Embedding Learning through Intermediate Distance Regression Supervision for Instance Segmentation—0
ConES: Concept Embedding Search for Parameter Efficient Tuning Large Vision Language Models—0
Attention-based Feature Aggregation—0
HIDA: Towards Holistic Indoor Understanding for the Visually Impaired via Semantic Instance Segmentation with a Wearable Solid-State LiDAR Sensor—0
Improving the Explain-Any-Concept by Introducing Nonlinearity to the Trainable Surrogate Model—0
Large-scale interactive object segmentation with human annotators—0
Adversarial Stain Transfer to Study the Effect of Color Variation on Cell Instance Segmentation—0
H-EMD: A Hierarchical Earth Mover's Distance Method for Instance Segmentation—0
A Dynamic Feature Interaction Framework for Multi-task Visual Perception—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
8GLEE-Promask AP54.2—Unverified
9ViT-Adapter-L (HTC++, BEiTv2, O365, multi-scale)mask AP54.2—Unverified
10SwinV2-G (HTC++)mask AP53.7—Unverified