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

Showing 351375 of 2262 papers

TitleStatusHype
MP-PolarMask: A Faster and Finer Instance Segmentation for Concave Images0
MultiPly: Reconstruction of Multiple People from Monocular Video in the Wild0
An expert-driven data generation pipeline for histological imagesCode0
Layout Agnostic Scene Text Image Synthesis with Diffusion Models0
From Seedling to Harvest: The GrowingSoy Dataset for Weed Detection in Soy Crops via Instance SegmentationCode0
Extreme Point Supervised Instance Segmentation0
OpenDAS: Open-Vocabulary Domain Adaptation for 2D and 3D Segmentation0
Reasoning3D -- Grounding and Reasoning in 3D: Fine-Grained Zero-Shot Open-Vocabulary 3D Reasoning Part Segmentation via Large Vision-Language Models0
Adapting Pre-Trained Vision Models for Novel Instance Detection and SegmentationCode2
BAISeg: Boundary Assisted Weakly Supervised Instance SegmentationCode0
Understanding the Effect of using Semantically Meaningful Tokens for Visual Representation Learning0
Video Prediction Models as General Visual Encoders0
Efficient Temporal Action Segmentation via Boundary-aware Query VotingCode0
Autonomous Quilt Spreading for Caregiving Robots0
Harmony: A Joint Self-Supervised and Weakly-Supervised Framework for Learning General Purpose Visual RepresentationsCode0
Vision Transformer with Sparse Scan PriorCode0
PerSense: Personalized Instance Segmentation in Dense ImagesCode1
Unsupervised Pre-training with Language-Vision Prompts for Low-Data Instance SegmentationCode0
Semantic Equitable Clustering: A Simple and Effective Strategy for Clustering Vision Tokens0
Improving the Explain-Any-Concept by Introducing Nonlinearity to the Trainable Surrogate Model0
Unifying 3D Vision-Language Understanding via Promptable Queries0
DiverGen: Improving Instance Segmentation by Learning Wider Data Distribution with More Diverse Generative DataCode2
UDA4Inst: Unsupervised Domain Adaptation for Instance Segmentation0
MambaOut: Do We Really Need Mamba for Vision?Code7
PLUTO: Pathology-Universal Transformer0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1InternImage-HAP5080.8Unverified
2ResNeSt-200 (multi-scale)AP5070.2Unverified
3CenterMask + VoVNetV2-99 (multi-scale)AP5066.2Unverified
4CenterMask + VoVNetV2-57 (single-scale)AP5060.8Unverified
5Co-DETRmask AP57.1Unverified
6CBNetV2 (EVA02, single-scale)mask AP56.1Unverified
7ISDA (ResNet-50)APL55.7Unverified
8EVAmask AP55.5Unverified
9FD-SwinV2-Gmask AP55.4Unverified
10Mask Frozen-DETRmask AP55.3Unverified
#ModelMetricClaimedVerifiedStatus
1InternImage-BGFLOPs501Unverified
2Co-DETRmask AP56.6Unverified
3ViT-CoMer-L (Mask RCNN, DINOv2)mask AP55.9Unverified
4InternImage-Hmask AP55.4Unverified
5EVAmask AP55Unverified
6Mask Frozen-DETRmask AP54.9Unverified
7MasK DINO (SwinL, multi-scale)mask AP54.5Unverified
8ViT-Adapter-L (HTC++, BEiTv2, O365, multi-scale)mask AP54.2Unverified
9GLEE-Promask AP54.2Unverified
10SwinV2-G (HTC++)mask AP53.7Unverified