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Foreground Segmentation

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Showing 1–25 of 84 papers

TitleStatusHype
Seg-R1: Segmentation Can Be Surprisingly Simple with Reinforcement LearningCode2
E-InMeMo: Enhanced Prompting for Visual In-Context LearningCode1
Vision-Centric Representation-Efficient Fine-Tuning for Robust Universal Foreground Segmentation—0
PraNet-V2: Dual-Supervised Reverse Attention for Medical Image SegmentationCode1
FOCUS: Towards Universal Foreground SegmentationCode2
A Unified Framework for Foreground and Anonymization Area Segmentation in CT and MRI DataCode0
MRI Breast tissue segmentation using nnU-Net for biomechanical modeling—0
CLOVER: Context-aware Long-term Object Viewpoint- and Environment- Invariant Representation Learning—0
Towards Global Optimal Visual In-Context Learning Prompt Selection—0
MUSTAN: Multi-scale Temporal Context as Attention for Robust Video Foreground Segmentation—0
PartSTAD: 2D-to-3D Part Segmentation Task AdaptationCode2
Kernel Adaptive Convolution for Scene Text Detection via Distance Map Prediction—0
Cable Slack Detection for Arresting Gear Application using Machine Vision—0
IMProv: Inpainting-based Multimodal Prompting for Computer Vision Tasks—0
Instruct Me More! Random Prompting for Visual In-Context LearningCode1
OpenIllumination: A Multi-Illumination Dataset for Inverse Rendering Evaluation on Real Objects—0
AquaSAM: Underwater Image Foreground SegmentationCode0
BEVControl: Accurately Controlling Street-view Elements with Multi-perspective Consistency via BEV Sketch Layout—0
Explicit Visual Prompting for Universal Foreground SegmentationsCode2
Retinal Vessel Segmentation via a Multi-resolution Contextual Network and Adversarial Learning—0
ZBS: Zero-shot Background Subtraction via Instance-level Background Modeling and Foreground SelectionCode1
Explicit Visual Prompting for Low-Level Structure SegmentationsCode2
Markerless Camera-to-Robot Pose Estimation via Self-supervised Sim-to-Real Transfer—0
Visual Prompting via Image InpaintingCode2
Spacecraft depth completion based on the gray image and the sparse depth map—0
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