Papers Foreground Segmentation
“Foreground Segmentation” 태그가 달린 논문 84편 · 필터 해제
Seg-R1: Segmentation Can Be Surprisingly Simple with Reinforcement Learning
We present Seg-R1, a preliminary exploration of using reinforcement learning (RL) to enhance the pixel-level understanding and reasoning capabilities of large multimodal models (LMMs). Starting with foreground segmentati…
Foreground Segmentationobject-detectionObject DetectionReasoning Segmentation+3E-InMeMo: Enhanced Prompting for Visual In-Context Learning
Large-scale models trained on extensive datasets have become the standard due to their strong generalizability across diverse tasks. In-context learning (ICL), widely used in natural language processing, leverages these …
Foreground SegmentationIn-Context Learningobject-detectionObject DetectionVision-Centric Representation-Efficient Fine-Tuning for Robust Universal Foreground Segmentation
Foreground segmentation is crucial for scene understanding, yet parameter-efficient fine-tuning (PEFT) of vision foundation models (VFMs) often fails in complex scenarios, such as camouflage and infrared imagery. We attr…
AttributeForeground Segmentationparameter-efficient fine-tuningScene UnderstandingPraNet-V2: Dual-Supervised Reverse Attention for Medical Image Segmentation
Accurate medical image segmentation is essential for effective diagnosis and treatment. Previously, PraNet-V1 was proposed to enhance polyp segmentation by introducing a reverse attention (RA) module that utilizes backgr…
Foreground SegmentationImage SegmentationMedical Image SegmentationSegmentation+1FOCUS: Towards Universal Foreground Segmentation
Foreground segmentation is a fundamental task in computer vision, encompassing various subdivision tasks. Previous research has typically designed task-specific architectures for each task, leading to a lack of unificati…
Camouflaged Object SegmentationDefocus Blur DetectionForeground SegmentationSalient Object Detection+2A Unified Framework for Foreground and Anonymization Area Segmentation in CT and MRI Data
This study presents an open-source toolkit to address critical challenges in preprocessing data for self-supervised learning (SSL) for 3D medical imaging, focusing on data privacy and computational efficiency. The toolki…
Computational EfficiencyForeground SegmentationSegmentationSelf-Supervised LearningMRI Breast tissue segmentation using nnU-Net for biomechanical modeling
Integrating 2D mammography with 3D magnetic resonance imaging (MRI) is crucial for improving breast cancer diagnosis and treatment planning. However, this integration is challenging due to differences in imaging modaliti…
3D ReconstructionDiagnosticForeground SegmentationSegmentationCLOVER: Context-aware Long-term Object Viewpoint- and Environment- Invariant Representation Learning
In many applications, robots can benefit from object-level understanding of their environments, including the ability to distinguish object instances and re-identify previously seen instances. Object re-identification is…
Foreground SegmentationObjectRepresentation LearningTowards Global Optimal Visual In-Context Learning Prompt Selection
Visual In-Context Learning (VICL) is a prevailing way to transfer visual foundation models to new tasks by leveraging contextual information contained in in-context examples to enhance learning and prediction of query sa…
ColorizationForeground SegmentationImage ColorizationIn-Context Learning+2MUSTAN: Multi-scale Temporal Context as Attention for Robust Video Foreground Segmentation
Video foreground segmentation (VFS) is an important computer vision task wherein one aims to segment the objects under motion from the background. Most of the current methods are image-based, i.e., rely only on spatial c…
Foreground SegmentationOptical Flow EstimationPartSTAD: 2D-to-3D Part Segmentation Task Adaptation
We introduce PartSTAD, a method designed for the task adaptation of 2D-to-3D segmentation lifting. Recent studies have highlighted the advantages of utilizing 2D segmentation models to achieve high-quality 3D segmentatio…
3D Part SegmentationForeground SegmentationInstance SegmentationSegmentation+1Kernel Adaptive Convolution for Scene Text Detection via Distance Map Prediction
Segmentation-based scene text detection algorithms that are accurate to the pixel level can satisfy the detection of arbitrary shape scene text and have received widespread attention. On the one hand due to the compl…
Foreground SegmentationScene Text DetectionText DetectionIMProv: Inpainting-based Multimodal Prompting for Computer Vision Tasks
In-context learning allows adapting a model to new tasks given a task description at test time. In this paper, we present IMProv - a generative model that is able to in-context learn visual tasks from multimodal prompts.…
ColorizationForeground SegmentationIn-Context Learningobject-detection+1Cable Slack Detection for Arresting Gear Application using Machine Vision
The cable-based arrestment systems are integral to the launch and recovery of aircraft onboard carriers and on expeditionary land-based installations. These modern arrestment systems rely on various mechanisms to absorb …
Edge DetectionForeground SegmentationInstruct Me More! Random Prompting for Visual In-Context Learning
Large-scale models trained on extensive datasets, have emerged as the preferred approach due to their high generalizability across various tasks. In-context learning (ICL), a popular strategy in natural language processi…
Foreground SegmentationIn-Context Learningobject-detectionObject DetectionOpenIllumination: A Multi-Illumination Dataset for Inverse Rendering Evaluation on Real Objects
We introduce OpenIllumination, a real-world dataset containing over 108K images of 64 objects with diverse materials, captured under 72 camera views and a large number of different illuminations. For each image in the da…
Foreground SegmentationInverse RenderingAquaSAM: Underwater Image Foreground Segmentation
The Segment Anything Model (SAM) has revolutionized natural image segmentation, nevertheless, its performance on underwater images is still restricted. This work presents AquaSAM, the first attempt to extend the success …
Foreground SegmentationImage SegmentationSegmentationSemantic SegmentationBEVControl: Accurately Controlling Street-view Elements with Multi-perspective Consistency via BEV Sketch Layout
Using synthesized images to boost the performance of perception models is a long-standing research challenge in computer vision. It becomes more eminent in visual-centric autonomous driving systems with multi-view camera…
Autonomous DrivingBEV SegmentationForeground SegmentationSegmentationExplicit Visual Prompting for Universal Foreground Segmentations
Foreground segmentation is a fundamental problem in computer vision, which includes salient object detection, forgery detection, defocus blur detection, shadow detection, and camouflage object detection. Previous works h…
Camouflaged Object SegmentationDefocus Blur DetectionForeground SegmentationImage Manipulation Detection+4Retinal Vessel Segmentation via a Multi-resolution Contextual Network and Adversarial Learning
Timely and affordable computer-aided diagnosis of retinal diseases is pivotal in precluding blindness. Accurate retinal vessel segmentation plays an important role in disease progression and diagnosis of such vision-thre…
Foreground SegmentationRetinal Vessel SegmentationSegmentation