Papers Universal Segmentation
“Universal Segmentation” 태그가 달린 논문 32편 · 필터 해제
The Missing Point in Vision Transformers for Universal Image Segmentation
Image segmentation remains a challenging task in computer vision, demanding robust mask generation and precise classification. Recent mask-based approaches yield high-quality masks by capturing global context. However, a…
Image SegmentationInstance SegmentationPanoptic SegmentationSegmentation+2SemiSAM+: Rethinking Semi-Supervised Medical Image Segmentation in the Era of Foundation Models
Deep learning-based medical image segmentation typically requires large amount of labeled data for training, making it less applicable in clinical settings due to high annotation cost. Semi-supervised learning (SSL) has …
Image SegmentationMedical Image SegmentationSegmentationSemantic Segmentation+2SegAnyPET: Universal Promptable Segmentation from Positron Emission Tomography Images
Positron Emission Tomography (PET) imaging plays a crucial role in modern medical diagnostics by revealing the metabolic processes within a patient's body, which is essential for quantification of therapy response and mo…
Image SegmentationSegmentationSemantic SegmentationUniversal SegmentationMedicoSAM: Towards foundation models for medical image segmentation
Medical image segmentation is an important analysis task in clinical practice and research. Deep learning has massively advanced the field, but current approaches are mostly based on models trained for a specific task. T…
Image SegmentationInteractive SegmentationMedical Image SegmentationSegmentation+2FOCUS: 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+2VOILA: Complexity-Aware Universal Segmentation of CT images by Voxel Interacting with Language
Satisfactory progress has been achieved recently in universal segmentation of CT images. Following the success of vision-language methods, there is a growing trend towards utilizing text prompts and contrastive learning …
Contrastive LearningImage SegmentationSegmentationSemantic Segmentation+1HyperSeg: Hybrid Segmentation Assistant with Fine-grained Visual Perceiver
This paper aims to address universal segmentation for image and video perception with the strong reasoning ability empowered by Visual Large Language Models (VLLMs). Despite significant progress in current unified se…
Reasoning SegmentationSegmentationUniversal SegmentationVideo Segmentation+2Towards Continual Universal Segmentation
Despite the significant progress in continual image segmentation, existing arts still strive to balance between stability and plasticity. Additionally, they are specialist to specific tasks and models, which hinders …
Image SegmentationSegmentationSemantic SegmentationUniversal SegmentationInstructSeg: Unifying Instructed Visual Segmentation with Multi-modal Large Language Models
Boosted by Multi-modal Large Language Models (MLLMs), text-guided universal segmentation models for the image and video domains have made rapid progress recently. However, these methods are often developed separately for…
Reasoning SegmentationSegmentationUniversal SegmentationVideo Segmentation+2HyperSeg: Towards Universal Visual Segmentation with Large Language Model
This paper aims to address universal segmentation for image and video perception with the strong reasoning ability empowered by Visual Large Language Models (VLLMs). Despite significant progress in current unified segmen…
Language ModelingLarge Language ModelOpen Vocabulary Semantic SegmentationPanoptic Segmentation+9Large Language Model with Region-guided Referring and Grounding for CT Report Generation
Computed tomography (CT) report generation is crucial to assist radiologists in interpreting CT volumes, which can be time-consuming and labor-intensive. Existing methods primarily only consider the global features of th…
Computed Tomography (CT)DiagnosticLanguage ModelingLanguage Modelling+3Rethinking the Evaluation of Visible and Infrared Image Fusion
Visible and Infrared Image Fusion (VIF) has garnered significant interest across a wide range of high-level vision tasks, such as object detection and semantic segmentation. However, the evaluation of VIF methods remains…
object-detectionObject DetectionSegmentationSemantic Segmentation+1MedUniSeg: 2D and 3D Medical Image Segmentation via a Prompt-driven Universal Model
Universal segmentation models offer significant potential in addressing a wide range of tasks by effectively leveraging discrete annotations. As the scope of tasks and modalities expands, it becomes increasingly importan…
Image SegmentationMedical Image SegmentationSegmentationSemantic Segmentation+1OMG-LLaVA: Bridging Image-level, Object-level, Pixel-level Reasoning and Understanding
Current universal segmentation methods demonstrate strong capabilities in pixel-level image and video understanding. However, they lack reasoning abilities and cannot be controlled via text instructions. In contrast, lar…
DecoderSegmentationUniversal SegmentationVideo UnderstandingBeyond Human Vision: The Role of Large Vision Language Models in Microscope Image Analysis
Vision language models (VLMs) have recently emerged and gained the spotlight for their ability to comprehend the dual modality of image and textual data. VLMs such as LLaVA, ChatGPT-4, and Gemini have recently shown impr…
Image CaptioningQuestion AnsweringSpatial ReasoningUniversal Segmentation+2RadGenome-Chest CT: A Grounded Vision-Language Dataset for Chest CT Analysis
Developing generalist foundation model has recently attracted tremendous attention among researchers in the field of AI for Medicine (AI4Medicine). A pivotal insight in developing these models is their reliance on datase…
SegmentationSentenceUniversal SegmentationVisual Question Answering (VQA)Progressive Token Length Scaling in Transformer Encoders for Efficient Universal Segmentation
A powerful architecture for universal segmentation relies on transformers that encode multi-scale image features and decode object queries into mask predictions. With efficiency being a high priority for scaling such mod…
object-detectionObject DetectionSegmentationUniversal SegmentationCOCONut: Modernizing COCO Segmentation
In recent decades, the vision community has witnessed remarkable progress in visual recognition, partially owing to advancements in dataset benchmarks. Notably, the established COCO benchmark has propelled the developmen…
Panoptic SegmentationSegmentationUniversal SegmentationSegICL: A Multimodal In-context Learning Framework for Enhanced Segmentation in Medical Imaging
In the field of medical image segmentation, tackling Out-of-Distribution (OOD) segmentation tasks in a cost-effective manner remains a significant challenge. Universal segmentation models is a solution, which aim to gene…
Few-Shot LearningImage SegmentationIn-Context LearningMedical Image Segmentation+3Unsupervised Universal Image Segmentation
Several unsupervised image segmentation approaches have been proposed which eliminate the need for dense manually-annotated segmentation masks; current models separately handle either semantic segmentation (e.g., STEGO) …
Image SegmentationInstance SegmentationPanoptic SegmentationSegmentation+8