Papers Image Segmentation
“Image Segmentation” 태그가 달린 논문 5,370편 · 필터 해제
RobotWorld: Benchmarking Multimodal Agents for Robot Use Across Diverse Tasks and Embodiments
General-purpose agents increasingly write code, use tools, and complete complex digital tasks, raising the question of how far these capabilities carry into the physical world. To investigate this, we introduce RobotWorl…
Image SegmentationFSANet: Frequency-Spatial Aware Network for Image Segmentation
Image segmentation remains challenging due to occlusions, poor lighting, and irregular structures. Although transformer-based methods achieve high accuracy, they rely heavily on long-range spatial features, leading to hi…
Image SegmentationCost-efficient Active Learning for Referring Image Segmentation and Grounding
Collecting natural-language referring expressions along with region annotations, such as masks or boxes, is a major bottleneck in visual grounding (VG), as annotators must write descriptions that distinguish target regio…
Image SegmentationVisual GroundingActive LearningMCSeg: Pre-training and Fine-tuning Volumetric Pyramid Transformer for Multi-modal Cardiac Image Segmentation
Automatic cardiac image segmentation is pivotal for diagnosing and treating cardiac diseases. In this work, we introduce MCSeg, a volumetric transformer-based network tailored for multi-modal cardiac segmentation. To ove…
Image SegmentationFU-Mamba: A Frequency-Enhanced Dynamic Scanning Framework for Oralscan Image Segmentation
Oralscan image segmentation is essential for computer-aided diagnosis and treatment planning in digital dentistry. However, existing visual state space models (SSMs) often rely on manually designed scanning orders to fla…
Image SegmentationThe canonical facets of multi-separator polytopes
We initiate a polyhedral study of the graph multi-separator problem proposed by Irmai et al. (2024) as an alternative to the lifted multicut problem for application to the task of image segmentation. Starting with an int…
Image SegmentationDiCoR: Decoupled Referent Disambiguation and Contour Recalibration for Efficient Referring Remote Sensing Image Segmentation
Referring remote sensing image segmentation (RRSIS) aims to delineate targets specified by natural language expressions in remote sensing imagery. Existing methods mainly follow joint fusion segmentation (JFS) or decoupl…
Image SegmentationAeroReformer2: Spoken-Query Referring Segmentation for Aerial Images
Spoken language offers a natural, hands-free interface for specifying an arbitrary target in dense remote-sensing imagery, yet existing referring remote-sensing image segmentation benchmarks accept only written expressio…
Image SegmentationVision-Language Grounding as Bidirectional Concept Correspondence
Vision-language grounding connects language to visual content, yet most existing formulations reduce grounding to a unidirectional localization problem: given a prespecified text phrase or category name, identify the cor…
Referring ExpressionImage SegmentationPhrase GroundingUniversal Concept Disruption for SAM3 Image Segmentation
SAM3 extends promptable segmentation from geometry-driven mask prediction to open-vocabulary concept segmentation, where a text-conditioned grounding model decides whether a concept is present and segments all matching i…
Adversarial RobustnessImage SegmentationAdversarial AttackCROSS: Cascaded Distillation and Dual-Constraint Grounding for Remote Sensing Referring Segmentation
Referring Remote Sensing Image Segmentation (RRSIS) has achieved significant progress through the integration of VLMs and the Segment Anything Model (SAM). However, this progress largely relies on strong pre-trained capa…
Contrastive LearningImage SegmentationSpatial ReasoningMBO Scheme for Local Chan--Vese Segmentation
Robust to intensity inhomogeneity, the local Chan--Vese (LCV) model extends the classical Chan--Vese (CV) image segmentation method by incorporating local statistical information around each pixel. Originally, the LCV mo…
Image SegmentationOpen-Ended CT Volume Segmentation with Weak Supervision from Language
We introduce a method for training a text-conditioned segmentation model for CT scans, which combines voxel-level supervision with coarse but scalable slice-level supervision from reports. We extract, from a large databa…
Image SegmentationFood Image Segmentation with LLM-Derived Ingredient Labels and Multimodal Fusion
Food image segmentation plays a vital role in health-related applications such as nutrition tracking and personalized health monitoring. However, existing models often underperform on visually similar ingredients and rar…
Image SegmentationLloyd's $K$-Means Clustering Algorithm Is Frank-Wolfe in Disguise
Lloyd's $K$-means algorithm, also known as naïve $K$-means, is a widely used ad hoc optimization heuristic, designed to minimize the sum of squared errors (SSE) across all $K$-partitions of a dataset via iterative cluste…
Image SegmentationA Unified Variational Framework for Deep Weakly Supervised Image Segmentation
We propose a unified variational framework for image segmentation under sparse pixel-level supervision. Our method is based on a simplex-constrained Potts model with a smooth perimeter regularizer, yielding a convex, smo…
Image SegmentationFrom Reconstruction to Interpretation: Zero-Setup Multi-Phase Segmentation of X-ray Tomography Data
X-ray tomography enables nondestructive characterization of material microstructures, while advances in micro-CT imaging have accelerated volumetric data acquisition and reconstruction. However, rapid interpretation rema…
Semantic SegmentationImage SegmentationAlign and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels
Supervised learning for image segmentation typically requires spatially aligned image and label sets. When images and labels originate from different sources, the pairing may be misaligned, which can significantly deteri…
Semantic SegmentationImage SegmentationData AugmentationToward Efficient Weakly Supervised Semantic Segmentation Using Only Low-Magnification Histopathological Images
Whole-slide images (WSIs) provide rich tissue-level and cellular-level information, but storing and transmitting high-magnification pathology data is resource-intensive. Moreover, annotating WSIs at the pixel level is la…
Semantic SegmentationImage SegmentationAttribute Retrieving for Open-Vocabulary Endoscopic Compositional Referring Segmentation
Referring Image Segmentation (RIS) aims to segment image regions specified by natural language, enabling fine-grained and controllable visual understanding. Extending RIS to endoscopic imagery, however, presents unique c…
Image Segmentation