Universal precautions

Latest AI and machine learning research in universal precautions for healthcare professionals.

2,998 articles

Marten: Visual Question Answering with Mask Generation for Multi-modal Document Understanding

Multi-modal Large Language Models (MLLMs) have introduced a novel dimension to document understand...

Mar 2025 http://arxiv.org/abs/2503.14140v1

Adams Bashforth Moulton Solver for Inversion and Editing in Rectified Flow

Rectified flow models have achieved remarkable performance in image and video generation tasks. Ho...

Mar 2025 http://arxiv.org/abs/2503.16522v1

HiMTok: Learning Hierarchical Mask Tokens for Image Segmentation with Large Multimodal Model

The remarkable performance of large multimodal models (LMMs) has attracted significant interest fr...

Mar 2025 http://arxiv.org/abs/2503.13026v1

E-SAM: Training-Free Segment Every Entity Model

Entity Segmentation (ES) aims at identifying and segmenting distinct entities within an image with...

Mar 2025 http://arxiv.org/abs/2503.12094v1

SeqSAM: Autoregressive Multiple Hypothesis Prediction for Medical Image Segmentation using SAM

Pre-trained segmentation models are a powerful and flexible tool for segmenting images. Recently, ...

Mar 2025 http://arxiv.org/abs/2503.09797v1

FCaS: Fine-grained Cardiac Image Synthesis based on 3D Template Conditional Diffusion Model

Solving medical imaging data scarcity through semantic image generation has attracted significant ...

Mar 2025 http://arxiv.org/abs/2503.09560v1

MaskAttn-UNet: A Mask Attention-Driven Framework for Universal Low-Resolution Image Segmentation

Low-resolution image segmentation is crucial in real-world applications such as robotics, augmente...

Mar 2025 http://arxiv.org/abs/2503.10686v2

MF-VITON: High-Fidelity Mask-Free Virtual Try-On with Minimal Input

Recent advancements in Virtual Try-On (VITON) have significantly improved image realism and garmen...

Mar 2025 http://arxiv.org/abs/2503.08650v1

SegAgent: Exploring Pixel Understanding Capabilities in MLLMs by Imitating Human Annotator Trajectories

While MLLMs have demonstrated adequate image understanding capabilities, they still struggle with ...

Mar 2025 http://arxiv.org/abs/2503.08625v1

Diffusion Transformer Meets Random Masks: An Advanced PET Reconstruction Framework

Deep learning has significantly advanced PET image re-construction, achieving remarkable improveme...

Mar 2025 http://arxiv.org/abs/2503.08339v1

PRISM: Privacy-Preserving Improved Stochastic Masking for Federated Generative Models

Despite recent advancements in federated learning (FL), the integration of generative models into ...

Mar 2025 http://arxiv.org/abs/2503.08085v3

Customized SAM 2 for Referring Remote Sensing Image Segmentation

Referring Remote Sensing Image Segmentation (RRSIS) aims to segment target objects in remote sensi...

Mar 2025 http://arxiv.org/abs/2503.07266v2

OmniSAM: Omnidirectional Segment Anything Model for UDA in Panoramic Semantic Segmentation

Segment Anything Model 2 (SAM2) has emerged as a strong base model in various pinhole imaging segm...

Mar 2025 http://arxiv.org/abs/2503.07098v1

DiffAtlas: GenAI-fying Atlas Segmentation via Image-Mask Diffusion

Accurate medical image segmentation is crucial for precise anatomical delineation. Deep learning m...

Mar 2025 http://arxiv.org/abs/2503.06748v1

Fits like a Flex-Glove: Automatic Design of Personalized FPCB-Based Tactile Sensing Gloves

Resistive tactile sensing gloves have captured the interest of researchers spanning diverse domain...

Mar 2025 http://arxiv.org/abs/2503.06349v1

Segment Anything, Even Occluded

Amodal instance segmentation, which aims to detect and segment both visible and invisible parts of...

Mar 2025 http://arxiv.org/abs/2503.06261v1

ForestSplats: Deformable transient field for Gaussian Splatting in the Wild

Recently, 3D Gaussian Splatting (3D-GS) has emerged, showing real-time rendering speeds and high-q...

Mar 2025 http://arxiv.org/abs/2503.06179v2

Improving SAM for Camouflaged Object Detection via Dual Stream Adapters

Segment anything model (SAM) has shown impressive general-purpose segmentation performance on natu...

Mar 2025 http://arxiv.org/abs/2503.06042v2

Conformal Prediction for Image Segmentation Using Morphological Prediction Sets

Image segmentation is a challenging task influenced by multiple sources of uncertainty, such as th...

Mar 2025 http://arxiv.org/abs/2503.05618v1

S4M: Segment Anything with 4 Extreme Points

The Segment Anything Model (SAM) has revolutionized open-set interactive image segmentation, inspi...

Mar 2025 http://arxiv.org/abs/2503.05534v1

PathoPainter: Augmenting Histopathology Segmentation via Tumor-aware Inpainting

Tumor segmentation plays a critical role in histopathology, but it requires costly, fine-grained i...

Mar 2025 http://arxiv.org/abs/2503.04634v1

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