Copertina

An unsupervised approach towards promptable defect segmentation in laser-based additive manufacturing by Segment Anything

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Era, Israt Zarin, Ahmed, Imtiaz, Liu, Zhichao, Das, Srinjoy
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Computer Vision and Pattern Recognition
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

In-Situ Melt Pool Characterization via Thermal Imaging for Defect Detection in Directed Energy Deposition Using Vision Transformers
di: Era, Israt Zarin, et al.
Pubblicazione: (2024)

3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation
di: Gong, Shizhan, et al.
Pubblicazione: (2023)

Inference-time Stochastic Refinement of GRU-Normalizing Flow for Real-time Video Motion Transfer
di: Haque, Tasmiah, et al.
Pubblicazione: (2025)

Text-promptable Object Counting via Quantity Awareness Enhancement
di: Shi, Miaojing, et al.
Pubblicazione: (2025)

UVOSAM: A Mask-free Paradigm for Unsupervised Video Object Segmentation via Segment Anything Model
di: Zhang, Zhenghao, et al.
Pubblicazione: (2023)

Matching Anything by Segmenting Anything
di: Li, Siyuan, et al.
Pubblicazione: (2024)

Promoting Segment Anything Model towards Highly Accurate Dichotomous Image Segmentation
di: Liu, Xianjie, et al.
Pubblicazione: (2023)

Deep Gaussian mixture model for unsupervised image segmentation
di: Schwab, Matthias, et al.
Pubblicazione: (2024)

Deep asymmetric mixture model for unsupervised cell segmentation
di: Nan, Yang, et al.
Pubblicazione: (2024)

Deep kernel video approximation for unsupervised action segmentation
di: Pintea, Silvia L., et al.
Pubblicazione: (2026)

Segment and Caption Anything
di: Huang, Xiaoke, et al.
Pubblicazione: (2023)

A Data-Efficient Sequential Learning Framework for Melt Pool Defect Classification in Laser Powder Bed Fusion
di: Raihan, Ahmed Shoyeb, et al.
Pubblicazione: (2024)

Towards Efficient Real-Time Video Motion Transfer via Generative Time Series Modeling
di: Haque, Tasmiah, et al.
Pubblicazione: (2025)

Matte Anything: Interactive Natural Image Matting with Segment Anything Models
di: Yao, Jingfeng, et al.
Pubblicazione: (2023)

PosSAM: Panoptic Open-vocabulary Segment Anything
di: VS, Vibashan, et al.
Pubblicazione: (2024)

RadSAM: Segmenting 3D radiological images with a 2D promptable model
di: Khlaut, Julien, et al.
Pubblicazione: (2025)

Contour Field based Elliptical Shape Prior for the Segment Anything Model
di: Zhao, Xinyu, et al.
Pubblicazione: (2025)

Bridge the Points: Graph-based Few-shot Segment Anything Semantically
di: Zhang, Anqi, et al.
Pubblicazione: (2024)

SAMamba3D: adapting Segment Anything for generalizable 3D segmentation of multiphase pore-scale images
di: Zhang, Rui, et al.
Pubblicazione: (2026)

Register Anything: Estimating "Corresponding Prompts" for Segment Anything Model
di: Huang, Shiqi, et al.
Pubblicazione: (2025)

CLISC: Bridging clip and sam by enhanced cam for unsupervised brain tumor segmentation
di: Ma, Xiaochuan, et al.
Pubblicazione: (2025)

SegAssess: Panoramic quality mapping for robust and transferable unsupervised segmentation assessment
di: Yang, Bingnan, et al.
Pubblicazione: (2025)

Hi-SAM: Marrying Segment Anything Model for Hierarchical Text Segmentation
di: Ye, Maoyuan, et al.
Pubblicazione: (2024)

Measure Anything: Real-time, Multi-stage Vision-based Dimensional Measurement using Segment Anything
di: Lee, Yongkyu, et al.
Pubblicazione: (2024)

Segment Anything, Even Occluded
di: Tai, Wei-En, et al.
Pubblicazione: (2025)

Segment Anything with Multiple Modalities
di: Xiao, Aoran, et al.
Pubblicazione: (2024)

Segment Anything for Cell Tracking
di: Chen, Zhu, et al.
Pubblicazione: (2025)

Perceive Anything: Recognize, Explain, Caption, and Segment Anything in Images and Videos
di: Lin, Weifeng, et al.
Pubblicazione: (2025)

Superpixel Anything: A general object-based framework for accurate yet regular superpixel segmentation
di: Walther, Julien, et al.
Pubblicazione: (2025)

S-SAM: SVD-based Fine-Tuning of Segment Anything Model for Medical Image Segmentation
di: Paranjape, Jay N., et al.
Pubblicazione: (2024)

Segment Anything Model Can Not Segment Anything: Assessing AI Foundation Model's Generalizability in Permafrost Mapping
di: Li, Wenwen, et al.
Pubblicazione: (2024)

Boosting Few-Shot Semantic Segmentation Via Segment Anything Model
di: Feng, Chen-Bin, et al.
Pubblicazione: (2024)

Adapting Segment Anything Model for Power Transmission Corridor Hazard Segmentation
di: Chen, Hang, et al.
Pubblicazione: (2025)

QA-VLM: Providing human-interpretable quality assessment for wire-feed laser additive manufacturing parts with Vision Language Models
di: Zheng, Qiaojie, et al.
Pubblicazione: (2025)

FluoroSAM: A Language-promptable Foundation Model for Flexible X-ray Image Segmentation
di: Killeen, Benjamin D., et al.
Pubblicazione: (2024)

Scene-aware SAR ship detection guided by unsupervised sea-land segmentation
di: Ke, Han, et al.
Pubblicazione: (2025)

StAR: Segment Anything Reasoner
di: Yun, Seokju, et al.
Pubblicazione: (2026)

Learning to Prompt Segment Anything Models
di: Huang, Jiaxing, et al.
Pubblicazione: (2024)

MESA: Matching Everything by Segmenting Anything
di: Zhang, Yesheng, et al.
Pubblicazione: (2024)

On Efficient Variants of Segment Anything Model: A Survey
di: Sun, Xiaorui, et al.
Pubblicazione: (2024)