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检索条件"主题词=weakly-supervised semantic segmentation"
33 条 记 录,以下是21-30 订阅
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SCC-CAM: weakly supervised segmentation on Brain Tumor MRI with Similarity Constraint and Causality  7th
SCC-CAM: Weakly Supervised Segmentation on Brain Tumor MRI w...
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7th Chinese Conference on Pattern Recognition and Computer Vision
作者: Jiao, Panpan Tian, Zhiqiang Chen, Zhang Guo, Xuejian Chen, Zhi Dou, Liang Du, Shaoyi Xi An Jiao Tong Univ Sch Software Engn Xian Shaanxi Peoples R China Ecovacs Robot Co Ltd Suzhou Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Shaanxi Peoples R China
This paper introduces SCC-CAM, a novel weakly supervised segmentation (WSSS) method for medical images. Transformer-based methods frequently face challenges like over-activation and inaccuracy in generating class atte... 详细信息
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Dual-aware Domain Mining and Cross-aware Supervision forweakly-supervised semantic segmentation
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ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 2023年 第7期17卷 101-101页
作者: Guo, Yuhui Liang, Xun Wu, Bo Zheng, Xiangping Zhang, Xuan Renmin Univ China 59 Zhong Guancun St Beijing 100872 Peoples R China
weakly supervised semantic segmentation with image-level annotation uses localization maps from the classifier to generate pseudo labels. However, such localization maps focus only on sparse salient object regions, it... 详细信息
来源: 评论
Which CAM is Better for Extracting Geographic Objects? A Perspective From Principles and Experiments
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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 5623-5635页
作者: Su, Qi Zhang, Xueliang Xiao, Pengfeng Li, Zhenshi Wang, Wenye Nanjing Univ Sch Geog & Ocean Sci Minist Nat ResourcesKey Lab Land Satellite Remot Jiangsu Prov Key Lab Geog Informat Sci & Technol Nanjing 210023 Peoples R China
As a method of deep learning interpretability, class activation mapping (CAM) is efficient and convenient for extracting geographic objects supervised by image-level labels. However, in addition to the inherent proble... 详细信息
来源: 评论
TRL: Transformer based refinement learning for hybrid-supervised semantic segmentation
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PATTERN RECOGNITION LETTERS 2022年 164卷 239-245页
作者: Cheng, Lin Fang, Pengfei Yan, Yan Lu, Yang Wang, Hanzi Xiamen Univ Sch Informat Fujian Key Lab Sensing & Comp Smart City Xiamen 361005 Peoples R China Australian Natl Univ Coll Engn & Comp Sci Canberra ACT 2601 Australia
This paper studies a new yet practical setting of semi-supervised semantic segmentation, i.e., hybrid-supervised semantic segmentation, where a small number of pixel-level (strong) annotations and a large number of im... 详细信息
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Learning Whole-Slide segmentation from Inexact and Incomplete Labels Using Tissue Graphs  24th
Learning Whole-Slide Segmentation from Inexact and Incomplet...
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International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)
作者: Anklin, Valentin Pati, Pushpak Jaume, Guillaume Bozorgtabar, Behzad Foncubierta-Rodriguez, Antonio Thiran, Jean-Philippe Sibony, Mathilde Gabrani, Maria Goksel, Orcun IBM Res Europe Zurich Switzerland Swiss Fed Inst Technol Zurich Switzerland Ecole Polytech Fed Lausanne Lausanne Switzerland Cochin Hosp Paris France Univ Paris Paris France Uppsala Univ Uppsala Sweden
Segmenting histology images into diagnostically relevant regions is imperative to support timely and reliable decisions by pathologists. To this end, computer-aided techniques have been proposed to delineate relevant ... 详细信息
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weakly-supervised PLATE AND FOOD REGION segmentation
WEAKLY-SUPERVISED PLATE AND FOOD REGION SEGMENTATION
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IEEE International Conference on Multimedia and Expo (ICME)
作者: Shimoda, Wataru Yanai, Keiji Univ Electro Commun Tokyo Japan
In this paper, we propose a novel method to infer plate regions of food images without any pixel-wise annotation. We synthesize plate segmentation masks using difference of visualization in food image classifiers. To ... 详细信息
来源: 评论
Structured Deep Learning for Pixel-level Understanding  18
Structured Deep Learning for Pixel-level Understanding
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26th ACM Multimedia Conference (MM)
作者: Wei, Yunchao Liang, Xiaodan Liu, Si Lin, Liang UIUC Beckman Inst Urbana IL 61801 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Beihang Univ Beijing Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China
This article summarizes the corresponding half-day tutorial at ACM Multimedia 2018. This tutorial reviews recent progresses for pixel-level understanding with structured deep learning, including 1) human-centric analy... 详细信息
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PUZZLE-CAM: IMPROVED LOCALIZATION VIA MATCHING PARTIAL AND FULL FEATURES
PUZZLE-CAM: IMPROVED LOCALIZATION VIA MATCHING PARTIAL AND F...
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IEEE International Conference on Image Processing (ICIP)
作者: Jo, Sanghyun Yu, In-Jae GYNetworks Incheon South Korea Korea Adv Inst Sci & Technol Sch Comp Daejeon South Korea
weakly-supervised semantic segmentation (WSSS) is introduced to narrow the gap for semantic segmentation performance from pixel-level supervision to image-level supervision. Most advanced approaches are based on class... 详细信息
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TPRO: Text-Prompting-Based weakly supervised Histopathology Tissue segmentation  26th
TPRO: Text-Prompting-Based Weakly Supervised Histopathology ...
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26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)
作者: Zhang, Shaoteng Zhang, Jianpeng Xie, Yutong Xia, Yong Northwestern Polytech Univ Ningbo Inst Ningbo 315048 Peoples R China Northwestern Polytech Univ Sch Comp Sci & Engn Natl Engn Lab Integrated Aero Space Ground Ocean Xian 710072 Peoples R China Univ Adelaide Australian Inst Machine Learning Adelaide SA Australia Northwestern Polytech Univ Inst Res & Dev Shenzhen 518057 Peoples R China
Most existing weakly-supervised segmentation methods rely on class activation maps (CAM) to generate pseudo-labels for training segmentation models. However, CAM has been criticized for highlighting only the most disc... 详细信息
来源: 评论
AME-CAM: Attentive Multiple-Exit CAM for weakly supervised segmentation on MRI Brain Tumor  26th
AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised S...
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26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)
作者: Chen, Yu-Jen Hu, Xinrong Shi, Yiyu Ho, Tsung-Yi Natl Tsing Hua Univ Hsinchu Taiwan Univ Notre Dame Notre Dame IN 46556 USA Chinese Univ Hong Kong Hong Kong Peoples R China
Magnetic resonance imaging (MRI) is commonly used for brain tumor segmentation, which is critical for patient evaluation and treatment planning. To reduce the labor and expertise required for labeling, weakly-supervis... 详细信息
来源: 评论