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Weakly Supervised Instance Segmentation by Deep Community Learning [article]

Jaedong Hwang, Seohyun Kim, Jeany Son, Bohyung Han
2020 arXiv   pre-print
We present a weakly supervised instance segmentation algorithm based on deep community learning with multiple tasks.  ...  This task is formulated as a combination of weakly supervised object detection and semantic segmentation, where individual objects of the same class are identified and segmented separately.  ...  The proposed community learning framework for weakly supervised instance segmentation.  ... 
arXiv:2001.11207v3 fatcat:b5wdslbtezf65ezansgzdkisai

Weakly Supervised Object Localization and Detection: A Survey [article]

Dingwen Zhang, Junwei Han, Gong Cheng, Ming-Hsuan Yang
2021 arXiv   pre-print
As an emerging and challenging problem in the computer vision community, weakly supervised object localization and detection plays an important role for developing new generation computer vision systems  ...  In this work, we review (1) classic models, (2) approaches with feature representations from off-the-shelf deep networks, (3) approaches solely based on deep learning, and (4) publicly available datasets  ...  Multi-Task Learning Another future direction is to combine multiple weakly supervised learning tasks into a unified learning framework.  ... 
arXiv:2104.07918v1 fatcat:dwl6sjfzibdilnvjnrbifp4uke

Deep multiple instance learning for image classification and auto-annotation

Jiajun Wu, Yinan Yu, Chang Huang, Kai Yu
2015 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
In this paper, we attempt to model deep learning in a weakly supervised learning (multiple instance learning) framework.  ...  We conduct extensive experiments and prove that our weakly supervised deep learning framework not only achieves convincing performance in vision tasks including classification and image annotation, but  ...  We hope our findings could arouse further research in the fields of deep learning and weakly supervised learning in the vision community.  ... 
doi:10.1109/cvpr.2015.7298968 dblp:conf/cvpr/WuYHY15 fatcat:x4b4cmcuvzbdphuevi6f2upy2a

Medical Image Segmentation with 3D Convolutional Neural Networks: A Survey [article]

S Niyas, S J Pawan, M Anand Kumar, Jeny Rajan
2022 arXiv   pre-print
Here, we present an extensive review of the recently evolved 3D deep learning methods in medical image segmentation.  ...  In addition, with the rapid advancements in three-dimensional (3D) imaging systems and the availability of excellent hardware and software support to process large volumes of data, 3D deep learning methods  ...  segmentation models (c) Multi-task learning models 2. 3D CNN with Semi-supervised learning 3. 3D CNN with Weakly-supervised learning 4.  ... 
arXiv:2108.08467v3 fatcat:s2rzghycjbczpparmrflsdzujq

Cyclic Guidance for Weakly Supervised Joint Detection and Segmentation

Yunhang Shen, Rongrong Ji, Yan Wang, Yongjian Wu, Liujuan Cao
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
In this paper, we join weakly supervised object detection and segmentation tasks with a multi-task learning scheme for the first time, which uses their respective failure patterns to complement each other's  ...  Weakly supervised learning has attracted growing research attention due to the significant saving in annotation cost for tasks that require intra-image annotations, such as object detection and semantic  ...  Multi-task Learning. Learning detection and segmentation jointly was first employed by Hariharan et al. [29] in fully supervised learning.  ... 
doi:10.1109/cvpr.2019.00079 dblp:conf/cvpr/ShenJWWC19 fatcat:k7rkomxfwzchzpnw76iz7f4dsm

DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision [article]

Shiyi Lan, Zhiding Yu, Christopher Choy, Subhashree Radhakrishnan, Guilin Liu, Yuke Zhu, Larry S. Davis, Anima Anandkumar
2021 arXiv   pre-print
We introduce DiscoBox, a novel framework that jointly learns instance segmentation and semantic correspondence using bounding box supervision.  ...  Our best model achieves 37.9% AP on COCO instance segmentation, surpassing prior weakly supervised methods and is competitive to supervised methods.  ...  WILDCAT: Weakly supervised learning of Jiao. Weakly supervised instance segmentation using class deep convnets for image classification, pointwise localiza- peak response.  ... 
arXiv:2105.06464v2 fatcat:wr5iyiqvivb3novhhkgxwk6mv4

ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases

Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, Ronald M. Summers
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
Importantly, we demonstrate that these commonly occurring thoracic diseases can be detected and even spatially-located via a unified weakly-supervised multi-label image classification and disease localization  ...  A tremendous number of X-ray imaging studies accompanied by radiological reports are accumulated and stored in many modern hospitals' Picture Archiving and Communication Systems (PACS).  ...  Acknowledgements This work was supported by the Intramural Research Programs of the NIH Clinical Center and National Library of Medicine. We thank NVIDIA Corporation for the GPU donation.  ... 
doi:10.1109/cvpr.2017.369 dblp:conf/cvpr/WangPLLBS17 fatcat:7fk6qbqutzd7flnh5jwioiyhou

A Survey of Visual Sensory Anomaly Detection [article]

Xi Jiang, Guoyang Xie, Jinbao Wang, Yong Liu, Chengjie Wang, Feng Zheng, Yaochu Jin
2022 arXiv   pre-print
Furthermore, we classify each kind of anomaly according to the level of supervision. Finally, we summarize the challenges and provide open directions for this community.  ...  However, no thorough review has been provided to summarize this area for the computer vision community.  ...  instance segmentation.  ... 
arXiv:2202.07006v1 fatcat:2bqzmmrnjzggti5tcewa3mh3sa

A Brief Survey on Weakly Supervised Semantic Segmentation

Youssef Ouassit, Soufiane Ardchir, Mohammed Yassine El Ghoumari, Mohamed Azouazi
2022 International Journal of Online and Biomedical Engineering (iJOE)  
This paper aims to provide a brief survey of research efforts on deep-learning-based semantic segmentation methods on limited labeled data and focus our survey on weakly-supervised methods.  ...  This survey is expected to familiarize readers with the progress and challenges of weakly supervised semantic segmentation research in the deep learning era and present several valuable growing research  ...  Weakly supervised methods: State of art Weakly supervised learning methods are a set of models which attempt to build predictive models by learning with weak supervision.  ... 
doi:10.3991/ijoe.v18i10.31531 fatcat:6klflaiecrdgrizzlpgybimt6q

Deep neural network models for computational histopathology: A survey [article]

Chetan L. Srinidhi, Ozan Ciga, Anne L. Martel
2019 arXiv   pre-print
From the survey of over 130 papers, we review the fields progress based on the methodological aspect of different machine learning strategies such as supervised, weakly supervised, unsupervised, transfer  ...  We also provide an overview of deep learning based survival models that are applicable for disease-specific prognosis tasks.  ...  In histology, Xu et al. (2017) formulated the gland instance segmentation as two sub-tasks -gland segmentation and instance recognition task, using a multi-channel deep network model (Dai et al., 2016  ... 
arXiv:1912.12378v1 fatcat:xdfkzzwzb5alhjfhffqpcurb2u

Weakly Supervised Semantic Point Cloud Segmentation:Towards 10X Fewer Labels [article]

Xun Xu, Gim Hee Lee
2020 arXiv   pre-print
In this work, we propose a weakly supervised point cloud segmentation approach which requires only a tiny fraction of points to be labelled in the training stage.  ...  This is made possible by learning gradient approximation and exploitation of additional spatial and color smoothness constraints.  ...  This work was partially supported by the Singapore MOE Tier 1 grant R-252-000-A65-114.  ... 
arXiv:2004.04091v1 fatcat:74rdfskoffepvbi5jouzny7jbe

Weakly-Supervised Learning of a Deep Convolutional Neural Networks for Semantic Segmentation

Yanqing Feng, Lunwen Wang, Mengbo Zhang
2019 IEEE Access  
INDEX TERMS Weakly supervised, semantic segmentation, deep learning, convolutional neural networks, image segmentation, image processing, artificial neural networks.  ...  His current research interests include the areas of image processing, deep learning, and computer vision.  ...  weakly-and semi-supervised learning tasks.  ... 
doi:10.1109/access.2019.2926972 fatcat:zabf3ovhhnfotbwntoiv7iwmlu

OpenMedIA: Open-Source Medical Image Analysis Toolbox and Benchmark under Heterogeneous AI Computing Platforms [article]

Jia-Xin Zhuang, Xiansong Huang, Yang Yang, Jiancong Chen, Yue Yu, Wei Gao, Ge Li, Jie Chen, Tong Zhang
2022 arXiv   pre-print
In this paper, we present OpenMedIA, an open-source toolbox library containing a rich set of deep learning methods for medical image analysis under heterogeneous Artificial Intelligence (AI) computing  ...  Various medical image analysis methods, including 2D/3D medical image classification, segmentation, localisation, and detection, have been included in the toolbox with PyTorch and/or MindSpore implementations  ...  We acknowledge the support provided by OpenI Community (https://git.openi. org.cn).  ... 
arXiv:2208.05616v1 fatcat:oogprf23dfhm3aws4hnpm7q3iy

Bilateral Ordinal Relevance Multi-instance Regression for Facial Action Unit Intensity Estimation

Yong Zhang, Rui Zhao, Weiming Dong, Bao-Gang Hu, Qiang Ji
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
In this paper, we propose a novel weakly supervised regression model-Bilateral Ordinal Relevance Multi-instance Regression (BORMIR), which learns a frame-level intensity estimator with weakly labeled sequences  ...  The majority of methods directly apply supervised learning techniques to AU intensity estimation while few methods exploit unlabeled samples to improve the performance.  ...  This work was also supported in part by the National Natural Science Foundation of China (NSFC) under Grant Nos. 61672520, 61573348 and 61702488.  ... 
doi:10.1109/cvpr.2018.00735 dblp:conf/cvpr/ZhangZDHJ18 fatcat:hrzvys6idngmzpyrvlsf6lmz4e

Weakly Supervised Learning of Object Segmentations from Web-Scale Video [chapter]

Glenn Hartmann, Matthias Grundmann, Judy Hoffman, David Tsai, Vivek Kwatra, Omid Madani, Sudheendra Vijayanarasimhan, Irfan Essa, James Rehg, Rahul Sukthankar
2012 Lecture Notes in Computer Science  
We formulate this problem as learning weakly supervised classifiers for a set of independent spatio-temporal segments.  ...  We propose to learn pixel-level segmentations of objects from weakly labeled (tagged) internet videos.  ...  The weakly supervised learning task bears some similarity to multi-instance learning. In the vision community, related work in this vein includes: Zha et al.'  ... 
doi:10.1007/978-3-642-33863-2_20 fatcat:4i7y7b7xafg6lbnacdizvrwzby
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