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Artykuły w czasopismach na temat „Fully- and weakly-Supervised learning”

Autor: Grafiati

Data publikacji: 9 listopada 2024

Data aktualizacji: 31 lipca 2025

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1

Sheng, Taoran, and Manfred Huber. "Reducing Label Dependency in Human Activity Recognition with Wearables: From Supervised Learning to Novel Weakly Self-Supervised Approaches." Sensors 25, no. 13 (2025): 4032. https://doi.org/10.3390/s25134032.

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Human activity recognition (HAR) using wearable sensors has advanced through various machine learning paradigms, each with inherent trade-offs between performance and labeling requirements. While fully supervised techniques achieve high accuracy, they demand extensive labeled datasets that are costly to obtain. Conversely, unsupervised methods eliminate labeling needs but often deliver suboptimal performance. This paper presents a comprehensive investigation across the supervision spectrum for wearable-based HAR, with particular focus on novel approaches that minimize labeling requirements whi

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Cuypers, Suzanna, Maarten Bassier, and Maarten Vergauwen. "Deep Learning on Construction Sites: A Case Study of Sparse Data Learning Techniques for Rebar Segmentation." Sensors 21, no. 16 (2021): 5428. http://dx.doi.org/10.3390/s21165428.

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With recent advancements in deep learning models for image interpretation, it has finally become possible to automate construction site monitoring processes that rely on remote sensing. However, the major drawback of these models is their dependency on large datasets of training images labeled at pixel level, which have to be produced manually by skilled personnel. To alleviate the need for training data, this study evaluates weakly- and semi-supervised semantic segmentation models for construction site imagery to efficiently automate monitoring tasks. As a case study, we compare fully-, weakl

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Wang, Ning, Jiajun Deng, and Mingbo Jia. "Cycle-Consistency Learning for Captioning and Grounding." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 6 (2024): 5535–43. http://dx.doi.org/10.1609/aaai.v38i6.28363.

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We present that visual grounding and image captioning, which perform as two mutually inverse processes, can be bridged together for collaborative training by careful designs. By consolidating this idea, we introduce CyCo, a cyclic-consistent learning framework to ameliorate the independent training pipelines of visual grounding and image captioning. The proposed framework (1) allows the semi-weakly supervised training of visual grounding; (2) improves the performance of fully supervised visual grounding; (3) yields a general captioning model that can describe arbitrary image regions. Extensive

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4

Wang, Guangyao. "A Study of Object Detection Based on Weakly Supervised Learning." International Journal of Computer Science and Information Technology 2, no. 1 (2024): 476–78. http://dx.doi.org/10.62051/ijcsit.v2n1.50.

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Object detection is one of the important research contents in the field of computer vision. At present, the classical object detection methods can be divided into two categories: fully supervised-based target detection and weakly supervised-based target detection. Since the fully supervised object detection model requires a large number of training data with category labels and target bounding boxes, and such labeled data is difficult to obtain, it is of great significance to explore the weakly supervised object detection method that only needs category label data.

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Xu, Xinyan. "Weakly Supervised Semantic Segmentation with Deep Learning." Applied and Computational Engineering 166, no. 1 (2025): 44–49. https://doi.org/10.54254/2755-2721/2025.tj23839.

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Weakly supervised semantic segmentation aims to achieve segmentation performance comparable to fully supervised methods through low-cost annotation forms such as image level labels or bounding boxes. This article systematically reviews two types of weakly supervised learning methods based on image level labels and bounding box supervision. For image level label supervision, mainstream methods generate initial seed regions through Class Activation Mapping (CAM) and use pixel correlation expansion or iterative optimization strategies (such as erasure and adversarial training) to solve the proble

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Moraes, Daniel, Manuel L. Campagnolo, and Mário Caetano. "A Weakly Supervised and Self-Supervised Learning Approach for Semantic Segmentation of Land Cover in Satellite Images with National Forest Inventory Data." Remote Sensing 17, no. 4 (2025): 711. https://doi.org/10.3390/rs17040711.

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National Forest Inventories (NFIs) provide valuable land cover (LC) information but often lack spatial continuity and an adequate update frequency. Satellite-based remote sensing offers a viable alternative, employing machine learning to extract thematic data. State-of-the-art methods such as convolutional neural networks rely on fully pixel-level annotated images, which are difficult to obtain. Although reference LC datasets have been widely used to derive annotations, NFIs consist of point-based data, providing only sparse annotations. Weakly supervised and self-supervised learning approache

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Adke, Shrinidhi, Changying Li, Khaled M. Rasheed, and Frederick W. Maier. "Supervised and Weakly Supervised Deep Learning for Segmentation and Counting of Cotton Bolls Using Proximal Imagery." Sensors 22, no. 10 (2022): 3688. http://dx.doi.org/10.3390/s22103688.

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The total boll count from a plant is one of the most important phenotypic traits for cotton breeding and is also an important factor for growers to estimate the final yield. With the recent advances in deep learning, many supervised learning approaches have been implemented to perform phenotypic trait measurement from images for various crops, but few studies have been conducted to count cotton bolls from field images. Supervised learning models require a vast number of annotated images for training, which has become a bottleneck for machine learning model development. The goal of this study i

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Dorent, Reuben, Roya Khajavi, Tagwa Idris, et al. "LNQ 2023 challenge: Benchmark of weakly-supervised techniques for mediastinal lymph node quantification." Machine Learning for Biomedical Imaging 3, MICCAI 2023 LNQ challenge (2025): 1–15. https://doi.org/10.59275/j.melba.2025-d482.

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Accurate assessment of lymph node size in 3D CT scans is crucial for cancer staging, therapeutic management, and monitoring treatment response. Existing state-of-the-art segmentation frameworks in medical imaging often rely on fully annotated datasets. However, for lymph node segmentation, these datasets are typically small due to the extensive time and expertise required to annotate the numerous lymph nodes in 3D CT scans. Weakly-supervised learning, which leverages incomplete or noisy annotations, has recently gained interest in the medical imaging community as a potential solution. Despite

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9

Ni, Ansong, Pengcheng Yin, and Graham Neubig. "Merging Weak and Active Supervision for Semantic Parsing." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 8536–43. http://dx.doi.org/10.1609/aaai.v34i05.6375.

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A semantic parser maps natural language commands (NLs) from the users to executable meaning representations (MRs), which are later executed in certain environment to obtain user-desired results. The fully-supervised training of such parser requires NL/MR pairs, annotated by domain experts, which makes them expensive to collect. However, weakly-supervised semantic parsers are learnt only from pairs of NL and expected execution results, leaving the MRs latent. While weak supervision is cheaper to acquire, learning from this input poses difficulties. It demands that parsers search a large space w

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Colin, Aurélien, Ronan Fablet, Pierre Tandeo, et al. "Semantic Segmentation of Metoceanic Processes Using SAR Observations and Deep Learning." Remote Sensing 14, no. 4 (2022): 851. http://dx.doi.org/10.3390/rs14040851.

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Through the Synthetic Aperture Radar (SAR) embarked on the satellites Sentinel-1A and Sentinel-1B of the Copernicus program, a large quantity of observations is routinely acquired over the oceans. A wide range of features from both oceanic (e.g., biological slicks, icebergs, etc.) and meteorologic origin (e.g., rain cells, wind streaks, etc.) are distinguishable on these acquisitions. This paper studies the semantic segmentation of ten metoceanic processes either in the context of a large quantity of image-level groundtruths (i.e., weakly-supervised framework) or of scarce pixel-level groundtr

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11

Cai, Tingting, Hongping Yan, Kun Ding, Yan Zhang, and Yueyue Zhou. "WSPolyp-SAM: Weakly Supervised and Self-Guided Fine-Tuning of SAM for Colonoscopy Polyp Segmentation." Applied Sciences 14, no. 12 (2024): 5007. http://dx.doi.org/10.3390/app14125007.

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Ensuring precise segmentation of colorectal polyps holds critical importance in the early diagnosis and treatment of colorectal cancer. Nevertheless, existing deep learning-based segmentation methods are fully supervised, requiring extensive, precise, manual pixel-level annotation data, which leads to high annotation costs. Additionally, it remains challenging to train large-scale segmentation models when confronted with limited colonoscopy data. To address these issues, we introduce the general segmentation foundation model—the Segment Anything Model (SAM)—into the field of medical image segm

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12

Hong, Yining, Qing Li, Daniel Ciao, Siyuan Huang, and Song-Chun Zhu. "Learning by Fixing: Solving Math Word Problems with Weak Supervision." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 6 (2021): 4959–67. http://dx.doi.org/10.1609/aaai.v35i6.16629.

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Previous neural solvers of math word problems (MWPs) are learned with full supervision and fail to generate diverse solutions. In this paper, we address this issue by introducing a weakly-supervised paradigm for learning MWPs. Our method only requires the annotations of the final answers and can generate various solutions for a single problem. To boost weakly-supervised learning, we propose a novel learning-by-fixing (LBF) framework, which corrects the misperceptions of the neural network via symbolic reasoning. Specifically, for an incorrect solution tree generated by the neural network, the

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13

Chen, Shaolong, and Zhiyong Zhang. "A Semi-Automatic Magnetic Resonance Imaging Annotation Algorithm Based on Semi-Weakly Supervised Learning." Sensors 24, no. 12 (2024): 3893. http://dx.doi.org/10.3390/s24123893.

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The annotation of magnetic resonance imaging (MRI) images plays an important role in deep learning-based MRI segmentation tasks. Semi-automatic annotation algorithms are helpful for improving the efficiency and reducing the difficulty of MRI image annotation. However, the existing semi-automatic annotation algorithms based on deep learning have poor pre-annotation performance in the case of insufficient segmentation labels. In this paper, we propose a semi-automatic MRI annotation algorithm based on semi-weakly supervised learning. In order to achieve a better pre-annotation performance in the

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14

Zhang, Yachao, Zonghao Li, Yuan Xie, Yanyun Qu, Cuihua Li, and Tao Mei. "Weakly Supervised Semantic Segmentation for Large-Scale Point Cloud." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 4 (2021): 3421–29. http://dx.doi.org/10.1609/aaai.v35i4.16455.

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Existing methods for large-scale point cloud semantic segmentation require expensive, tedious and error-prone manual point-wise annotation. Intuitively, weakly supervised training is a direct solution to reduce the labeling costs. However, for weakly supervised large-scale point cloud semantic segmentation, too few annotations will inevitably lead to ineffective learning of network. We propose an effective weakly supervised method containing two components to solve the above problem. Firstly, we construct a pretext task, \textit{i.e.,} point cloud colorization, with a self-supervised training

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15

Cherikbayeva, L. Ch, N. K. Mukazhanov, Z. Alibiyeva, S. A. Adilzhanova, G. A. Tyulepberdinova, and M. Zh Sakypbekova. "SOLUTION TO THE PROBLEM WEAKLY CONTROLLED REGRESSION USING COASSOCIATION MATRIX AND REGULARIZATION." Herald of the Kazakh-British technical university 21, no. 2 (2024): 83–94. http://dx.doi.org/10.55452/1998-6688-2024-21-2-83-94.

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Currently, the theory and methods of machine learning (ML) are rapidly developing and are increasingly used in various fields of science and technology, in particular in manufacturing, education and medicine. Weakly supervised learning is a subset of machine learning research that aims to develop models and methods for analyzing various types of information. When formulating a weakly supervised learning problem, it is assumed that some objects in the model are not defined correctly. This inaccuracy can be understood in different ways. Weakly supervised learning is a type of machine learning me

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16

Feng, Jiahao, Ce Li, and Jin Wang. "CAM-TMIL: A Weakly-Supervised Segmentation Framework for Histopathology based on CAMs and MIL." Journal of Physics: Conference Series 2547, no. 1 (2023): 012014. http://dx.doi.org/10.1088/1742-6596/2547/1/012014.

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Abstract Semantic segmentation plays a significant role in histopathology by assisting pathologists in diagnosis. Although fully-supervised learning achieves excellent success on segmentation for histopathological images, it costs pathologists and experts great efforts on pixel-level annotation in the meantime. Thus, to reduce the annotation workload, we proposed a weakly-supervised learning framework called CAM-TMIL, which assembles methods based on class activation maps (CAMs) and multiple instance learning (MIL) to perform segmentation with image-level labels. By leveraging the MIL method,

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17

Qian, Xiaoliang, Chenyang Lin, Zhiwu Chen, and Wei Wang. "SAM-Induced Pseudo Fully Supervised Learning for Weakly Supervised Object Detection in Remote Sensing Images." Remote Sensing 16, no. 9 (2024): 1532. http://dx.doi.org/10.3390/rs16091532.

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Weakly supervised object detection (WSOD) in remote sensing images (RSIs) aims to detect high-value targets by solely utilizing image-level category labels; however, two problems have not been well addressed by existing methods. Firstly, the seed instances (SIs) are mined solely relying on the category score (CS) of each proposal, which is inclined to concentrate on the most salient parts of the object; furthermore, they are unreliable because the robustness of the CS is not sufficient due to the fact that the inter-category similarity and intra-category diversity are more serious in RSIs. Sec

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18

Chen, Jie, Fen He, Yi Zhang, Geng Sun, and Min Deng. "SPMF-Net: Weakly Supervised Building Segmentation by Combining Superpixel Pooling and Multi-Scale Feature Fusion." Remote Sensing 12, no. 6 (2020): 1049. http://dx.doi.org/10.3390/rs12061049.

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The lack of pixel-level labeling limits the practicality of deep learning-based building semantic segmentation. Weakly supervised semantic segmentation based on image-level labeling results in incomplete object regions and missing boundary information. This paper proposes a weakly supervised semantic segmentation method for building detection. The proposed method takes the image-level label as supervision information in a classification network that combines superpixel pooling and multi-scale feature fusion structures. The main advantage of the proposed strategy is its ability to improve the i

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Wu, Zhenyu, Lin Wang, Wei Wang, et al. "Pixel Is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detection." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 3 (2023): 2883–91. http://dx.doi.org/10.1609/aaai.v37i3.25390.

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Although weakly-supervised techniques can reduce the labeling effort, it is unclear whether a saliency model trained with weakly-supervised data (e.g., point annotation) can achieve the equivalent performance of its fully-supervised version. This paper attempts to answer this unexplored question by proving a hypothesis: there is a point-labeled dataset where saliency models trained on it can achieve equivalent performance when trained on the densely annotated dataset. To prove this conjecture, we proposed a novel yet effective adversarial trajectory-ensemble active learning (ATAL). Our contrib

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Liu, Xiangquan, and Xiaoming Huang. "Weakly supervised salient object detection via bounding-box annotation and SAM model." Electronic Research Archive 32, no. 3 (2024): 1624–45. http://dx.doi.org/10.3934/era.2024074.

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<abstract><p>Salient object detection (SOD) aims to detect the most attractive region in an image. Fully supervised SOD based on deep learning usually needs a large amount of data with human annotation. Researchers have gradually focused on the SOD task using weakly supervised annotation such as category, scribble, and bounding-box, while these existing weakly supervised methods achieve limited performance and demonstrate a huge performance gap with fully supervised methods. In this work, we proposed one novel two-stage weakly supervised method based on bounding-box annotation and

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Božič, Jakob, Domen Tabernik, and Danijel Skočaj. "Mixed supervision for surface-defect detection: From weakly to fully supervised learning." Computers in Industry 129 (August 2021): 103459. http://dx.doi.org/10.1016/j.compind.2021.103459.

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Zhang, Xinyue, Jianfeng Wang, Jinqiao Wei, Xinyu Yuan, and Ming Wu. "A Review of Non-Fully Supervised Deep Learning for Medical Image Segmentation." Information 16, no. 6 (2025): 433. https://doi.org/10.3390/info16060433.

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Medical image segmentation, a critical task in medical image analysis, aims to precisely delineate regions of interest (ROIs) such as organs, lesions, and cells, and is crucial for applications including computer-aided diagnosis, surgical planning, radiation therapy, and pathological analysis. While fully supervised deep learning methods have demonstrated remarkable performance in this domain, their reliance on large-scale, pixel-level annotated datasets—a significant label scarcity challenge—severely hinders their widespread deployment in clinical settings. Addressing this limitation, this re

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Fu, Kun, Wanxuan Lu, Wenhui Diao, et al. "WSF-NET: Weakly Supervised Feature-Fusion Network for Binary Segmentation in Remote Sensing Image." Remote Sensing 10, no. 12 (2018): 1970. http://dx.doi.org/10.3390/rs10121970.

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Binary segmentation in remote sensing aims to obtain binary prediction mask classifying each pixel in the given image. Deep learning methods have shown outstanding performance in this task. These existing methods in fully supervised manner need massive high-quality datasets with manual pixel-level annotations. However, the annotations are generally expensive and sometimes unreliable. Recently, using only image-level annotations, weakly supervised methods have proven to be effective in natural imagery, which significantly reduce the dependence on manual fine labeling. In this paper, we review e

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Ge, Yongtao, Qiang Zhou, Xinlong Wang, Chunhua Shen, Zhibin Wang, and Hao Li. "Point-Teaching: Weakly Semi-supervised Object Detection with Point Annotations." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 1 (2023): 667–75. http://dx.doi.org/10.1609/aaai.v37i1.25143.

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Point annotations are considerably more time-efficient than bounding box annotations. However, how to use cheap point annotations to boost the performance of semi-supervised object detection is still an open question. In this work, we present Point-Teaching, a weakly- and semi-supervised object detection framework to fully utilize the point annotations. Specifically, we propose a Hungarian-based point-matching method to generate pseudo labels for point-annotated images. We further propose multiple instance learning (MIL) approaches at the level of images and points to supervise the object dete

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Watanabe, Takumi, Hiroki Takahashi, Yusuke Iwasawa, Yutaka Matsuo, and Ikuko Eguchi Yairi. "Weakly Supervised Learning for Evaluating Road Surface Condition from Wheelchair Driving Data." Information 11, no. 1 (2019): 2. http://dx.doi.org/10.3390/info11010002.

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Providing accessibility information about sidewalks for people with difficulties with moving is an important social issue. We previously proposed a fully supervised machine learning approach for providing accessibility information by estimating road surface conditions using wheelchair accelerometer data with manually annotated road surface condition labels. However, manually annotating road surface condition labels is expensive and impractical for extensive data. This paper proposes and evaluates a novel method for estimating road surface conditions without human annotation by applying weakly

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Roth, Holger R., Dong Yang, Ziyue Xu, Xiaosong Wang, and Daguang Xu. "Going to Extremes: Weakly Supervised Medical Image Segmentation." Machine Learning and Knowledge Extraction 3, no. 2 (2021): 507–24. http://dx.doi.org/10.3390/make3020026.

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Medical image annotation is a major hurdle for developing precise and robust machine-learning models. Annotation is expensive, time-consuming, and often requires expert knowledge, particularly in the medical field. Here, we suggest using minimal user interaction in the form of extreme point clicks to train a segmentation model which, in effect, can be used to speed up medical image annotation. An initial segmentation is generated based on the extreme points using the random walker algorithm. This initial segmentation is then used as a noisy supervision signal to train a fully convolutional net

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Nartey, Obed Tettey, Guowu Yang, Sarpong Kwadwo Asare, Jinzhao Wu, and Lady Nadia Frempong. "Robust Semi-Supervised Traffic Sign Recognition via Self-Training and Weakly-Supervised Learning." Sensors 20, no. 9 (2020): 2684. http://dx.doi.org/10.3390/s20092684.

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Traffic sign recognition is a classification problem that poses challenges for computer vision and machine learning algorithms. Although both computer vision and machine learning techniques have constantly been improved to solve this problem, the sudden rise in the number of unlabeled traffic signs has become even more challenging. Large data collation and labeling are tedious and expensive tasks that demand much time, expert knowledge, and fiscal resources to satisfy the hunger of deep neural networks. Aside from that, the problem of having unbalanced data also poses a greater challenge to co

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Baek, Kyungjune, Minhyun Lee, and Hyunjung Shim. "PsyNet: Self-Supervised Approach to Object Localization Using Point Symmetric Transformation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 10451–59. http://dx.doi.org/10.1609/aaai.v34i07.6615.

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Existing co-localization techniques significantly lose performance over weakly or fully supervised methods in accuracy and inference time. In this paper, we overcome common drawbacks of co-localization techniques by utilizing self-supervised learning approach. The major technical contributions of the proposed method are two-fold. 1) We devise a new geometric transformation, namely point symmetric transformation and utilize its parameters as an artificial label for self-supervised learning. This new transformation can also play the role of region-drop based regularization. 2) We suggest a heat

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Wang, Lukang, Min Zhang, Xu Gao, and Wenzhong Shi. "Advances and Challenges in Deep Learning-Based Change Detection for Remote Sensing Images: A Review through Various Learning Paradigms." Remote Sensing 16, no. 5 (2024): 804. http://dx.doi.org/10.3390/rs16050804.

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Change detection (CD) in remote sensing (RS) imagery is a pivotal method for detecting changes in the Earth’s surface, finding wide applications in urban planning, disaster management, and national security. Recently, deep learning (DL) has experienced explosive growth and, with its superior capabilities in feature learning and pattern recognition, it has introduced innovative approaches to CD. This review explores the latest techniques, applications, and challenges in DL-based CD, examining them through the lens of various learning paradigms, including fully supervised, semi-supervised, weakl

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Zhang, Quan, Yuxin Qi, Xi Tang, et al. "Rethinking Pseudo-Label Guided Learning for Weakly Supervised Temporal Action Localization from the Perspective of Noise Correction." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 10 (2025): 10085–93. https://doi.org/10.1609/aaai.v39i10.33094.

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Pseudo-label learning methods have been widely applied in weakly-supervised temporal action localization. Existing works directly utilize weakly-supervised base model to generate instance-level pseudo-labels for training the fully-supervised detection head. We argue that the noise in pseudo-labels would interfere with the learning of fully-supervised detection head, leading to significant performance leakage. Issues with noisy labels include:(1) inaccurate boundary localization; (2) undetected short action clips; (3) multiple adjacent segments incorrectly detected as one segment. To target the

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Hoang, Nhat M., Kehong Gong, Chuan Guo, and Michael Bi Mi. "MotionMix: Weakly-Supervised Diffusion for Controllable Motion Generation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 3 (2024): 2157–65. http://dx.doi.org/10.1609/aaai.v38i3.27988.

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Controllable generation of 3D human motions becomes an important topic as the world embraces digital transformation. Existing works, though making promising progress with the advent of diffusion models, heavily rely on meticulously captured and annotated (e.g., text) high-quality motion corpus, a resource-intensive endeavor in the real world. This motivates our proposed MotionMix, a simple yet effective weakly-supervised diffusion model that leverages both noisy and unannotated motion sequences. Specifically, we separate the denoising objectives of a diffusion model into two stages: obtaining

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Lin, Jianghang, Yunhang Shen, Bingquan Wang, Shaohui Lin, Ke Li, and Liujuan Cao. "Weakly Supervised Open-Vocabulary Object Detection." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 4 (2024): 3404–12. http://dx.doi.org/10.1609/aaai.v38i4.28127.

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Despite weakly supervised object detection (WSOD) being a promising step toward evading strong instance-level annotations, its capability is confined to closed-set categories within a single training dataset. In this paper, we propose a novel weakly supervised open-vocabulary object detection framework, namely WSOVOD, to extend traditional WSOD to detect novel concepts and utilize diverse datasets with only image-level annotations. To achieve this, we explore three vital strategies, including dataset-level feature adaptation, image-level salient object localization, and region-level vision-lan

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Qian, Rui, Yunchao Wei, Honghui Shi, Jiachen Li, Jiaying Liu, and Thomas Huang. "Weakly Supervised Scene Parsing with Point-Based Distance Metric Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8843–50. http://dx.doi.org/10.1609/aaai.v33i01.33018843.

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Semantic scene parsing is suffering from the fact that pixellevel annotations are hard to be collected. To tackle this issue, we propose a Point-based Distance Metric Learning (PDML) in this paper. PDML does not require dense annotated masks and only leverages several labeled points that are much easier to obtain to guide the training process. Concretely, we leverage semantic relationship among the annotated points by encouraging the feature representations of the intra- and intercategory points to keep consistent, i.e. points within the same category should have more similar feature represent

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Sebai, Meriem, Xinggang Wang, and Tianjiang Wang. "MaskMitosis: a deep learning framework for fully supervised, weakly supervised, and unsupervised mitosis detection in histopathology images." Medical & Biological Engineering & Computing 58, no. 7 (2020): 1603–23. http://dx.doi.org/10.1007/s11517-020-02175-z.

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Krishnamurthy, Jayant, and Thomas Kollar. "Jointly Learning to Parse and Perceive: Connecting Natural Language to the Physical World." Transactions of the Association for Computational Linguistics 1 (December 2013): 193–206. http://dx.doi.org/10.1162/tacl_a_00220.

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This paper introduces Logical Semantics with Perception (LSP), a model for grounded language acquisition that learns to map natural language statements to their referents in a physical environment. For example, given an image, LSP can map the statement “blue mug on the table” to the set of image segments showing blue mugs on tables. LSP learns physical representations for both categorical (“blue,” “mug”) and relational (“on”) language, and also learns to compose these representations to produce the referents of entire statements. We further introduce a weakly supervised training procedure that

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Xie, Fei, Panpan Zhang, Tao Jiang, et al. "Lesion Segmentation Framework Based on Convolutional Neural Networks with Dual Attention Mechanism." Electronics 10, no. 24 (2021): 3103. http://dx.doi.org/10.3390/electronics10243103.

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Computational intelligence has been widely used in medical information processing. The deep learning methods, especially, have many successful applications in medical image analysis. In this paper, we proposed an end-to-end medical lesion segmentation framework based on convolutional neural networks with a dual attention mechanism, which integrates both fully and weakly supervised segmentation. The weakly supervised segmentation module achieves accurate lesion segmentation by using bounding-box labels of lesion areas, which solves the problem of the high cost of pixel-level labels with lesions

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Zhang, Wei, Ping Tang, Thomas Corpetti, and Lijun Zhao. "WTS: A Weakly towards Strongly Supervised Learning Framework for Remote Sensing Land Cover Classification Using Segmentation Models." Remote Sensing 13, no. 3 (2021): 394. http://dx.doi.org/10.3390/rs13030394.

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Land cover classification is one of the most fundamental tasks in the field of remote sensing. In recent years, fully supervised fully convolutional network (FCN)-based semantic segmentation models have achieved state-of-the-art performance in the semantic segmentation task. However, creating pixel-level annotations is prohibitively expensive and laborious, especially when dealing with remote sensing images. Weakly supervised learning methods from weakly labeled annotations can overcome this difficulty to some extent and achieve impressive segmentation results, but results are limited in accur

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Wang, Yaodong, Lili Yue, and Maoqing Li. "Cascaded Searching Reinforcement Learning Agent for Proposal-Free Weakly-Supervised Phrase Comprehension." Electronics 13, no. 5 (2024): 898. http://dx.doi.org/10.3390/electronics13050898.

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Phrase comprehension (PC) aims to locate a specific object in an image according to a given linguistic query. The existing PC methods work in either a fully supervised or proposal-based weakly supervised manner, which rely explicitly or implicitly on expensive region annotations. In order to completely remove the dependence on the supervised region information, this paper proposes to address PC in a proposal-free weakly supervised training paradigm. To this end, we developed a novel cascaded searching reinforcement learning agent (CSRLA). Concretely, we first leveraged a visual language pre-tr

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Wang, Sherrie, William Chen, Sang Michael Xie, George Azzari, and David B. Lobell. "Weakly Supervised Deep Learning for Segmentation of Remote Sensing Imagery." Remote Sensing 12, no. 2 (2020): 207. http://dx.doi.org/10.3390/rs12020207.

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Accurate automated segmentation of remote sensing data could benefit applications from land cover mapping and agricultural monitoring to urban development surveyal and disaster damage assessment. While convolutional neural networks (CNNs) achieve state-of-the-art accuracy when segmenting natural images with huge labeled datasets, their successful translation to remote sensing tasks has been limited by low quantities of ground truth labels, especially fully segmented ones, in the remote sensing domain. In this work, we perform cropland segmentation using two types of labels commonly found in re

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Zhao, Lulu, Yanan Zhao, Ting Liu, and Hanbing Deng. "A Weakly Supervised Semantic Segmentation Model of Maize Seedlings and Weed Images Based on Scrawl Labels." Sensors 23, no. 24 (2023): 9846. http://dx.doi.org/10.3390/s23249846.

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The task of semantic segmentation of maize and weed images using fully supervised deep learning models requires a large number of pixel-level mask labels, and the complex morphology of the maize and weeds themselves can further increase the cost of image annotation. To solve this problem, we proposed a Scrawl Label-based Weakly Supervised Semantic Segmentation Network (SL-Net). SL-Net consists of a pseudo label generation module, encoder, and decoder. The pseudo label generation module converts scrawl labels into pseudo labels that replace manual labels that are involved in network training, i

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Ouassit, Youssef, Reda Moulouki, Mohammed Yassine El Ghoumari, Mohamed Azzouazi, and Soufiane Ardchir. "Liver Segmentation: A Weakly End-to-End Supervised Model." International Journal of Online and Biomedical Engineering (iJOE) 16, no. 09 (2020): 77. http://dx.doi.org/10.3991/ijoe.v16i09.15159.

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Liver segmentation in CT images has multiple clinical applications and is expanding in scope. Clinicians can employ segmentation for pathological diagnosis of liver disease, surgical planning, visualization and volumetric assessment to select the appropriate treatment. However, segmentation of the liver is still a challenging task due to the low contrast in medical images, tissue similarity with neighbor abdominal organs and high scale and shape variability. Recently, deep learning models are the state of art in many natural images processing tasks such as detection, classification, and segmen

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Yan, Qing, Tao Sun, Jingjing Zhang, and Lina Xun. "Visibility Estimation Based on Weakly Supervised Learning under Discrete Label Distribution." Sensors 23, no. 23 (2023): 9390. http://dx.doi.org/10.3390/s23239390.

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This paper proposes an end-to-end neural network model that fully utilizes the characteristic of uneven fog distribution to estimate visibility in fog images. Firstly, we transform the original single labels into discrete label distributions and introduce discrete label distribution learning on top of the existing classification networks to learn the difference in visibility information among different regions of an image. Then, we employ the bilinear attention pooling module to find the farthest visible region of fog in the image, which is incorporated into an attention-based branch. Finally,

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Zhang, Shuyuan, Hongli Xu, Xiaoran Zhu, and Lipeng Xie. "Automatic Crack Detection Using Weakly Supervised Semantic Segmentation Network and Mixed-Label Training Strategy." Foundations of Computing and Decision Sciences 49, no. 1 (2024): 95–118. http://dx.doi.org/10.2478/fcds-2024-0007.

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Abstract Automatic crack detection in construction facilities is a challenging yet crucial task. However, existing deep learning (DL)-based semantic segmentation methods for this field are based on fully supervised learning models and pixel-level manual annotation, which are time-consuming and labor-intensive. To solve this problem, this paper proposes a novel crack semantic segmentation network using weakly supervised approach and mixed-label training strategy. Firstly, an image patch-level classifier of crack is trained to generate a coarse localization map for automatic pseudo-labeling of c

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Mostafa, Iman, Marwa Gamal, Rehab F. Abdel-Kader, and Khaled Abd El Salam. "ABC-WSVAD: Swarm Optimization for Weakly-Supervised Video Anomaly Detection." Inteligencia Artificial 28, no. 75 (2025): 281–97. https://doi.org/10.4114/intartif.vol28iss75pp281-297.

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Nowadays, Video Anomaly Detection (VAD) has undergone a significant transformation due to advancements in Deep Learning (DL) and Computer Vision (CV). VAD holds substantial importance in various applications, particularly security, given the increasing spectrum of criminal activities. Conventional supervised anomaly detection techniques heavily rely on meticulously labeled data, which is time-consuming to annotate and is the basis for training anomaly classifiers. However, assembling extensive annotated datasets for VAD poses challenges due to the hard and time-consuming of the task. Weakly Su

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Chen, Hao, Shuang Peng, Chun Du, Jun Li, and Songbing Wu. "SW-GAN: Road Extraction from Remote Sensing Imagery Using Semi-Weakly Supervised Adversarial Learning." Remote Sensing 14, no. 17 (2022): 4145. http://dx.doi.org/10.3390/rs14174145.

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Road networks play a fundamental role in our daily life. It is of importance to extract the road structure in a timely and precise manner with the rapid evolution of urban road structure. Recently, road network extraction using deep learning has become an effective and popular method. The main shortcoming of the road extraction using deep learning methods lies in the fact that there is a need for a large amount of training datasets. Additionally, the datasets need to be elaborately annotated, which is usually labor-intensive and time-consuming; thus, lots of weak annotations (such as the cente

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Zheng, Shida, Chenshu Chen, Xi Yang, and Wenming Tan. "MaskBooster: End-to-End Self-Training for Sparsely Supervised Instance Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 3 (2023): 3696–704. http://dx.doi.org/10.1609/aaai.v37i3.25481.

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The present paper introduces sparsely supervised instance segmentation, with the datasets being fully annotated bounding boxes and sparsely annotated masks. A direct solution to this task is self-training, which is not fully explored for instance segmentation yet. In this paper, we propose MaskBooster for sparsely supervised instance segmentation (SpSIS) with comprehensive usage of pseudo masks. MaskBooster is featured with (1) dynamic and progressive pseudo masks from an online updating teacher model, (2) refining binary pseudo masks with the help of bounding box prior, (3) learning inter-cla

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Qiang, Zhuang, Jingmin Shi, and Fanhuai Shi. "Phenotype Tracking of Leafy Greens Based on Weakly Supervised Instance Segmentation and Data Association." Agronomy 12, no. 7 (2022): 1567. http://dx.doi.org/10.3390/agronomy12071567.

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Phenotype analysis of leafy green vegetables in planting environment is the key technology of precision agriculture. In this paper, deep convolutional neural network is employed to conduct instance segmentation of leafy greens by weakly supervised learning based on box-level annotations and Excess Green (ExG) color similarity. Then, weeds are filtered based on area threshold, K-means clustering and time context constraint. Thirdly, leafy greens tracking is achieved by bipartite graph matching based on mask IoU measure. Under the framework of phenotype tracking, some time-context-dependent phen

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Mo, Shaoyi, Yufeng Shi, Qi Yuan, and Mingyue Li. "A Survey of Deep Learning Road Extraction Algorithms Using High-Resolution Remote Sensing Images." Sensors 24, no. 5 (2024): 1708. http://dx.doi.org/10.3390/s24051708.

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Roads are the fundamental elements of transportation, connecting cities and rural areas, as well as people’s lives and work. They play a significant role in various areas such as map updates, economic development, tourism, and disaster management. The automatic extraction of road features from high-resolution remote sensing images has always been a hot and challenging topic in the field of remote sensing, and deep learning network models are widely used to extract roads from remote sensing images in recent years. In light of this, this paper systematically reviews and summarizes the deep-learn

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Liu, Yiqing, Qiming He, Hufei Duan, Huijuan Shi, Anjia Han, and Yonghong He. "Using Sparse Patch Annotation for Tumor Segmentation in Histopathological Images." Sensors 22, no. 16 (2022): 6053. http://dx.doi.org/10.3390/s22166053.

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Tumor segmentation is a fundamental task in histopathological image analysis. Creating accurate pixel-wise annotations for such segmentation tasks in a fully-supervised training framework requires significant effort. To reduce the burden of manual annotation, we propose a novel weakly supervised segmentation framework based on sparse patch annotation, i.e., only small portions of patches in an image are labeled as ‘tumor’ or ‘normal’. The framework consists of a patch-wise segmentation model called PSeger, and an innovative semi-supervised algorithm. PSeger has two branches for patch classific

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Fan, Yifei. "Image semantic segmentation using deep learning technique." Applied and Computational Engineering 4, no. 1 (2023): 810–17. http://dx.doi.org/10.54254/2755-2721/4/2023439.

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With the deepening research on image understanding in many application fields, including auto drive system, unmanned aerial vehicle (UAV) landing point judgment, virtual reality wearable devices, etc., computer vision and machine learning researchers are paying more and more attention to image semantic segmentation (ISS). In this paper, according to the different region generation algorithms, the regional classification image semantic segmentation methods are classified into the candidate region method and the segmentation mask method, according to different learning methods, the image semanti

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