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Relevant bibliographies by topics / Semantic-Segmentation-Suite

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Academic literature on the topic 'Semantic-Segmentation-Suite'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "Semantic-Segmentation-Suite"

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Zou, Yanmei, Hongshan Yu, Zhengeng Yang, Zechuan Li, and Naveed Akhtar. "Improved MLP Point Cloud Processing with High-Dimensional Positional Encoding." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 7 (2024): 7891–99. http://dx.doi.org/10.1609/aaai.v38i7.28625.

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Multi-Layer Perceptron (MLP) models are the bedrock of contemporary point cloud processing. However, their complex network architectures obscure the source of their strength. We first develop an “abstraction and refinement” (ABS-REF) view for the neural modeling of point clouds. This view elucidates that whereas the early models focused on the ABS stage, the more recent techniques devise sophisticated REF stages to attain performance advantage in point cloud processing. We then borrow the concept of “positional encoding” from transformer literature, and propose a High-dimensional Positional En

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Acevedo Zamora, Marco Andres, and Balz S. Kamber. "Petrographic Microscopy with Ray Tracing and Segmentation from Multi-Angle Polarisation Whole-Slide Images." Minerals 13, no. 2 (2023): 156. http://dx.doi.org/10.3390/min13020156.

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‘Slide scanners’ are rapid optical microscopes equipped with automated and accurate x-y travel stages with virtual z-motion that cannot be rotated. In biomedical microscopic imaging, they are widely deployed to generate whole-slide images (WSI) of tissue samples in various modes of illumination. The availability of WSI has motivated the development of instrument-agnostic advanced image analysis software, helping drug development, pathology, and many other areas of research. Slide scanners are now being modified to enable polarised petrographic microscopy by simulating stage rotation with the a

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Ciușdel, Costin F., Alex Serban, and Tiziano Passerini. "ConceptVAE: Self-Supervised Fine-Grained Concept Disentanglement from 2D Echocardiographies." Applied Sciences 15, no. 3 (2025): 1415. https://doi.org/10.3390/app15031415.

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While traditional self-supervised learning methods improve performance and robustness across various medical tasks, they rely on single-vector embeddings that may not capture fine-grained concepts such as anatomical structures or organs. The ability to identify such concepts and their characteristics without supervision has the potential to improve pre-training methods, and enable novel applications such as fine-grained image retrieval and concept-based outlier detection. In this paper, we introduce ConceptVAE, a novel pre-training framework that detects and disentangles fine-grained concepts

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Bounouioua, Ferial, Djamila Rouag Saffidine, and Ammar Korichi. "An Enhanced HBIM Framework Integrating Advanced Technologies to strengthen the Cultural Heritage." Journal of Information Technology in Construction 30 (April 19, 2025): 570–602. https://doi.org/10.36680/j.itcon.2025.024.

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Cultural heritage (CH) conveys values through every physical element and its intrinsic essence, necessitating careful attention to its preservation and longevity. In an era increasingly shaped by technology, digital conversion is an essential component of relevant research and is consistently considered for application in future CH strategies. This study addresses the unavailability of historical, graphical and technical records, in addition to the disparities in responsibilities that hinder the recognition and management of heritage in Algeria. This highlights the capability of digital explor

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Temimi, Marouane, Mohamed Abdelkader, Achraf Tounsi, et al. "An Automated System to Monitor River Ice Conditions Using Visible Infrared Imaging Radiometer Suite Imagery." Remote Sensing 15, no. 20 (2023): 4896. http://dx.doi.org/10.3390/rs15204896.

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This study presents an innovative, automated deep learning-based technique for near real-time satellite monitoring of river ice conditions in northern watersheds of the United States and Canada. The method leverages high-resolution imagery from the VIIRS bands onboard the NOAA-20 and NPP satellites and employs the U-Net deep learning algorithm for the semantic segmentation of images under varying cloud and land surface conditions. The system autonomously generates detailed maps delineating classes such as water, land, vegetation, snow, river ice, cloud, and cloud shadow. The verification of sy

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Qianqian, Song. "Semantic-Segmentation-Suite." March 18, 2020. https://doi.org/10.5281/zenodo.3715378.

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Liu, Yajie, Pu Ge, Guodong Wang, Qingjie Liu, and Di Huang. "Multi-Grained Contrastive Learning for Text-supervised Open-vocabulary Semantic Segmentation." ACM Transactions on Multimedia Computing, Communications, and Applications, January 10, 2025. https://doi.org/10.1145/3711868.

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Learning open-vocabulary semantic segmentation (OVSS) from text supervision has recently received increasing attention for its promising potential in real-world applications. However, only with image-level supervision, it struggles to achieve dense and robust cross-modal alignment and thus limits pixel-level predictions. In this paper, we present a novel approach to this task with M ulti- G rained C ross-modal C ontrastive L earning, named MGCCL. Specifically, unlike current solutions restricted by coarse image/object-text alignment, MGCCL constructs pseudo multi-granular semantic corresponden

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Castillo-Navarro, Javiera, Bertrand Le Saux, Alexandre Boulch, Nicolas Audebert, and Sébastien Lefèvre. "Semi-supervised semantic segmentation in Earth Observation: the MiniFrance suite, dataset analysis and multi-task network study." Machine Learning, April 14, 2021. http://dx.doi.org/10.1007/s10994-020-05943-y.

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Wang, Ziming, Yujiang Liu, Yifan Duan, et al. "USTC FLICAR: A sensors fusion dataset of LiDAR-inertial-camera for heavy-duty autonomous aerial work robots." International Journal of Robotics Research, August 27, 2023. http://dx.doi.org/10.1177/02783649231195650.

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In this paper, we present the USTC FLICAR Dataset, which is dedicated to the development of simultaneous localization and mapping and precise 3D reconstruction of the workspace for heavy-duty autonomous aerial work robots. In recent years, numerous public datasets have played significant roles in the advancement of autonomous cars and unmanned aerial vehicles (UAVs). However, these two platforms differ from aerial work robots: UAVs are limited in their payload capacity, while cars are restricted to two-dimensional movements. To fill this gap, we create the “Giraffe” mapping robot based on a bu

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Zeller, Matthias, Sandhu Vardeep Singh, Benedikt Mersch, Jens Behley, Michael Heidingsfeld, and Cyrill Stachniss. "Dataset for Moving Instance Segmentation Based on RadarScenes." November 24, 2023. https://doi.org/10.5281/zenodo.10203864.

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The proposed work is based on the RadarScenes dataset by Schumann et al., and therefore, the original work has to be cited. Please visit https://radar-scenes.com for more information.<strong>Moving Instance Segmentation Benchmark</strong>The RadarScenes dataset provides individual point clouds for the four radar sensors. The measurements are from the near-range mode of the 77 GHz automotive radar sensors, which cover detections in a range of up to 100m. Two sensors are mounted at ± 85° and two sensors at ± 25° with respect to the driving direction. Since the directions in which the sensors poi

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Conference papers on the topic "Semantic-Segmentation-Suite"

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Zhao, Yifan, Zhenyu Liang, Zhichao Lu, and Ran Cheng. "A Multi-objective Optimization Benchmark Test Suite for Real-time Semantic Segmentation." In GECCO '24 Companion: Genetic and Evolutionary Computation Conference Companion. ACM, 2024. http://dx.doi.org/10.1145/3638530.3654389.

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Peñarroya, Pelayo, Pablo Hermosín, and Simone Centuori. "CNN-based Autonomous Hazard Detection: a LiDAR-less approach." In ESA 12th International Conference on Guidance Navigation and Control and 9th International Conference on Astrodynamics Tools and Techniques. ESA, 2023. http://dx.doi.org/10.5270/esa-gnc-icatt-2023-069.

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Interacting with planetary surfaces is becoming a key technology for the current space market. Missions like Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer (OSIRIS-REx), Hayabusa2, or Double Asteroid Redirection Test (DART) are clear examples that the goal when flying a spacecraft in a small-body environment is to take a sample of the surface materials to take back to Earth for further analysis or to make contact with the asteroid or comet in some way; even to the point of trying to deviate from its trajectory. Be that as it may, whether it is for sci

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