Semantic segmentation survey. (), semantic segmentation Wu et al.
Semantic segmentation survey In the past five years, various papers came up with different objective loss functions used in different cases such as biased data, sparse segmentation, etc. This paper presents the survey centered on semantic With this survey we review the state-of-the-art research in the area of unsupervised domain adaptation for semantic segmentation for the synthetic-to-real domain shift. This technique has numerous real-world applications, such as autonomous driving Oct 9, 2023 · The structure, data set and experimental settings of the U-Net model, including the basic structure, characteristics and advantages of the U-Net model, and its application in semantic segmentation of colorectal polyp images are introduced. org Abstract—Image Segmentation has been an active field of research as it has a wide range of applications, ranging from automated disease detection to self driving cars. Over the past decade, deep learning-based methods have made remarkable strides in this area. 1(f-h)), which are further divided into eight sub-fields. nwpu. Specifically, humans can perform image As an important research topic in recent years, semantic segmentation has been widely applied to image understanding problems in various fields. Jun 17, 2022 · Semantic segmentation is the problem of assigning a class label to every pixel in an image, and is an important component of an autonomous vehicle vision stack for facilitating scene understanding and object detection. We hope that this survey helps accelerate progress in this field. A survey of loss functions for semantic segmentation Shruti Jadon IEEE Member shrutijadon@ieee. We will add the Aug 24, 2018 · A Survey on Continual/Incremental Semantic Segmentation - YBIO/SurveyCSS. However, the acquisition of pixel-level labels in fully supervised learning is time consuming and Jun 17, 2022 · A thorough look at the works that aim to address semantic segmentation misalignment with more compact and efficient models capable of deployment on low-memory embedded systems while meeting the constraint of real-time inference. This survey is an May 5, 2023 · In this survey, we discuss some of the different ViT architectures that can be used for semantic segmentation and how their evolution managed the above-stated challenge. Recently published approaches with convolutional neural networks are Abstract: Semantic image segmentation, the process of classifying each pixel in an image into a particular class, plays an important role in many visual understanding systems. This project website contains both an up-to-date leaderboard and interactive plots to analyze the progress and methods of UDA for semantic segmentation. Semantic image segmentation, the process of classifying each pixel in an image into a particular class, plays an important role in many visual understanding systems. However, it does not hold a broad and comprehensive understanding of DG in semantic segmentation. Recently convolutional neural network is a widely used model in image segmentation, target recognition and scene Mar 4, 2024 · The pseudo-label method is a well-known technique in the semi-supervised learning field that first appeared in Lee and others and has gained popularity in recent computer vision research, including domain adaptation Li et al. In the related literature, the taxonomy scheme used for the classification of the 3DSS deep learning methods is ambiguous. Summary of Contents Nov 24, 2017 · During the long history of computer vision, one of the grand challenges has been semantic segmentation which is the ability to segment an unknown image into different parts and objects (e. Deep learning approaches have lately emerged as the preferred method for 3D segmentation problems as a result of their outstanding performance in 2D computer vision. Metrics and datasets for the evaluation of segmentation algorithms and traditional approaches May 1, 2021 · Semantic image segmentation is a fundamental task in computer vision that assigns a label to each pixel, a. For every single image, patches of the image called windows are extracted and those windows are classified. Various semantic segmentation surveys already exist such as the works by Zhu et al. edu. Feb 9, 2022 · In this survey, we mainly focus on the recent scientific developments in semantic segmentation, specifically on deep learning-based methods using 2D images. Apr 10, 2023 · The computer vision, graphics, and machine learning research groups have given a significant amount of focus to 3D object recognition (segmentation, detection, and classification). i. Feb 21, 2016 · This survey gives an overview over different techniques used for pixel-level semantic segmentation. , partitioning of individual persons). Another survey (Li et al. Review of state-of-the-art datasets and evaluation metrics for semantic segmentation. Firstly, the commonly used image segmentation datasets are listed. (), semantic segmentation Wu et al. The comparison of the ViT models specialized for semantic segmentation is discussed with architecture-wise and tabulated specific sets of model variants that can be compared with the same set of benchmark datasets. In this scenario, it makes sense to approach the problem from a semi-supervised point of view, where both labeled and unlabeled images are exploited. 1(a-e)) and video semantic segmentation (Fig. Recent work shows the capability of Deep Neural Networks in labelling 3D point clouds of major sensors like: LiDAR and Radar. The main goal of weakly-supervised semantic segmentation is to train a model by images with only coarse or sparse annotations. Semantic segmentation mainly takes raw data such as images as input and converts it into a mask that highlights Jul 3, 2020 · Hence, instance segmentation may be defined as the technique of simultaneously solving the problem of object detection as well as that of semantic segmentation. Applications such as autonomous driving, Unmanned Aerial Vehicle System (UAVS), and even virtual or augmented reality systems require accurate and efficient segmentation mechanisms. Jan 15, 2020 · Image segmentation is a key topic in image processing and computer vision with applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among many others. However, in challenging situations, DNNs are not generalizable because of the inherent domain shift due to the nature of training under the i. , which are favored for their simplicity and impressive performance. The existing semantic segmentation has a Semantic Segmentation is a computer vision task for predicting the pixel labels corresponding to its belonging region or enclosing region area. the places in the text Aug 12, 2024 · In this survey, for the first time, we present a comprehensive review of DG for semantic segmentation. Unlike other types of image segmentation, such as instance segmentation or boundary detection, semantic segmentation focuses on understanding the high Oct 22, 2023 · Abstract page for arXiv paper 2310. The resulting semantic segmentation can be refined by @article{elhassan2024real, title={Real-time semantic segmentation for autonomous driving: A review of CNNs, Transformers, and Beyond}, author={Elhassan, Mohammed AM and Zhou, Changjun and Khan, Ali and Benabid, Amina and Adam, Abuzar BM and Mehmood, Atif and Wambugu, Naftaly}, journal={Journal of King Saud University-Computer and Information Sciences}, pages={102226}, year={2024}, publisher In the field of computer vision, image semantic segmentation is an important research branch and it is also a challenging task. Nevertheless, owing to the irregular structure of 3D mesh data, the progress in developing pertinent semantic segmentation techniques is still in its nascent stages. In this paper, we have summarized some of the well-known loss functions widely Jun 6, 2024 · The generative models have been widely researched in the field of semantic segmentation. Semantic segmentation is aim at understanding special object class in the scene. A head-free lightweight architecture specifically for semantic segmentation, named Adaptive Frequency Transformer (AFFormer), which adopts a parallel architecture to leverage prototype representations as specific learnable local descriptions which replaces the decoder and preserves the rich image semantics on high-resolution features. At first, we make Mar 1, 2024 · Abstract. Aug 29, 2023 · With the rapid development of sensor technologies and the widespread use of laser scanning equipment, point clouds, as the main data form and an important information carrier for 3D scene analysis and understanding, play an essential role in the realization of national strategic needs, such as traffic scene perception, natural resource management, and forest biomass carbon stock estimation. With the popularity of artificial intelligence models and the increasing expectation of artificial intelligence applications in many fields, reference image segmentation (RIS) has attracted much Feb 21, 2016 · Figure 2: A typical segmentation pipeline gets raw pixel data, applies preprocessing techniques like scaling and feature extraction like HOG features. In this survey paper on instance segmentation- its background, issues, techniques, evolution, popular datasets, related work up to the state of the art and future scope have been discussed. RNNs are useful in processing sequential data, such as videos, where data at Mar 4, 2024 · Semantic segmentation is an important and popular research area in computer vision that focuses on classifying pixels in an image based on their semantics. Semantic segmentation has a broad range of applications in a variety of domains including land coverage analysis mantic segmentation and properly interpret their proposals, prune subpar approaches, and validate results. Many fully supervised deep learning models are designed to implement complex semantic segmentation tasks and the experimental results are remarkable. de Abstract—This survey gives an overview over different techniques used for pixel-level semantic segmentation. Deep neural networks (DNNs) have proven explicit contributions in making autonomous driving cars and related tasks such as semantic segmentation, motion tracking, object detection Dec 6, 2019 · Image semantic segmentation is one of the most important tasks in the field of computer vision, and it has made great progress in many applications. . The goal of semantic Nov 1, 2018 · Many surveys and reviews like [22,23, 39] describe semantic segmentation as the Computer Vision (CV) task of predicting a category label at the pixel level. Additionally, many recent works have been published that focus on implementing pure Transformer or hybrid architectures for real-time semantic segmentation This article examines several seminal efficient deep learning methods, placing them in a taxonomy based on the network architecture design approach, and evaluates the quality and efficiency of some existing efficient DNNs on a publicly available remote sensing semantic segmentation benchmark dataset, OpenEarthMap. Semantic segmentation has always been a very challenging research topic in computer vision and deep learning and has extensive applications in real-life scenarios. Mar 4, 2024 · Semantic segmentation is an important and popular research area in computer vision that focuses on classifying pixels in an image based on their semantics. These survey studies cover almost all the popular semantic segmentation image sets and methods, and for all modalities, such as 2D, RGB, 2. Utilizing 3D mesh data presents myriad advantages for representing actual environments, encompassing the capability to exhibit geometric information and high-quality textures Apr 12, 2023 · This paper consists of a comprehensive survey of Few-Shot Semantic Segmentation, tracing its evolution and exploring various model designs, from the more popular conditional and prototypical networks to the more niche latent space optimization methods, presenting also the new opportunities offered by recent foundational models. This technique has numerous real-world applications, such as autonomous driving, image editing, robot sensing, and medical analysis. Sep 29, 2022 · Instance segmentation technology not only detects the location of the object but also marks edges for each single instance, which can solve both object detection and semantic segmentation Feb 29, 2024 · The review focuses specifically on semantic segmentation using Vision Transformers. Keywords: vision transformer, semantic segmentation, review, survey, Dec 8, 2023 · A novel taxonomy and thorough review of how these loss functions are customized and leveraged in image segmentation, with a systematic categorization emphasizing their significant features and applications is provided. However, in many applications, a frequent obstacle is the lack of labeled images, due to the high cost of pixel-level labeling. This survey gives an overview over different techniques used for pixel-level semantic segmentation. This will be worthwhile for the community to yield knowledge re-garding the implementations carried out in semantic segmentation and to discover more e cient methodologies using ViTs. This method helps to solve some of the most critical problems in autonomous driving applications. In this paper, we present a comprehensive overview of the state-of-the-art semantic image segmentation methods using deep-learning techniques aiming to Jan 15, 2020 · A comprehensive review of recent pioneering efforts in semantic and instance segmentation, including convolutional pixel-labeling networks, encoder-decoder architectures, multiscale and pyramid-based approaches, recurrent networks, visual attention models, and generative models in adversarial settings are provided. [12] and Thoma [13], which do Apr 19, 2023 · This survey provides a thorough overview of transformer-based visual segmentation, summarizing recent advancements and presents a meta-architecture that unifies all recent transformer-based approaches. Linear Text Segmentation is the task of automatically tagging text documents with topic shifts, i. Sheng 4 1 School of Computer Science and Technology, Hainan University, Haikou, 570228, China 2 School of Cyberspace Security (School of Cryptology), Hainan University, Haikou, 570228, China 3 Hainan Blockchain Technology Engineering Research Center 3D semantic segmentation is a fundamental task for many applications like Autonomous Driving. The accuracy and effectiveness of fully supervised semantic segmentation tasks have greatly improved with the increase in the number of accessible datasets. In the context of deep learning-based segmentation algorithms, the choice of an appropriate loss function is crucial for Feb 21, 2016 · This survey gives an overview over different techniques used for pixel-level semantic segmentation such as unsupervised methods, Decision Forests and SVMs and recently published approaches with convolutional neural networks. , assigning each pixel a pre-defined class label (semantic segmentation) [1, 2], or associating each pixel with an object instance (instance segmentation) [], or the Nov 26, 2024 · The pseudo-label method is a well-known technique in the semi-supervised learning field that first appeared in Lee and others and has gained popularity in recent computer vision research, including domain adaptation Li et al. Against this backdrop, the broad success of deep learning (DL) has prompted the The background concept of image semantic segmentation is introduced, generalize the commonly used image semantic segmentation methods, and compare the segmentation results of each method, and the commonly used image semantic segmentation datasets are summarized. To realize more refined semantic image segmentation, this paper studies the semantic segmentation task with a novel perspective, in which three key issues affecting the segmentation effect are considered. Event cameras are a kind of radically novel vision sensors Nov 2, 2023 · The semantic segmentation of point clouds, a crucial step in comprehending 3D scenes, has drawn much attention. e. With the development of computing hardware and deep learning technology, researchers have a higher research enthusiasm for semantic segmentation. Yet Jan 15, 2020 · In this survey, we provide a comprehensive review of the literature at the time of this writing, covering a broad spectrum of pioneering works for semantic and instance-level segmentation instance segmentation may be defined as the technique of simultaneously solving the problem of object detection as well as that of semantic segmentation. , 2018, Minaee et al. Its primary objective is to generate a dense prediction for a given image, i. Semantic segmentation plays a fundamental role in a broad variety of computer vision applications, providing key information for the global understanding of an image. It has received significant attention from the computer vision, graphics and machine learning communities. Various algorithms for image segmentation have been developed in the literature. Aug 23, 2024 · Image segmentation is a long-standing challenge in computer vision, studied continuously over several decades, as evidenced by seminal algorithms such as N-Cut, FCN, and MaskFormer. Metrics and datasets for the evaluation of segmentation algorithms and traditional approaches for segmentation such as unsupervised methods, Decision Forests and SVMs are described and pointers to the relevant papers are given. A large number of novel methods have been proposed. Dec 15, 2023 · Existing 3D mesh semantic segmentation techniques are presented, categorized into traditional methods and deep learning methods, and the challenges confronted by 3D mesh semantic segmentation at present are deliberated. g. In this paper, we present a review of CSS, committing to building a comprehensive survey on problem formulations, primary challenges, universal datasets, neoteric theories and multifarious applications. formulae-sequence 𝑖 𝑒 i. pixel-level classification. This review aims to provide a first comprehensive and organized overview of Jul 2, 2021 · In this survey, we comprehensively review two basic lines of research -- generic object segmentation (of unknown categories) in videos, and video semantic segmentation -- by introducing their respective task settings, background concepts, perceived need, development history, and main challenges. Feb 13, 2023 · Semantic image segmentation (SiS) plays a fundamental role in a broad variety of computer vision applications, providing key information for the global understanding of an image. Dec 1, 2021 · RGB-D semantic segmentation with depth information has been proved to achieve better segmentation results by a lot of experiments, but there is a lack of a comprehensive survey. Jan 1, 2021 · This new benchmark provides standard data of driving scenarios under real-world conditions for a fair comparison of semantic segmentation algorithms. ). Mar 4, 2024 · This review aims to provide a first comprehensive and organized overview of the state-of-the-art research results on pseudo-label methods in the field of semi-supervised semantic segmentation, which is categorize from different perspectives and present specific methods for specific application areas. In the field of computer vision, image semantic segmentation is an important research branch and it is also a challenging task Image semantic segmentation is an important branch in the field of AI. Conventional methods for 3D segmentation, based on hand-crafted features and machine learning classifiers, lack generalization ability. d. With the successful application of deep learning methods in machine vision, the superior performance has been transferred to agricultural image processing by combining them with traditional methods. It has many applications, including tracking forest fires, detecting changes in land use and land cover, crop health monitoring, and so on. Semantic segmentation is an important and popular research area in computer vision that motion segmentation, etc. Semantic segmentation is the problem of assigning a class label to every pixel in an image, and is an important component of an autonomous vehicle vision stack for Jul 1, 2020 · This survey introduces what image semantic segmentation is, what the semantic segmentations approaches are, and the methods of image segmentation with CNN, and several data sets that are often used inimage segmentation experiments. (), etc. Recurrent neural networks (RNNs). cn Abstract Semantic segmentation is an important and popu- This survey presents the first detailed survey on open vocabulary tasks, including open-vocabulary object detection, open-vocabulary segmentation, and 3D/video open-vocabulary tasks. by Jieren Cheng 1,3, Hua Li 2,*, Dengbo Li 3, Shuai Hua 2, Victor S. With the success of efficient deep learning methods [i. Keywords: vision transformer, semantic segmentation, review, survey, Jun 26, 2020 · Image Segmentation has been an active field of research as it has a wide range of applications, ranging from automated disease detection to self-driving cars. 2023a) discussed the transformers for the segmentation task. Image Segmentation has been an active field of research as it has a wide range of applications, ranging from automated disease detection to self driving cars. It builds upon simpler vision tasks This repo is used for recording, tracking, and benchmarking several recent transformer-based visual segmentation methods, as a supplement to our survey. In this survey paper on instance segmentation, its background, issues, techniques, evolution, popular datasets, related work up to the state of the art and future scope have been discussed. Traditional semantic segmentation algorithms are mostly specific to the problem, and there is no universal segmentation algorithm suitable for all images. italic_i . Jan 1, 2022 · Semantic segmentation was traditionally performed using primitive methods; however, in recent times, a significant growth in the advancement of deep learning techniques for the same is observed. It is an important part in many CV tasks and plays a significant role in machine learning. The application area includes remote sensing, autonomous driving, indoor navigation, video Jul 7, 2022 · This section surveys the datasets most commonly used for training and testing semantic segmentation models based on deep learning. Furthermore, segmentation is even deeper than object recognition because recognition is not necessary for segmentation. In the paper, we will give a survey of Semantic Segmentation. We review the recent advances in traditional semantic segmentation methods and deep learning methods used for agricultural images. In this work, we present a comprehensive study of 9 of the most recent Aug 12, 2024 · Five RIS methods are elaborated with their core model structure and procedure in performing RIS, and are categorized into 5 classes in this paper based on how multimodal information is processed. Recently deep learning models have improved state-of-the-art performance on most of the well-known semantic segmentation datasets. It has Jun 7, 2024 · The generative models have been widely researched in the field of semantic segmentation. The common challenge with this type of semantic segmentation is the computational complexity of scaling the spatial dimension of the video using the temporal frame rate. In recent years this line of research Weakly-supervised image semantic segmentation is a popular technology in computer vision and deep learning today. , efficient deep neural networks (DNNs)] for real-time Although some notable 3D segmentation surveys have been released including RGB-D semantic segmentation , remote sensing imagery segmentation , point clouds segmentation , , , , , , these surveys do not comprehensively cover all 3D data types and typical application domains. The initial limitation lies in the surveys’ common focus on comparing methods solely based on segmentation accuracy, typically measured using metrics like mean Feb 21, 2016 · This survey gives an overview over different techniques used for pixel-level semantic segmentation. But deep learning approaches can solve this problem. In this survey paper on instance segmentation -- its background, issues, techniques, evolution, popular datasets, related work up to the state of the art and future scope have been Jul 21, 2024 · Image segmentation is often benchmarked as a pixel-level classification task, which is further refined into three different segmentation tasks: semantic segmentation [1, 46], instance segmentation, and panoramic segmentation [15, 24]. Aug 12, 2024 · This survey presents a comprehensive summary of recent works related to domain generalization in semantic segmentation, which establishes the importance of generalizing to new environments of segmentation models. Sep 29, 2022 · Instance segmentation technology not only detects the location of the object but also marks edges for each single instance, which can solve both object detection and semantic segmentation concurrently. If you find any work missing or have any suggestions (papers, implementations and other resources), feel free to pull requests. assumption. , 2019). Recently, transformers, a type of neural network based on Jun 1, 2024 · Recently, some surveys on semantic image segmentation are available [30], [31]. Semantic segmentation methods have revolutionized to a specific region of interest within the image. As a result, many innovative approaches have been Image segmentation is one of the most researched problems in computer vision. In this survey paper, we present a comprehensive review of the generative models, with a specific focus on Generative Adversarial Networks (GANs), Diffusion Models (DMs), and Variational Autoencoders (VAEs), in the realm of semantic segmentation. The main challenge that faces this task is the nature of 3D point clouds being unordered and spatially-uncorrelated, making it different in terms of processing algorithms than Aug 29, 2023 · This paper systematically outline the main research problems and related research methods in point cloud semantic segmentation and summarize the mainstream public datasets and common performance evaluation metrics. With the advent of foundation models (FMs), contemporary segmentation methodologies have embarked on a new epoch by either adapting FMs (e. Oct 24, 2023 · Real-time semantic segmentation of remote sensing imagery is a challenging task that requires a tradeoff between effectiveness and efficiency. a. Many vision applications need accurate and efficient image segmentation and segment classification mechanisms for assessing the visual contents and perform the real-time decision making. Based on the taxonomy schemes of 9 existing review papers, a new taxonomy scheme of the 3DSS deep learning methods is Jun 17, 2022 · Semantic segmentation is the problem of assigning a class label to every pixel in an image, and is an important component of an autonomous vehicle vision stack for facilitating scene understanding . Nov 4, 2024 · In this paper an exhaustive review and comprehensive analysis of recent and former deep learning methods in 3D Semantic Segmentation (3DSS) is presented. Image semantic segmentation is an important branch in the field of AI. For training, data augmentation techniques such as image rotation can be applied. e. italic_e . Image segmentation is a key task in computer vision and image processing with Mar 9, 2021 · 3D segmentation is a fundamental and challenging problem in computer vision with applications in autonomous driving and robotics. , beach, ocean, sun, dog, swimmer). Mar 9, 2021 · This paper comprehensively surveys the recent progress in deep learning-based 3D segmentation techniques, covering over 220 works from the last six years, analyze their strengths and limitations, and discusses their competitive results on benchmark datasets. Semi-Supervised Semantic Segmentation Based on Pseudo-Labels: A Survey Lingyan Ran 1, Yali Li , Guoqiang Liang∗1, Yanning Zhang1 1School of Computer Science, Northwestern Polytechnical University {lran,gqliang, ynzhang}@nwpu. , 2022a, Guo et al. This work briefly introduces several semantic segmentation models, datasets, and the Image segmentation plays a fundamental role in a wide range of visual understanding systems. cn, yarili@mail. It is worth noting that RailSem19 [142] is the first public outdoor scene dataset for semantic segmentation targeting the rail domain, which is useful for rail applications and road applications Apr 19, 2023 · Visual segmentation seeks to partition images, video frames, or point clouds into multiple segments or groups. The main goal of weakly-supervised semantic segmentation is to train a model by images with only Nov 25, 2024 · An extensive overview of current advances in linear text segmentation is provided, describing the state of the art in terms of resources and approaches for the task and indicating ways forward based on the most recent literature and under-explored research directions. As the tion of domain generalization in semantic segmentation. Jan 1, 2023 · In recent years, deep-learning semantic segmentation algorithms have emerged as important tools for intelligent interpretation of remote-sensing images due to their ability to achieve Apr 21, 2019 · Extensive survey on deep neural networks for semantic segmentation. In this survey paper, we present a comprehensive review of the generative models, with a specific focus on Feb 17, 2021 · Image segmentation is a key task in computer vision and image processing with important applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among others, and numerous segmentation algorithms are found in the literature. However, these achievements rely on time-consuming and expensive full labelling. Mar 25, 2022 · This paper first describes the basic principle and advantageous properties of event camera, then uses DeepLab to do semantic segmentation of the scene and applies this result to the corresponding points of the 3D reconstruction of the same scene, and proposes potential solution to solve the ambiguity problem of semantic segmentsation. There are several repre-sentative methods for semantic segmentation such as query-based and close-set Dec 6, 2024 · Unlike it, weakly supervised semantic segmentation (WSSS) uses only partial or incomplete annotations to learn the segmentation task. Aug 16, 2022 · This survey aims to provide a comprehensive overview of deep learning methods in the field of general image semantic segmentation. Recent developments in deep learning have demonstrated important performance boosting in terms of accuracy. Semantic image segmentation is a vast area of interest for computer vision and machine learning researchers. 5D, RGB-D, and 3D data. In this paper, we have summarized some of the well-known loss functions widely Aug 21, 2024 · Continual semantic segmentation (CSS), of which the dense prediction peculiarity makes it a challenging, intricate and burgeoning task. Various algorithms and techniques in artificial intelligence and machine learning were used and experimented with. As the predominant criterion for evaluating the performance of statistical models, loss functions are crucial for shaping the development of deep learning-based segmentation algorithms and improving their overall performance. Recently, due to the success of deep learning models in a wide range Instance segmentation extends semantic segmentation scope further by detecting and delineating each object of interest in the image (e. Our survey covers the most recent literature in image segmentation and discusses more than a hundred deep learning-based segmentation methods proposed until 2019. Weakly-supervised image semantic segmentation is a popular technology in computer vision and deep learning today. We started with an analysis of the public image sets and leaderboards for 2D semantic segmentation, with an overview of the techniques employed in performance evaluation. 110 neural network models are categorized into 10 different concepts. The goal of semantic segmentation is to classify and assign a label to every pixel in an image, indicating the category or class it belongs to. [15] covering a wide range of the papers and areas of semantic segmentation topics including, interactive methods, recent development in the super-pixel, object proposals, semantic image The procedure of 3D mesh semantic segmentation entails assigning distinct semantic labels to each triangle, thus dividing the entire scene into various categories. Continual learning, also known as incremental learning or life-long learning, stands at the forefront of deep learning and AI systems. In this paper, the main purpose is to offer a detailed review of RGB-D semantic segmentation according to the research progress in recent years. Visual segmentation seeks to partition images, video frames, or point clouds into multiple segments or groups. This survey mainly focuses on recent progress in two major branches of video segmentation, namely video object segmentation (Fig. It serves as a vital component in computer vision-based applications including lane analysis for autonomous vehicles (Fischer, Azimi, Roschlaub, & Krauß, 2018) and geolocalization for Unmanned Aerial Vehicles (Nassar, Amer, ElHakim, & ElHelw, 2018). However, many of the top performing semantic segmentation models are extremely complex and cumbersome, and as such are not suited to deployment onboard autonomous vehicle A Survey of Semantic Segmentation Martin Thoma info@martin-thoma. This paper aims to provide a brief review of research efforts on deep-learning-based semantic segmentation methods. Sep 17, 2020 · Semantic segmentation is a challenging task in computer vision. , CLIP, Stable Diffusion, DINO) for image segmentation or developing Sep 22, 2022 · A Survey on Image Semantic Segmentation Using Deep Learning Techniques. Metrics and datasets for the evaluation of segmenta-tion algorithms and traditional approaches for segmen-tation such as unsupervised methods, Decision Forests Nov 1, 2024 · In spite of the existence of valuable survey papers on semantic segmentation (Mo et al. k. To realize more refined semantic image segmentation, this paper studies the semantic segmentation task with a novel Nov 1, 2023 · Usually, the idea is to apply semantic segmentation on frames of a high-resolution video where the video is considered as a set of uncorrelated fixed images (Jain et al. To Apr 21, 2019 · Some surveys and review papers have addressed advancements and innovations on the subject of deep learning and semantic segmentation. 3D segmentation is a fundamental and challenging problem in computer vision with applications in autonomous driving and robotics. Metrics and datasets for the evaluation of segmentation algorithms and Feb 20, 2023 · Semantic segmentation is one of the most challenging tasks in computer vision. This makes the weakly supervised approach more feasible for real-world applications, where obtaining large amounts of fully labeled data can be prohibitively expensive or time consuming. we present a comprehensive summary of recent works related to domain generalization in Aug 1, 2022 · In particular, deep neural networks headed by convolutional neural networks can effectively solve many challenging semantic segmentation tasks. According to whether the datasets take into account the changes of lighting conditions, weather and seasonal, this paper divides these datasets into two categories: no cross-domain datasets and cross-domain datasets, and provides the characteristics of each dataset. In particular, deep neural networks headed by convolutional neural networks can effectively solve many challenging semantic segmentation tasks. Colorectal polyps are common intestinal diseases, in which image segmentation of colorectal polyps is crucial for diagnosis and treatment. ViT architectures designed for semantic segmentation using benchmarking datasets. cn Abstract Semantic segmentation is an important and popu- Sep 17, 2020 · Semantic segmentation is a challenging task in computer vision. In recent years, the performance of semantic segmentation has been greatly improved by using deep learning techniques. However, supervised deep learning requires large amounts of data to train models and the process of labeling images pixel by pixel is time-consuming and laborious. A Survey by Zhu et al. But up to now, there has not been a systematic summary of semantic segmentation of agricultural images. To the best of our knowledge, this is the first review to focus explicitly on deep learning for semantic segmentation. In the past 5 years, various papers came up with different objective loss Sep 22, 2021 · Semantic image segmentation for autonomous driving is a challenging task due to its requirement for both effectiveness and efficiency. 14277: A Survey on Continual Semantic Segmentation: Theory, Challenge, Method and Application Continual learning, also known as incremental learning or life-long learning, stands at the forefront of deep learning and AI systems. , 2021), they typically exhibit four common limitations. May 8, 2023 · ViT architectures designed for semantic segmentation using benchmarking datasets. Real-time semantic segmentation of remote sensing imagery is a challenging task May 1, 2023 · This paper summarizes the state-of-the-art research results and analyzes the existing problems and future development directions in the field of weakly-supervised semantic segmentation. Jun 28, 2020 · Hence, instance segmentation may be defined as the technique of simultaneously solving the problem of object detection as well as that of semantic segmentation. With the rapid development of sensor technologies and the widespread use of laser scanning equipment, point clouds, as the main data form and an important information carrier for 3D Dec 1, 2024 · However, these real-time semantic segmentation surveys have focused solely on CNN-based methods, and since then, there has been significant progress in convolutional neural networks. Aug 12, 2024 · Deep neural networks (DNNs) have proven explicit contributions in making autonomous driving cars and related tasks such as semantic segmentation, motion tracking, object detection, sensor fusion, and planning. Nov 15, 2023 · Semantic segmentation is a method to classify each of the pixels in an image from a given list of predefined classes. This survey is an effort to summarize five years of this incredibly rapidly growing field, which embraces the importance of semantic segmentation itself and a critical need of adapting segmentation models to new environments. Our survey will give a detail introduction to the instance segmentation technology based on deep learning, reinforcement learning and transformers. Dec 8, 2023 · Semantic image segmentation, the process of classifying each pixel in an image into a particular class, plays an important role in many visual understanding systems. Specifically, it assigns a label to each pixel through coarse label refinement or sparse label propagation, etc. Driven by their success May 10, 2020 · The various DL-based image segmentation models included in this survey are: Fully Convolutional networks; Later on, another semantic segmentation model emerged based on Graph LSTM (Graph Long Semi-Supervised Semantic Segmentation Based on Pseudo-Labels: A Survey Lingyan Ran 1, Yali Li , Guoqiang Liang∗1, Yanning Zhang1 1School of Computer Science, Northwestern Polytechnical University {lran,gqliang, ynzhang}@nwpu. Traditional semantic segmentation algorithms are mostly specific to the problem, and there is May 5, 2023 · This survey aims to review and compare the performances of ViT architectures designed for semantic segmentation using benchmarking datasets to yield knowledge regarding the implementations carried out in semantic segmentations and to discover more efficient methodologies using ViTs. As Apr 2, 2022 · Recently, many semantic segmentation methods based on fully supervised learning are leading the way in the computer vision field. With the Aug 24, 2023 · Image Segmentation Using Deep Learning: A Survey Shervin Minaee, Yuri Boykov et al. corhrrpyecpisqjjxfnsrmtovsarrqeutgxhqcvikktooculmunimucdsldnvmgpkddxgoxvccctna