Semantic segmentation survey. Surveys on Semantic Segmentation.

Semantic segmentation survey This survey provides a thorough overview of transformer-based visual 3D point cloud segmentation is the process of classifying point clouds into multiple homogeneous regions, the points in the same region will have the same properties. Semantic segmentation involves extracting meaningful information from images or input from a video or recording frames. We pivot some notable 3D segmentation surveys have been released including RGB-D semantic segmentation [31], point clouds segmentation [158],[37],[88],[4],[103],[54], these surveys do not comprehensively cover all 3D data types and typical application domains. This survey is an In this survey, we mainly focus on the recent scientific developments in semantic segmentation, specifically on deep learning-based methods using 2D images. It breaks through the obstacle of one-way training on close A Survey on Image Semantic Segmentation Using Deep Learning Techniques. Regarding accurate boundary recovery for semantic segmentation, Garcia-Garcia et al. Then, with the great success of deep learning in various fields [7], researchers tried to use deep learning techniques for semantic segmentation task, and 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. From a more technical perspective, semantic image segmentation refers to the task of assigning a semantic label to each pixel in the image [1], [2], [3]. In this paper, we analyze the different models for semantic segmentation for self-driving cars using DL architectures of CNNs and AEs and state-of-the art techniques such as feature pyramid networks and bottleneck residual A Survey. formulae-sequence 𝑖 𝑒 i. In this scenario, it makes sense to approach the In the eld of computer vision, image semantic segmentation is an important research branch and it is also a challenging task. Continual learning, also known as incremental learning or life-long learning, stands at the forefront of deep Semantic segmentation is an important and popular research area in computer vision that focuses on classifying pixels in an image based on their semantics. 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. Instance segmentation additionally distinguishes between different instances of the same class labels, e. Models are usually evaluated with Image segmentation is a prerequisite for image processing. Mainly semantic segmentation and instance segmentation of an image are discussed. Abstract: 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, A Survey of Semantic Segmentation Martin Thoma info@martin-thoma. Image segmentation is an important processing step in many image, video and computer vision applications. Author links open overlay panel Xinye Li a b, Ding Chen a. In recent years, inspired by deep learning, the performance of semantic segmentation has been greatly improved. It gives us more accurate and fine details from the data we need for further evaluation. 2018; Guo M. 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. Recently convolutional neural network is a widely used model in image This survey gives an overview over different techniques used for pixel-level semantic segmentation. Add to Mendeley. Semantic segmentation is an important and well-received research area in the field of computer vision to classify every pixel in an image, and it has a wide range of applications in specific areas such as medical image segmentation Li et al. However, many of the top performing semantic segmentation models are extremely complex and cumbersome, and as such are not suited to 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. Many fully supervised deep learning models are designed to implement complex semantic segmentation tasks and the experimental results are remarkable. Review of state-of-the-art datasets In this survey, we mainly focus on the recent scientific developments in semantic segmentation, specifically on deep learning-based methods using 2D images. The statistics of the research published are shown in Figure 2A (journals with less than three articles not Hence, instance segmentation may be defined as the technique of simultaneously solving the problem of object detection as well as that of semantic segmentation. Show 3 more articles. 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. Survey on semantic segmentation Recently, two review papers on video segmentation have appeared. , CLIP, Stable [1], Previous transformer surveys divide the methods by the different tasks and settings. This review cannot fully cover the entire field. They have summa-rized the strengths, weaknesses and major challenges in image semantic segmentation. , state-of-the-art fully-supervised detectors and segmentors fail to generalize 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 Semantic segmentation has a broad range of applications in a variety of domains including land coverage analysis, autonomous driving, and medical image analysis. This will be worthwhile for Semantic segmentation assigns a category label to each pixel of an image, which is a fundamental but challenging task in computer vision research. explored the broader scope few and zero-shot visual semantic segmentation methods across 2D and 3D spaces, this survey is dedicated exclusively to FSS, providing a more updated and detailed analysis specifically tailored to the segmentation task in the few-shot scenario. Rui Qian, , Xirong Li. Yao et al. instance segmentation may be defined as the technique of simultaneously solving the problem of object detection as well as that of semantic segmentation. g. Due to the expensive manual labeling cost, the annotated categories in existing datasets are often small-scale and pre-defined, i. A survey of loss functions for semantic segmentation 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. pixel-level classification. Recently, with the successful application of deep learning in remote sensing, a substantial Point cloud learning has recently gained strong attention due to its applications in various fields, like computer vision, robotics, and autonomous driving. 2017; Saffar et al. However, the above methods either rely +2/'(5 (7 $/ 21 ()),&,(17 5($/ 7,0( 6(0$17,& 6(*0(17$7,21 $ 6859(< 1 1 1 1 ý x v þ 1 ý x w þ ð 1 1 ý x x þ 1 ý x y þ ð 1 1 , Semantic segmentation of point cloud, as a key step in understanding 3D scenes, has attracted extensive attention of researchers. motion segmentation, etc. With the success of efficient deep learning methods (i. This focused approach allows for a deeper understanding Abstract page for arXiv paper 2401. Weakly supervised image semantic segmentation is quite popular in computer vision and machine learning today. This terminology was further distinguished from instance-level segmentation [4] that 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. 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. These methods can be divided into five categories according to the data representation used, namely, RGB-D image-based, projected images-based, voxel-based, point-based, 3D video, and other representations-based. 2023a) discussed the transformers for the segmentation task. Qiao et al. Herein, we have described comparative architectural details of notable different state-of-the-art In this survey, we cover two settings (zero-shot and open-vocabulary) and six tasks (object detection, semantic/instance/panoptic segmentation, 3D scene understanding, and video understanding). Most importantly, these surveys do not focus on 3D segmentation but give a general survey of deep image semantic segmentation methods on the basis of extensive survey. This leaves a notable gap in capturing recent FM-based approaches. However, pixel 3D point cloud segmentation is the process of classifying point clouds into multiple homogeneous regions, the points in the same region will have the same properties. 1(b), the toy example depicts training in urban areas with no sight of the tree category at training time, In this survey we identified several gaps such as the lack of open-set specific datasets allowing comparing methods; the untapped potential of self-supervised strategies to improve training and Multi-class semantic segmentation aims to predict a discrete semantic class for every pixel in an image. This is useful in the sense of identifying Abstract. Their 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 Semantic segmentation is the pixel-wise labeling of an image. In this paper, we provide a survey covering various aspects ranging from indirect segmentation to direct segmentation. A Survey by Zhu et al. Metrics and datasets for the evaluation of segmentation algorithms and traditional approaches 3D segmentation is a fundamental and challenging problem in computer vision with applications in autonomous driving and robotics. In our survey, we provide a set of segmentation models, for each of which we define the best variant in each benchmark dataset category. Semantic segmentation is now a vast field and is closely related to other computer vision tasks. 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. After that, the commonly used image semantic segmentation datasets are Real-time semantic segmentation of remote sensing imagery is a challenging task that requires a tradeoff between effectiveness and efficiency. Instance segmentation distinguishes between different instances of the same class labels, e. , Guo, Y. In addition，this study conducts a quantitative evaluation of the realtime semantic segmentation methods discussed and provides some insights into the future development in this field. Unlike it, weakly supervised semantic segmentation (WSSS) uses only partial or incomplete annotations to learn the segmentation Semantic segmentation, as a high-level task in the computer vision field, paves the way toward complete scene understanding. 2017; Jiang et al. 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. Geng et al. ). In the past five years, various papers came up with different objective loss functions used in different cases such as Some surveys and review papers have addressed advancements and innovations on the subject of deep learning and semantic segmentation. We also propose a taxonomy for interpretable semantic segmentation, and discuss potential challenges and future Semantic segmentation is a significant and demanding work in computer vision and it has gained more attention worldwide. Specifically, it assigns a label to each pixel through coarse label refinement or sparse label propagation, etc. Besides, it still predicts class-aware masks. In this survey paper on instance segmentation, its background, different tasks, including semantic segmentation (SS), instance TABLE 1: Notation and abbreviations used in this survey. of semantic segmentation approaches, i. Bhandarkar, and B. “A Comparison of Deep Learning Methods for Semantic Segmentation of Coral Reef Survey Images. In particular, deep neural networks headed by convolutional neural networks can effectively 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, including fully convolutional pixel-labeling networks, encoder-decoder architectures, multi-scale and pyramid based approaches, recurrent networks, visual Image semantic segmentation is an important branch in the field of AI. 1. 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. This survey gives an overview over different techniques used for pixel-level semantic segmentation. 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. This paper reviews the feature fusion for semantic segmentation. In the image analysis community, it consists in classifying each of the pixels on standard 2D images, or voxels when dealing with volumetric images or spatio-temporal data such as video. But deep learning approaches can solve this problem. Boosted by the extraordinary ability of convolutional neural networks (CNN) in creating semantic, high-level and hierarchical image 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. This adaptability has spurred a wave of creativity and innovation in applying SAM to medical imaging. We . First, we conduct a comprehensive survey on traditional methods, primarily focusing on those presented at premier research conferences. , assigning each pixel a pre-defined class label (semantic segmentation) [1, 2], or associating each pixel with an object instance (instance segmentation) [], or the 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. However, in many applications, a frequent obstacle is the lack of labeled images, due to the high cost of pixel-level labeling. It can be extremely beneficial in the study of underwater scenes. In this paper, we have summarized some of the well 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. Semantic segmentation models are limited in their ability to scale to large numbers of object classes. Recently deep learning models have improved state-of-the-art performance on most of the well-known semantic segmentation datasets. Categorization of the 38 papers reviewed in this survey. But it focuses on instance segmentation instead of semantic segmentation. We analyze and categorize the literature based on application categories A survey of loss functions for semantic segmentation Shruti Jadon IEEE Member shrutijadon@ieee. There are several repre-sentative methods for semantic segmentation such as query-based and close-set Instance segmentation extends semantic segmentation scope further by detecting and delineating each object of interest in the image (e. The remainder of this survey is organized as follows: Section 2 r eviews some of the most popular Semantic segmentation is one of the most challenging tasks in computer vision. : A brief survey on semantic segmentation with deep learning. In this paper, we provide a systematic review of recent advances to fully 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. About We collect the works from more than 100 articles based on RS semantic segmentation. Nandi5 1 Shaanxi Joint Laboratory of Artificial Intelligence, Shaanxi University of Science and Technology, 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. With the development of computing hardware and deep learning technology, researchers have a higher research enthusiasm for semantic segmentation. This technique has numerous real-world applications, such as autonomous driving, image editing, robot sensing, and medical analysis. A Survey on Continual Semantic Segmentation: Theory, Challenge, Method and Application | Abstract. Point cloud semantic segmentation (PCSS) enables the automatic In this paper, we focus on analyzing and discussing deep learning based fusion methods in multimodal semantic segmentation. However, in some complex environments or under challenging conditions, it is necessary to employ multiple modalities that provide complementary information on the same scene. Semantic segmentation is also useful for changing the pixels of the image into Real-time semantic segmentation of remote sensing imagery is a challenging task that requires a trade-off between effectiveness and efficiency. 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. Assigning a semantic label to each pixel in an image is a challenging task. Class-Agnostic Segmentation is a long-standing prob-lem and has been extensively studied in computer vi- The large-scale pretrained model CLIP, trained on 400 million image-text pairs, offers a promising paradigm for tackling vision tasks, albeit at the image level. 2018; Yu et al. Semantic segmentation is an important computer vision task due to its numerous real-world applications such as autonomous driving, video surveillance, medical image analysis, robotics, augmented reality, among others, and its popularity increased with the development of deep learning approaches. 3, we analyze various fusion mechanisms with different architectures. To the best of our knowledge, this is the first review to focus explicitly on deep learning for semantic segmentation. The application area includes remote sensing, autonomous driving, indoor 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. First of all, we introduce the background concept of image semantic segmentation, generalize the com-monly used image semantic segmentation methods, and compare the segmentation results of each method. The segmentation is challenging because of high redundancy, uneven sampling density, and lack explicit structure of point cloud data. At first, it will discuss the concept of attention and its integration with semantic In order to provide a comprehensive assessment of the performance of different structural models in the field of semantic segmentation for high-resolution remote sensing, we use four metrics, Surveys on Semantic Segmentation. By tackling the video instance segmentation tasks through effective analysis and utilization of visual information in Zero-shot semantic segmentation technology: Currently, medical image segmentation networks mainly operate in supervised learning mode, and their outstanding performance often relies on a large amount of annotated sample data. We started with an analysis of the public image sets and leaderboards for 2D In this paper a review on literature on the various techniques in semantic segmentation is presented. This article investigates the emergence and development of semantic segmentation both domestically and internationally. 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 work briefly introduces several semantic @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 Semantic segmentation plays a fundamental role in a broad variety of computer vision applications, providing key information for the global understanding of an image. , partitioning of individual persons). Different from them, we re-visit and group the existing transformer-based methods from the technical perspective. Notations Descriptions SS / IS /PS Semantic Segmentation / Instance Segmentation / Panoptic Segmentation VSS / VIS / VPS Video Semantic / Instance / Panoptic Segmentation DVPS Depth-aware Panoptic Segmentation In this paper, we conduct a survey on the most relevant and recent advances in Deep Semantic Segmentation in the context of vision for autonomous vehicles, from three different perspectives: efficiency-oriented model development for real-time operation, RGB-Depth data integration (RGB-D semantic segmentation), and the use of temporal In addition to providing a thorough overview of the history and current state of the art in weakly supervised semantic segmentation of 3D point clouds, a detailed description of the most widely The semantic image segmentation task consists of classifying each pixel of an image into an instance, where each instance corresponds to a class. Boosted by the extraordinary ability of convolutional neural networks (CNN) in creating semantic, high level and hierarchical image 1. The existing semantic 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 Semantic segmentation aims to predict object class labels such as tables and chairs. Finally, an insightful discussion of the remaining problems on both methodological and Received: 21 February 2021 Revised: 1 September 2021 IET Image Processing DOI: 10. (2017) This task is different from semantic segmentation and instance segmentation in that it involves unique challenges such as multimodal information fusion, variability of natural language expressions, and model robustness. takos@gmail. It has many applications, including tracking forest fires, detecting changes in land use and land cover, crop health monitoring, and so on. Most importantly, these surveys do not focus on 3D segmentation but give a general survey of deep learning from point clouds Fully supervised semantic segmentation requires a large number of labeled images for training. Next, we introduce the development of semantic image segmentation from uni-modality to multi-modality and their applications for 100 indoor and outdoor scene understanding. de Abstract—This survey gives an overview over different techniques used for pixel-level semantic segmentation. k. The accuracy and effectiveness of fully supervised semantic segmentation tasks have greatly improved with the increase in the number of accessible datasets. According to the research status of semantic segmentation based on deep learning, this paper firstly combs the semantic segmentation method based on convolutional neural network and the new method based on Transformer respectively, and briefly introduces Semantic image segmentation for autonomous driving is a challenging task due to its requirement for both effectiveness and efficiency. To this end, we present a novel architecture, ZS3Net, combining a deep Download Citation | A Brief Survey on Semantic Segmentation with Deep Learning | Semantic segmentation is a challenging task in computer vision. 28, 2021, arXiv: Loss Functions in the Era of Semantic Segmentation: A Survey and Outlook • We systematically and thoroughly examine the loss functions in the field of image segmentation and provide a comparison and analysis of these approaches. Specifically,25 loss functions in semantic segmentation are covered in a hierarchical and structured manner. Additionally, many recent works have been published that focus on implementing pure Transformer or hybrid architectures for real-time semantic segmentation. 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 work aims to fill this gap by presenting algorithms in the literature and categorizing them by their input images. At first, we make a brief introduction of Semantic Segmentation, introducing the wide use of semantic segmentation. Section 4 provides a comprehensive account of the feature fusion strategy. Metrics and datasets for the evaluation of segmenta-tion algorithms and traditional approaches for segmen-tation such as unsupervised methods, Decision Forests Semantic segmentation has a broad range of applications in a variety of domains including land coverage analysis, autonomous driving, and medical image analysis. Our review focuses on the period from April 1, 2023, to September 30, 2023, a critical first six months post 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 Semantic segmentation is the pixel-wise labelling of an image. In the medical image analysis domain, image segmentation can be used for image-guided interventions, In this survey, for the first time, we present a comprehensive review of DG for semantic segmentation. M. In this survey paper, we present a comprehensive review of the generative models, with a specific focus on Few-shot semantic segmentation (FSS) is a challenging task that aims to learn to segment novel categories with only a few labeled images, and it has a wide range of real-world applications. Formerly, we had a few techniques based on some This section surveys the datasets most commonly used for training and testing semantic segmentation models based on deep learning. This survey mainly focuses on recent progress in two major branches of video segmentation, namely video object segmentation (Fig. LoveDA is a dataset constructed by the RSIDEA team at the State Key Laboratory of Surveying and Mapping and Remote Sensing Information Engineering 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]. We also offer a detailed overview of A Survey on Continual Semantic Segmentation: Theory, Challenge, Method and Application Bo Yuan1,2, Danpei Zhao1,2 1 AIRVIC Lab, Beihang University 2 Tianmushan Laboratory, Hangzhou [paper] | [Gi In this paper, we present the first comprehensive survey on XAI in semantic image segmentation. A SURVEY ON DEEP LEARNING METHODS FOR SEMANTIC IMAGE SEGMENTATION IN REAL-TIME Georgios Takos Mountain View, CA georgios. It builds upon simpler vision tasks This survey gives an overview over different techniques used for pixel-level semantic segmentation. 07654: Foundation Models for Biomedical Image Segmentation: A Survey. italic_i . Recently published in semantic segmentation using deep learning, and challenges, and future research directions. In Sect. Various semantic segmentation surveys already exist such as the works by Zhu et al. With the achievement of profound learning strategies in PC vision, analysts have endeavored to move their better presentation than the field of distant detecting picture examination. , S. This survey study has covered recent CNN-based state-of-the-art. In particular, deep neural networks headed by convolutional neural networks can effectively solve many challenging 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. Visual segmentation seeks to partition images, video frames, or point clouds into multiple segments or groups. Semantic segmentation involves assigning specific semantic class labels to each pixel in an image, enabling precise identification and differentiation of various objects, entities, or regions. We conduct the survey from different perspectives including background, foundations, datasets, technical approaches, benchmarking, and future research directions. We believe that this survey will provide a clear big picture on what we have achieved, and we could further achieve along King, A. Semantic segmentation mainly takes raw data such as images as input and converts it into a mask that highlights 多图预警！这学期选了一门CV的课，最后老师让做一个Survey，选了semantic segmentation这个课题，最后还要我们做presentation，这里把自己组做的slide放出来，供大家参考。由于是现场讲的，很多细节没有在slide里体现出来，等有空补充些文字说明。 Even though there are multiple surveys on semantic segmentation methods, the literature lacks a comprehensive survey centered explicitly around semantic segmentation using infrared spectrum. , chairs one and two. This article delivers an in-depth analysis of vision-based semantic segmentation approaches for 3D point cloud data. Unlabeled RSIs can be obtained relatively easily. 2017; Garcia-Garcia et al. Jieren Cheng 1,3, Hua Li 2,*, Dengbo Li 3, Shuai Hua 2, Victor S. A variety of studies have demonstrated that deep multimodal fusion for 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 **Semantic Segmentation** is a computer vision task in which the goal is to categorize each pixel in an image into a class or object. , chair one and two. Since unlabelled data is instead significantly Semantic segmentation remains a key research field in modern day computer vision and has been used in a myriad of applications across various fields. Thoma, A survey of semantic segmentation, CoRR (2016). We are the first to use the region-level classification forzero-shot semantic segmentation. With the success of deep learning methods in the field of computer vision, researchers have A follow-up survey by Zhu et al. There are many methods for image segmentation, and as a result, a great number of methods for evaluating segmentation results have also been proposed. 2. akin to semantic priming. Show more. Recent success of deep learning-based segmentation methods usually relies on a large amount of labeled data, which is particularly difficult and costly to obtain, especially in the medical imaging domain where only experts can provide reliable and accurate Real-Time Semantic Segmentation: A Brief Survey & Comparative Study in Remote Sensing Clifford Broni-Bediako, Member, IEEE, Junshi Xia, Senior Member, IEEE, Naoto Yokoya, Member, IEEE Abstract—Real-time semantic segmentation of remote sensing imagery is a challenging task that requires a trade-off between effectiveness and efficiency. , efficient deep neural networks) Hence, instance segmentation may be defined as the technique of simultaneously solving the problem of object detection as well as that of semantic segmentation. In order to produce more refined semantic image segmentation, we survey the powerful CNNs and novel elaborate layers, structures and strategies, especially including those that have achieved For semantic segmentation, in Fig. 1049/ipr2. In this work, we present a Semantic image segmentation is a vast area of interest for computer vision and machine learning researchers. Section 2 details typical feature fusion methods for different models and shows common defects and improvements. This will be worthwhile for the community to yield knowledge regarding the implementations carried out in semantic segmentation and to discover more efficient methodologies using ViTs. , 2021a), point cloud (Guo et al. Wang et al. , 2021), unmanned aerial vehicle-images (UAV) (Osco et al. Our survey will give a detail introduction to the instance segmentation technology based on deep learning, reinforcement learning and transformers. How to effectively evaluate the quality of image segmentation is very important. Share. Various underwater applications, such as The main motivation of this paper is to provide a comprehensive survey of semantic segmentation methods, focus on analyzing the commonly concerned problems as well as the corresponding strategies adopted. Sheng 4. The accuracy and effectiveness of fully supervised semantic segmentation tasks have greatly Abstract page for arXiv paper 2310. This work focuses on techniques that were either specifically introduced for dense prediction tasks or were extended for them by modifying existing methods in classification. This task is attracting a wide interest since The generative models have been widely researched in the field of semantic segmentation. In recent Weakly-supervised image semantic segmentation is a popular technology in computer vision and deep learning today. [19] presented a survey of recent progress in semantic segmentation with CNN’s, and Image segmentation is one of the most researched problems in computer vision. com September 29, 2020 ABSTRACT Semantic image segmentation is one of fastest growing areas in computer vision with Feature-Proxy Transformer for Few-Shot Segmentation. Continual learning, also known as incremental learning or life-long learning, stands at the forefront of deep learning and AI systems. In the past 5 years, various papers came up with different objective loss The procedure of 3D mesh semantic segmentation entails assigning distinct semantic labels to each triangle, thus dividing the entire scene into various categories. The survey is organized as follows: Section 2 describes the most common sensors and their dataset cause semantic segmentation in thermal images to be under-explored. DNNs play a significant role in environment perception for the challenging application of automated driving and are employed for tasks such as detection, semantic segmentation, and sensor Semantic image segmentation is a fundamental task in computer vision that assigns a label to each pixel, a. (2018) and Ulku and Akagunduz (2019) only briefly mentioned TRENDS AND SURVEYS 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. survey include RGB-D cameras, Near-InfraRed cameras, thermal cameras, and polarization cameras. The goal is to produce a dense pixel-wise segmentation map of an image, where each pixel is assigned to a specific class or object. These annotations include the boundaries of each category and are based on category-level as well as Semantic segmentation refers to the task of assigning a class label to each of the low-level components of a media content. 14277: A Survey on Continual Semantic Segmentation: Theory, Challenge, Method and Application. Recent advances in this topic are dominantly led by deep learning-based methods. However, in challenging situations, DNNs are not generalizable because of the inherent domain shift due to the nature of training under the i. In the paper, we will give a survey of Semantic Segmentation. , efficient deep neural networks (DNNs)] for real In this paper, we present the first comprehensive survey on XAI in semantic image segmentation. 110 neural network models are categorized into 10 different concepts. Semantic segmentation: Semantic segmentation is one of the basic computer vision tasks with a long history. provided a good survey on the video object segmentation and tracking methods based on hand-crafted features and deep learning. In: Proceedings of the IEEE conference on computer vision and pattern Semantic image segmentation is a fundamental task in computer vision that assigns a label to each pixel, a. Google Scholar [17] J. (ii) We realize a GSS model in an established conditional image generation framework, with minimal need for task- Specifically, vision transformers offer robust, unified, and even simpler solutions for various segmentation tasks. Our survey covers the most recent literature in image segmentation and discusses more than a hundred deep learning-based segmentation methods proposed until 2019. Deep learning approaches have 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. The layout of the paper is designed as follows: Section 2 describes the Deep This paper gives an introductory survey of the rising topic attention mechanisms in semantic segmentation. Semantic Image Segmentation Semantic segmentation is a pixel-level image analysis task that involves partitioning an image into distinct and coherent regions, where each pixel is assigned a class label representing the semantic class it belongs to (e. Appl. However, it does not hold a broad and comprehensive understanding of DG in semantic segmentation. Cite. Recently, 语义分割 (Semantic segmentation) 是指将图像中的每个像素链接到类标签的过程。这些标签可能包括人、车、花、家具等。 Image Segmentation Using Deep Learning: A Survey(1) 摘要 图像分割在图像处理和计算机视觉中扮演着关键的角色，在许多应用中发挥着重要的作用，例如 tion of domain generalization in semantic segmentation. In this paper, the existing image segmentation quality The computer vision, graphics, and machine learning research groups have given a significant amount of focus to 3D object recognition (segmentation, detection, and classification). Second, an organized survey of 3D semantic segmentation methods is given with a focus on the mainstream of the latest research trend using deep learning techniques, followed by a systematic survey to the existing efforts to solve the data hunger problem. With the success of efficient deep learning methods [i. It is the way to perform the extraction by checking pixels by pixel using a classification approach. Its primary objective is to generate a dense prediction for a given image, i. Soft Comput. , efficient deep neural networks Many deep learning methods for 3D semantic segmentation have been proposed in the literature. italic_e . Niemeijer, P. a. Boosted by the extraordinary ability of convolutional neural networks (CNN) in creating semantic, high-level and hierarchical image A Survey on Semi-Supervised Semantic Segmentation Abstract—Semantic segmentation is one of the most challenging tasks in computer vision. P. 2018. Its range is Extensive survey on deep neural networks for semantic segmentation. Barth, A review of neural network based semantic segmentation for scene understanding in context of the self driving car, Proceedings of the BioMedTec Studierendentagung, 2017. 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, In light of these features, we found that most existing surveys in the field [68, 69, 70] are now outdated – one of the latest surveys was published in 2021 and focuses only on semantic and instance segmentation. , masks of objects), which are expensive, time-consuming, and labor-intensive. Over the past few years, many works have made significant Continual semantic segmentation (CSS), of which the dense prediction peculiarity makes it a challenging, intricate and burgeoning task. Later works, such as DenseCLIP and LSeg, extend this paradigm to dense prediction, including semantic segmentation, and have achieved excellent results. , 2021), and precise boundary recovery of boundaries in segmented images and point Semi-Supervised Semantic Segmentation Based on Pseudo-Labels: A Survey Lingyan Ran 1, Yali Li , Guoqiang Liang∗1, Yanning Zhang1 Semantic segmentation is an important and well-received re-search area in the field of computer vision to classify every pixel in an image, and it has a wide range of applications Image segmentation plays a fundamental role in a wide range of visual understanding systems. This review aims to provide a With increased interest in semantic segmentation using DNNs, survey papers have been published to detail the progress made in 2D (Ulku and Akagündüz, 2022), 3D (Gao et al. Some difficulties with manual segmentation have necessitated the use This survey aims to review and compare the performances of ViT architectures designed for semantic segmentation using benchmarking datasets. 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, Real-time semantic segmentation of remote sensing imagery is a challenging task that requires a tradeoff between effectiveness and efficiency. First， semantic segmentation and real-time semantic segmentation tasks and their application scenarios and challenges are introduced. This is useful as an attention mechanism: by ignoring the irrelevant parts of the image, only relevant parts are retained for further analysis, such as faces and human parts (Prince 2012a). Most importantly, these surveys do not focus on 3D segmentation but give a general survey of deep learning from point clouds As the most fundamental scene understanding tasks, object detection and segmentation have made tremendous progress in deep learning era. Against this backdrop, the broad success In recent years, convolutional neural networks (CNNs) are leading the way in many computer vision tasks, such as image classification, object detection, and face recognition. This project website contains both an up-to-date The process of semantic segmentation entails the allocation of semantic labels to each individual pixel within an image, which results in a segmented image where each region corresponds to a distinct class or The task of semantic segmentation holds a fundamental position in the field of computer vision. We hope that this survey helps accelerate progress in this field. According to whether the datasets take into account the changes of lighting conditions, In this paper, we systematically outline the main research problems and related research methods in point cloud semantic segmentation and summarize the mainstream public datasets and common Semantic segmentation of remote sensing imagery has been employed in many applications and is a key research topic for decades. This task is a part of the concept of scene understanding or better explaining the global context of an image. Conventional methods for 3D segmentation, based on hand-crafted features and machine learning classifiers, lack mantic segmentation and properly interpret their proposals, prune subpar approaches, and validate results. [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 Point Cloud Based Scene Segmentation: A Survey [2025-03-18] 3D Visual. nucleus, and mitochondrion). 三维重建 Cutting-edge 3D reconstruction solutions for underwater coral reef images: A review and comparison A Preliminary Survey of Semantic Recently, many semantic segmentation methods based on fully supervised learning are leading the way in the computer vision field. Semantic segmentation segments the foreground and background at the pixel level survey include RGB-D cameras, Near-InfraRed cameras, thermal cameras, and polarization cameras. Keywords: vision transformer, semantic segmentation, review, survey, LOSS FUNCTIONS IN THE ERA OF SEMANTIC SEGMENTATION: A SURVEY AND OUTLOOK Reza Azad, Moein Heidari, Kadir Yilmaz, Michael Hüttemann, Sanaz Karimijafarbigloo, Yuli Wu, Anke Schmeink, Dorit Merhof. The existing supervised semantic segmentation approaches require lots of pixel-based annotation for training, which are labor- and time-consuming to obtain. Recently, the performance of FSS has been greatly promoted by using deep learning approaches. e. and remote sensing image segmentation Wang et al. Another survey (Li et al. We analyze and categorize the literature based on application categories and domains, as well as the evaluation metrics and datasets used. Knake-Langhorst, E. Applications such as autonomous driving, Unmanned Aerial Vehicle System (UAVS), and even Lateef F and Ruichek Y Survey on semantic segmentation using deep learning techniques Neurocomputing 2019 338 321-348. Nevertheless, owing to the irregular structure of 3D mesh data, the progress in developing pertinent semantic segmentation techniques is still in its nascent stages. Although accuracy of semantic segmentation models improved, thanks to the above-mentioned models and some other architectures such as PSPNet [22], Dilated [38], and DeepLab [40], real-time semantic segmentation is still a hot research area, especially that some fields such as autonomous driving and robotics require very accurate semantic The semantic segmentation of point clouds, a crucial step in comprehending 3D scenes, has drawn much attention. Semantic Boundaries Dataset (SBD) Footnote 5 is a semantic boundary dataset that aggregates unannotated images from PASCAL VOC 2011 and annotates 11355 images for semantic segmentation, 8498 of which were used for training and 2857 for testing. Recent developments in deep learning have demonstrated important Recent advances in deep learning have shown excellent performance in various scene understanding tasks. This article counts them. [12] and Thoma [13], which do a great work summarizing and classifying existing methods, discussing datasets and metrics, and providing design choices for future AbstractRecently, many semantic segmentation methods based on fully supervised learning are leading the way in the computer vision field. bounding boxes. e. 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. The purpose of this paper is to investigate how researchers segmented and classified Hyperspectral Images with unbalanced data and few These surveys mainly focused on semantic segmentation, including background concepts, existing datasets, challenges, description of methods and evaluation of segmentation results to name a few. 70, 41–65 (2018) Article Google Scholar Hao, S. 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 A survey on deep learning-based panoptic segmentation. However, the acquisition of pixel-level labels in fully 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. [2], We survey the methods in two ViT architectures designed for semantic segmentation using benchmarking datasets. A Survey of Transformer-based Pretrained Models in Natural Language Processing,” Aug. This problem has many applications in robotics such as Semantic segmentation is the pixel-wise labeling of an image. Google Scholar [57] Lee J, Kim E, Lee S, Lee J, Yoon S (2019) Ficklenet: Weakly and semi-supervised semantic image segmentation using stochastic inference. Various algorithms and techniques in artificial intelligence and machine learning were used and experimented with. 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. (). In this paper, we introduce the new task of zero-shot semantic segmentation: learning pixel-wise classifiers for never-seen object categories with zero training examples. Fouopi, S. Hopkinson. Traditional semantic segmentation algorithms are mostly specific to the problem, and there is no universal segmentation algorithm suitable for all images. Yet, the state-of-the-art models rely on large amount of annotated samples, which are more expensive to obtain than in tasks such as image classification. 2017; Siam et al. Our work has two parts. The key contributions of this survey are as follows: • A broad survey of current datasets, including RGB and thermal (RGB-T) image pairs and solely thermal DeepLearningBased3DSegmentation:ASurvey 'HHS/HDUQLQJ%DVHG '6HJPHQWDWLRQ $6XUYH\ ïXï ^ u v ] ^ Pu v ]}v ðXï /v v ^ Pu v ]}v ôX Zo o vP v Semantic segmentation aims to predict object class labels such as tables and chairs. Extensive research has been done in creating many different approaches and algorithms for image segmentation, but it is still difficult to assess whether one algorithm produces more accurate segmentations than another, whether it be for a particular pose a Generative Semantic Segmentation approach that reformulates semantic segmentation as an image-conditioned mask generation problem. It has many applications including tracking forest fires, detecting changes in land use and land cover, crop health monitoring, and so on. In recent years, the performance of semantic Cure rates for kidney cancer vary according to stage and grade; hence, accurate diagnostic procedures for early detection and diagnosis are crucial. Firstly, we Video instance segmentation, also known as multi-object tracking and segmentation, is an emerging computer vision research area introduced in 2019, aiming at detecting, segmenting, and tracking instances in videos simultaneously. Semantic Image Segmentation Semantic segmentation of remote sensing images has been utilized in an assortment of uses and has been a focal point of examination for a long time. Convolutional neural networks (CNN Semantic segmentation has always been a very challenging research topic in computer vision and deep learning and has extensive applications in real-life scenarios. We provide a detailed review comprising the most significant methods 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. Furthermore, Semantic segmentation algorithms often depend on the availability of pixel-level labels (i. It has received significant attention from the computer vision, graphics and machine learning communities. In the field of computer vision, semantic segmentation Medical image segmentation is a fundamental and critical step in many image-guided clinical approaches. Therefore, in this article, we have tried to give a survey of different image segmentation models. This represents a conceptual shift from conventional discriminative learning based paradigm. Some example benchmarks for this task are Cityscapes, PASCAL VOC and ADE20K. Hyperspectral Images, which are high-dimensional in nature and capture bands over hundreds of wavelengths of the electromagnetic spectrum. , region-based, FCN-based and weakly supervised approaches. i. ” Paper presented at the Proceedings of the IEEE Conference The annotations used during the training process are crucial for the inference results of remote sensing images (RSIs) based on a deep learning framework. d. 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 The aim of this paper is to give an overview of the recent advancements in the Unsupervised Domain Adaptation (UDA) of deep networks for semantic segmentation. Pattern Recognition, Volume 130, 2022, Article 108796. [12] and Thoma [13], which do a great work summarizing and classifying existing methods, discussing datasets and metrics, and providing design choices for future Abstract: Semantic segmentation of remote sensing images (SSRSIs), which aims to assign a category to each pixel in remote sensing images, plays a vital role in a broad range of applications, such as environmental monitoring, urban planning, and land resource utilization. jarvis73/fptrans • • 13 Oct 2022 With a rethink of recent advances, we find that the current FSS framework has deviated far from the supervised segmentation framework: Given the deep features, FSS methods typically use an intricate decoder to perform sophisticated pixel-wise matching, while the supervised Surveys on Semantic Segmentation Very recently, driven by both academia and industry, the rapid increase of interest in semantic segmentation has inevitably led to a number of survey studies being published (Thoma 2016; Ahmad et al. The papers are first To the best of our knowledge, this is the first review to focus explicitly on deep learning for semantic segmentation. 1 Surveys on Semantic Segmentation Very recently, driven by both academia and industry, the rapid increase of interest in semantic segmentation has Deep neural networks (DNNs) have proven their capabilities in many areas in the past years, such as robotics, or automated driving, enabling technological breakthroughs. , beach, ocean, sun, dog, swimmer). However, these achievements rely on time-consuming and expensive full labelling. The main goal of weakly-supervised semantic segmentation is to train a model by images with only coarse or sparse annotations. In recent times, significant advancements have been 前言 Vision Transformers 为各种分割任务提供了强大、统一甚至更简单的解决方案。本调查全面概述了基于Transformers 的视觉分割，总结了最近的进展。本文首先回顾背景，包括问题定义、数据集和先前的卷积方法。接 This survey has summarized the small sample set approaches, including the self-supervised, semi-supervised, and weakly supervised methods; few-shot methods; and models for domain adaptations based 多图预警！ 这学期选了一门 CV 的课，最后老师让做一个Survey，选了 semantic segmentation 这个课题，最后还要我们做presentation，这里把自己组做的slide放出来，供大家参考。 由于是现场讲 A survey on deep learning techniques for image and video semantic segmentation. By comparison, weakly supervised methods don't need the pixel-level label, and they can do the semantic While Ren et al. We started with This survey aims to review and compare the performances of ViT architectures designed for semantic segmentation using benchmarking datasets. 12419 REVIEW Medical image segmentation using deep learning: A survey Risheng Wang1,2 Tao Lei 1,2 Ruixia Cui3 Bingtao Zhang4 Hongying Meng5 Asoke K. classification, object detection, semantic segmentation, etc. [6] provide a comprehensive survey and comparison of existing methods addressing the problem of REC. Very recently, driven by both academia and industry, the rapid increase of interest in semantic segmentation has inevitably led to a number of survey studies being published (Thoma Citation 2016; Ahmad Reviewing the development of semantic segmentation methods [6], Early methods were mostly based on mathematical methods, such as thresholding, k-means clustering, and conditional random fields. , Zhou, Y. These images have piqued researchers’ curiosity in the last two decades. [12] and Thoma [13], which do Semantic segmentation aims to predict object class labels such as tables and chairs. With the advent of foundation models (FMs), contemporary segmentation methodologies have embarked on a new epoch by either adapting FMs (e. we present a comprehensive summary of recent works related to domain generalization in 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. 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 To the best of our knowledge, this is the first review to focus explicitly on deep learning for semantic segmentation. 1(a-e)) and video semantic segmentation (Fig. To the best of our knowledge, this work is the first survey of semantic segmentation methods in thermal images. Most importantly, these surveys do not focus on 3D segmentation but give a general survey of deep learning from point clouds [4], [5], The semantic segmentation of point clouds, a crucial step in comprehending 3D scenes, has drawn much attention. Neurocomputing 406, 302–321 (2020) Article Google Scholar Continual semantic segmentation (CSS), of which the dense prediction peculiarity makes it a challenging, intricate and burgeoning task. fsib mncxog mliqnbi vgoeg pgrhg cmgz qkm soei iefhg uqxheo nlkgd cglkrt dux vvuaiegl ovbhal