Semantic Segmentation Framework Research Articles

Research on LiDAR point cloud data transformation method based on weighted altitude difference map

Road surface detection plays a pivotal role in the realm of autonomous vehicle navigation. Contemporary methodologies primarily leverage LiDAR for acquiring three-dimensional data and utilize imagery for chromatic information. However, these approaches encounter significant integration challenges, particularly due to the inherently unstructured nature of 3D point clouds. Addressing this, our novel algorithm, specifically tailored for predicting drivable areas, synergistically combines LiDAR point clouds with bidimensional imagery. Initially, it constructs an altitude discrepancy map via LiDAR, capitalizing on the height uniformity characteristic of planar road surfaces. Subsequently, we introduce an innovative and more efficacious attention mechanism, streamlined for image feature extraction. This mechanism employs adaptive weighting coefficients for the amalgamation of the altitude disparity imagery and two-dimensional image features, thereby facilitating road area delineation within a semantic segmentation framework. Empirical evaluations conducted using the KITTI dataset underscore our methodology’s superior road surface discernment and extraction precision, substantiating the efficacy of our proposed network architecture and data processing paradigms. This research endeavor seeks to propel the advancement of three-dimensional perception technology in the autonomous driving domain.

Privacy-Preserving Synthetic Continual Semantic Segmentation for Robotic Surgery.

Deep Neural Networks (DNNs) based semantic segmentation of the robotic instruments and tissues can enhance the precision of surgical activities in robot-assisted surgery. However, in biological learning, DNNs cannot learn incremental tasks over time and exhibit catastrophic forgetting, which refers to the sharp decline in performance on previously learned tasks after learning a new one. Specifically, when data scarcity is the issue, the model shows a rapid drop in performance on previously learned instruments after learning new data with new instruments. The problem becomes worse when it limits releasing the dataset of the old instruments for the old model due to privacy concerns and the unavailability of the data for the new or updated version of the instruments for the continual learning model. For this purpose, we develop a privacy-preserving synthetic continual semantic segmentation framework by blending and harmonizing (i) open-source old instruments foreground to the synthesized background without revealing real patient data in public and (ii) new instruments foreground to extensively augmented real background. To boost the balanced logit distillation from the old model to the continual learning model, we design overlapping class-aware temperature normalization (CAT) by controlling model learning utility. We also introduce multi-scale shifted-feature distillation (SD) to maintain long and short-range spatial relationships among the semantic objects where conventional short-range spatial features with limited information reduce the power of feature distillation. We demonstrate the effectiveness of our framework on the EndoVis 2017 and 2018 instrument segmentation dataset with a generalized continual learning setting. Code is available at https://github.com/XuMengyaAmy/Synthetic_CAT_SD.

Two-dimensional hybrid incremental learning (2DHIL) framework for semantic segmentation of skin tissues

This study aims to enhance the robustness and generalization capability of a deep learning transformer model used for segmenting skin carcinomas and tissues through the introduction of incremental learning. Deep learning AI models demonstrate their claimed performance only for tasks and data types for which they are specifically trained. Their performance is severely challenged for the test cases which are not similar to training data thus questioning their robustness and ability to generalize. Moreover, these models require an enormous amount of annotated data for training to achieve desired performance. The availability of large annotated data, particularly for medical applications, is itself a challenge. Despite efforts to alleviate this limitation through techniques like data augmentation, transfer learning, and few-shot training, the challenge persists. To address this, we propose refining the models incrementally as new classes are discovered and more data becomes available, emulating the human learning process. However, deep learning models face the challenge of catastrophic forgetting during incremental training. Therefore, we introduce a two-dimensional hybrid incremental learning framework for segmenting non-melanoma skin cancers and tissues from histopathology images. Our approach involves progressively adding new classes and introducing data of varying specifications to introduce adaptability in the models. We also employ a combination of loss functions to facilitate new learning and mitigate catastrophic forgetting. Our extended experiments demonstrate significant improvements, with an F1 score reaching 91.78, mIoU of 93.00, and an average accuracy of 95%. These findings highlight the effectiveness of our incremental learning strategy in enhancing the robustness and generalization of deep learning segmentation models while mitigating catastrophic forgetting.

Advancing zero-shot semantic segmentation through attribute correlations

Zero-shot semantic segmentation aims to segment novel classes that have not been encountered during the training phase. Existing methods leverage available text features obtained from pretrained language models to produce semantic segmentation results for both base and novel classes. However, the text-based feature-producing paradigm only provides insufficient class correlations and limits the full exploitation of image features from base classes. Besides, there exists a non-negligible domain gap between the text and image domains, resulting in severe feature bias during feature production. Different from existing methods, we advance the zero-shot semantic segmentation through attribute correlations. Specifically, we introduce a set of shared-attribute labels, of which the design fully considers the structural relations between attributes and classes, to provide rational and sufficient attribute-class correlations. Besides, due to the minor intra-class variations of shared attributes, the text features are more easily mapped to image features, thereby alleviating the domain gap issue. Furthermore, we propose a hierarchical semantic segmentation framework incorporating an attribute prompt tuning method. This approach is designed to enhance the model’s adaptation to the attribute segmentation task and effectively leverage attribute features to produce better semantic segmentation results. Correspondingly, we construct a Visual Hierarchical Semantic Classes (VHSC) benchmark, meticulously annotating shared-attributes at the pixel level to conduct the experiments. Extensive experiments on the VHSC benchmark showcase the superior performance of our method compared to existing zero-shot semantic segmentation methods, achieving mIoU of 73.0% and FBIoU of 87.5%. The VHSC benchmark and our code are available at https://github.com/RuntongZHANG/ZeroShot-VHSC.

Bridging knowledge distillation gap for few-sample unsupervised semantic segmentation

Due to privacy, security, and costly labeling of images, unsupervised semantic segmentation with very few samples has become a promising direction, but still remains unexplored. This inspires us to introduce the few-sample unsupervised semantic segmentation task, which is very challenging because generalizing the segmentation model from only a few unlabeled images is far from sufficient. We address this problem in the knowledge distillation perspective, by proposing a medium-sized auxiliary network as the bridge, which narrows down the semantic knowledge gap between teacher network (large) and student network (small). To this end, we develop the Knowledge Distillation Bridge (KDB) framework for few-sample unsupervised semantic segmentation. In particular, it consists of the teacher-auxiliary-student architecture, which adopts the block-wise distillation that encourages the auxiliary to imitate the teacher and the student to imitate the auxiliary. In this way, the knowledge gap between the source feature distribution and the target one is reduced, allowing the student with the smaller network to be readily deployed in highly-demanding environment. Meanwhile, each channel characterizes different semantics in feature map, which motivates us to distill the features of decoder in a channel-wise manner. Extensive experiments on two benchmarks including Pascal VOC2012 and Cityscapes demonstrate the promising performance of the proposed method, which strikes a good balance between precision and speed, e.g., it achieves the inference speed of 230 fps for a 512×512 image.

Remote sensing image semantic segmentation via class-guided structural interaction and boundary perception

Existing remote sensing semantic segmentation methods generally ignore the structural information of objects that is vital in the human visual recognition system. The absence of overall structural information often results in weak perceptions of subtle textures and fragmented predictions, especially for complex and variable ground object scenarios. Besides, they still suffer from the semantic ambiguity caused by the unclear object boundary features in remote sensing images. In this paper, we propose a novel remote sensing semantic segmentation framework, called CSBNet, which aims to enhance the capacity of class-guided structural interaction and boundary perception simultaneously. It consists of a class-guided structure interaction module (CSIM), a Transformer-based context aggregation module (TCAM) and a class-guided boundary supervision module (CBSM). The CSIM has the ability to progressively extract the class-specific structural features, i.e., refining the structural information of each class by iteratively exchanging information between initial coarse class tokens and contexts. Meanwhile, the TCAM is constructed to provide CSIM with more discriminative multi-scale contexts without losing spatial features. In particular, the CBSM plays an auxiliary role, which applies the boundary information obtained from the class tokens to supervise the segmentation of boundary regions. When tested on the ISPRS dataset, LoveDA dataset, UAVid dataset, our method significantly outperforms the state-of-the-art remote sensing semantic segmentation approaches.

Attentional adversarial training for few-shot medical image segmentation without annotations.

Medical image segmentation is a critical application that plays a significant role in clinical research. Despite the fact that many deep neural networks have achieved quite high accuracy in the field of medical image segmentation, there is still a scarcity of annotated labels, making it difficult to train a robust and generalized model. Few-shot learning has the potential to predict new classes that are unseen in training with a few annotations. In this study, a novel few-shot semantic segmentation framework named prototype-based generative adversarial network (PG-Net) is proposed for medical image segmentation without annotations. The proposed PG-Net consists of two subnetworks: the prototype-based segmentation network (P-Net) and the guided evaluation network (G-Net). On one hand, the P-Net as a generator focuses on extracting multi-scale features and local spatial information in order to produce refined predictions with discriminative context between foreground and background. On the other hand, the G-Net as a discriminator, which employs an attention mechanism, further distills the relation knowledge between support and query, and contributes to P-Net producing segmentation masks of query with more similar distributions as support. Hence, the PG-Net can enhance segmentation quality by an adversarial training strategy. Compared to the state-of-the-art (SOTA) few-shot segmentation methods, comparative experiments demonstrate that the proposed PG-Net provides noticeably more robust and prominent generalization ability on different medical image modality datasets, including an abdominal Computed Tomography (CT) dataset and an abdominal Magnetic Resonance Imaging (MRI) dataset.

A multi-resolution self-supervised learning framework for semantic segmentation in histopathology

Modern whole slide imaging technique together with supervised deep learning approaches have been advancing the field of histopathology, enabling accurate analysis of tissues. These approaches use whole slide images (WSIs) at various resolutions, utilising low-resolution WSIs to identify regions of interest in the tissue and high-resolution for detailed analysis of cellular structures. Due to the labour-intensive process of annotating gigapixels WSIs, accurate analysis of WSIs remains challenging for supervised approaches. Self-supervised learning (SSL) has emerged as an approach to build efficient and robust models using unlabelled data. It has been successfully used to pre-train models to learn meaningful image features which are then fine-tuned with downstream tasks for improved performance compared to training models from scratch. Yet, existing SSL methods optimised for WSI are unable to leverage the multi-resolutions and instead, work only in an individual resolution neglecting the hierarchical structure of multi-resolution inputs. This limitation prevents from the effective utilisation of complementary information between different resolutions, hampering discriminative WSI representation learning. In this paper we propose a Multi-resolution SSL Framework for WSI semantic segmentation (MSF-WSI) that effectively learns histopathological features. Our MSF-WSI learns complementary information from multiple WSI resolutions during the pre-training stage; this contrasts with existing works that only learn between the resolutions at the fine-tuning stage. Our pre-training initialises the model with a comprehensive understanding of multi-resolution features which can lead to improved performance in the subsequent tasks. To achieve this, we introduced a novel Context-Target Fusion Module (CTFM) and a masked jigsaw pretext task to facilitate the learning of multi-resolution features. Additionally, we designed Dense SimSiam Learning (DSL) strategy to maximise the similarities of image features from early model layers to enable discriminative learned representations. We evaluated our method using three public datasets on breast and liver cancer segmentation tasks. Our experiment results demonstrated that our MSF-WSI surpassed the accuracy of other state-of-the-art methods in downstream fine-tuning and semi-supervised settings.

A three in one bottom-up framework for simultaneous semantic segmentation, instance segmentation and classification of multi-organ nuclei in digital cancer histology

Simultaneous segmentation and classification of nuclei in digital histology remains challenging. The highest achieved Panoptic Quality (PQ) remains low due to overlapping nuclei, higher staining and tissue variability, and rough clinical conditions. The generic deep-learning methods usually rely on end-to-end models, which fail to address these problems associated explicitly with digital histology. We resolve these issues using a dual attention-based model combined with post-processing in a bottom-up fashion. We use three attention decoder heads, which produce semantic segmentation, edge proposals, and classification maps. We use these outputs to apply post-processing, including controlled watershed and pixel grouping, to produce instance segmentation and classification. Our multi-stage approach utilizes edge proposals and semantic segmentations compared to direct segmentation and classification strategies followed by most generic state-of-the-art methods. Due to this, we demonstrate a significant performance improvement in producing high-quality instance segmentation and nuclei classification. We have achieved a 0.841 Dice score for semantic segmentation, 0.713 bPQ scores for instance segmentation, and 0.633 mPQ for nuclei classification. Furthermore, the framework is less complex compared to the state-of-the-art.

An interactive method for bridging the gap between deep learning based building contour segmentation and manual annotation

ABSTRACT Although the emergence of deep learning has improved the performance of automatic building extraction, there is still a long way to go before it can completely replace the labour-intensive manual delineation of building contours. To narrow the gap between building extraction results of deep learning methods and manual-level annotations, this paper introduces an interactive semantic segmentation framework that uses manual clicks as interactive information to guide the process of semantic segmentation towards the manual annotation level. In our framework, we first use an interactive semantic segmentation network for coarse building extraction from high resolution remote sensing images. Then, we use an optimization network to further refine the extraction results. We comprehensively compare the automatic deep learning methods, the proposed interactive building extraction framework, and the full manual delineation in practical experimental settings. First, our interactive method can significantly improve the performance of automatic building extraction. Second, by comparing the efficiency of manual annotation using the ArcGIS software and our interactive method, it is found that the proposed method can save more than half of the time. The above results show the potential of the interactive method in improving the efficiency of contour annotation and reducing the cost of manual annotation.

SwinUNeLCsT: Global–local spatial representation learning with hybrid CNN–transformer for efficient tuberculosis lung cavity weakly supervised semantic segmentation

Radiological diagnosis of lung cavities (LCs) is the key to identifying tuberculosis (TB). Conventional deep learning methods rely on a large amount of accurate pixel-level data to segment LCs. This process is time-consuming and laborious, especially for those subtle LCs. To address such challenges, firstly, we introduce a novel 3D TB LCs imaging convolutional neural network (CNN)-transformer hybrid model (SwinUNeLCsT). The core idea of SwinUNeLCsT is to combine local details and global dependencies for TB CT scan image feature representation to effectively improve the recognition ability of LCs. Secondly, to reduce the dependence on accurate pixel-level annotations, we design an end-to-end LCs weakly supervised semantic segmentation (WSSS) framework. Through this framework, radiologists need only to classify the number and the approximate location (e.g., left lung, right lung, or both) of LCs in the CT scan to achieve efficient segmentation of the LCs. This process eliminates the need for meticulously drawing boundaries, greatly reducing the cost of annotation. Extensive experimental results show that SwinUNeLCsT outperforms currently popular medical 3D segmentation methods in the supervised semantic segmentation paradigm. Meanwhile, our WSSS framework based on SwinUNeLCsT also performs best among the existing state-of-the-art medical 3D WSSS methods.

Large-scale Point Cloud Segmentation based on Multi-feature Local Enhanced Fusion

This paper introduces a framework for large-scale 3D point cloud semantic segmentation - the MLEF-Net model. The model aims to improve the segmentation accuracy of large-scale point clouds by innovatively combining Manhattan distance-based KNN neighborhood search with feature aggregation techniques. This approach uniquely handles spatial, color, and normal vector attributes, thereby improving the segmentation results. The superiority of the model is validated through comprehensive testing on the SemanticKITTI and nuScenes datasets, demonstrating its potential to enhance point cloud segmentation through advanced feature fusion strategies.

A new framework for improving semantic segmentation in aerial imagery

High spatial resolution (HSR) remote sensing imagery presents a rich tapestry of foreground-background intricacies, rendering semantic segmentation in aerial contexts a formidable and vital undertaking. At its core, this challenge revolves around two pivotal questions: 1) Mitigating Background Interference and Enhancing Foreground Clarity. 2) Accurate Segmentation in Dense Small Object Cluster. Conventional semantic segmentation methods primarily cater to the segmentation of large-scale objects in natural scenes, yet they often falter when confronted with aerial imagery’s characteristic traits such as vast background areas, diminutive foreground objects, and densely clustered targets. In response, we propose a novel semantic segmentation framework tailored to overcome these obstacles. To address the first challenge, we leverage PointFlow modules in tandem with the Foreground-Scene (F-S) module. PointFlow modules act as a barrier against extraneous background information, while the F-S module fosters a symbiotic relationship between the scene and foreground, enhancing clarity. For the second challenge, we adopt a dual-branch structure termed disentangled learning, comprising Foreground Precedence Estimation and Small Object Edge Alignment (SOEA). Our foreground saliency guided loss optimally directs the training process by prioritizing foreground examples and challenging background instances. Extensive experimentation on the iSAID and Vaihingen datasets validates the efficacy of our approach. Not only does our method surpass prevailing generic semantic segmentation techniques, but it also outperforms state-of-the-art remote sensing segmentation methods.

FishSegSSL: A Semi-Supervised Semantic Segmentation Framework for Fish-Eye Images.

The application of large field-of-view (FoV) cameras equipped with fish-eye lenses brings notable advantages to various real-world computer vision applications, including autonomous driving. While deep learning has proven successful in conventional computer vision applications using regular perspective images, its potential in fish-eye camera contexts remains largely unexplored due to limited datasets for fully supervised learning. Semi-supervised learning comes as a potential solution to manage this challenge. In this study, we explore and benchmark two popular semi-supervised methods from the perspective image domain for fish-eye image segmentation. We further introduce FishSegSSL, a novel fish-eye image segmentation framework featuring three semi-supervised components: pseudo-label filtering, dynamic confidence thresholding, and robust strong augmentation. Evaluation on the WoodScape dataset, collected from vehicle-mounted fish-eye cameras, demonstrates that our proposed method enhances the model's performance by up to 10.49% over fully supervised methods using the same amount of labeled data. Our method also improves the existing image segmentation methods by 2.34%. To the best of our knowledge, this is the first work on semi-supervised semantic segmentation on fish-eye images. Additionally, we conduct a comprehensive ablation study and sensitivity analysis to showcase the efficacy of each proposed method in this research.

Multi-Scale Feature Fusion Network with Symmetric Attention for Land Cover Classification Using SAR and Optical Images

The complementary characteristics of SAR and optical images are beneficial in improving the accuracy of land cover classification. Deep learning-based models have achieved some notable results. However, how to effectively extract and fuse the unique features of multi-modal images for pixel-level classification remains challenging. In this article, a two-branch supervised semantic segmentation framework without any pretrained backbone is proposed. Specifically, a novel symmetric attention module is designed with improved strip pooling. The multiple long receptive fields can better perceive irregular objects and obtain more anisotropic contextual information. Meanwhile, to solve the semantic absence and inconsistency of different modalities, we construct a multi-scale fusion module, which is composed of atrous spatial pyramid pooling, varisized convolutions and skip connections. A joint loss function is introduced to constrain the backpropagation and reduce the impact of class imbalance. Validation experiments were implemented on the DFC2020 and WHU-OPT-SAR datasets. The proposed model achieved the best quantitative values on the metrics of OA, Kappa and mIoU, and its class accuracy was also excellent. It is worth mentioning that the number of parameters and the computational complexity of the method are relatively low. The adaptability of the model was verified on RGB–thermal segmentation task.

COMBINING IMAGE AND POINT CLOUD SEGMENTATION TO IMPROVE HERITAGE UNDERSTANDING

Abstract. Current 2D and 3D semantic segmentation frameworks are developed and trained on specific benchmark datasets, often rich of synthetic data, and when they are applied to complex and real-world heritage scenarios they offer much lower accuracy than expected. In this work, we present and demonstrate an early and late fusion of methods for semantic segmentation in cultural heritage applications. We rely on image datasets, point clouds and BIM models. The early fusion utilizes multi-view rendering to generate RGBD imagery of the scene. In contrast, the late fusion approach merges image-based segmentation with a Point Transformer applied to point clouds. Two scenarios are considered and inference results show that predictions are primarily influenced by whether the scene has a predominantly geometric or texture-based signature, underscoring the necessity of fusion methods.

Subdivided Mask Dispersion Framework for semi-supervised semantic segmentation

Learning the relationship between weak and strong perturbations has been considered a major part of semi-supervised semantic segmentation. We observed two problems with a publicly used perturbation method, which randomly generates a mask with a single large bounding box. The large single bounding box that entirely covers the important object components in an image, hindering the model from capturing partial object information. Furthermore, training the model with a single large bounding box as an image-level perturbation causes the model to be biased towards the shape of the large squared box, rather than the deformable object component shapes. In this paper, we propose Subdivided Mask Dispersion Framework (SMDF) to solve these problems. Our framework disperses the large squared box into small multi-scale boxes, capturing the crucial multi-scaled object components in the image. SMDF achieves state-of-the-art performance on five data partitions of PASCAL dataset and three partitions of Extended SBD dataset. Our extensive ablation studies show the effectiveness of dispersed small multi-scale bounding boxes in semi-supervised semantic segmentation.

Swin-Net: A Swin-Transformer-Based Network Combing with Multi-Scale Features for Segmentation of Breast Tumor Ultrasound Images

Breast cancer is one of the most common cancers in the world, especially among women. Breast tumor segmentation is a key step in the identification and localization of the breast tumor region, which has important clinical significance. Inspired by the swin-transformer model with powerful global modeling ability, we propose a semantic segmentation framework named Swin-Net for breast ultrasound images, which combines Transformer and Convolutional Neural Networks (CNNs) to effectively improve the accuracy of breast ultrasound segmentation. Firstly, our model utilizes a swin-transformer encoder with stronger learning ability, which can extract features of images more precisely. In addition, two new modules are introduced in our method, including the feature refinement and enhancement module (RLM) and the hierarchical multi-scale feature fusion module (HFM), given that the influence of ultrasonic image acquisition methods and the characteristics of tumor lesions is difficult to capture. Among them, the RLM module is used to further refine and enhance the feature map learned by the transformer encoder. The HFM module is used to process multi-scale high-level semantic features and low-level details, so as to achieve effective cross-layer feature fusion, suppress noise, and improve model segmentation performance. Experimental results show that Swin-Net performs significantly better than the most advanced methods on the two public benchmark datasets. In particular, it achieves an absolute improvement of 1.4–1.8% on Dice. Additionally, we provide a new dataset of breast ultrasound images on which we test the effect of our model, further demonstrating the validity of our method. In summary, the proposed Swin-Net framework makes significant advancements in breast ultrasound image segmentation, providing valuable exploration for research and applications in this domain.

Enhancing Semi-Supervised Semantic Segmentation of Remote Sensing Images via Feature Perturbation-Based Consistency Regularization Methods

In the field of remote sensing technology, the semantic segmentation of remote sensing images carries substantial importance. The creation of high-quality models for this task calls for an extensive collection of image data. However, the manual annotation of these images can be both time-consuming and labor-intensive. This has catalyzed the advent of semi-supervised semantic segmentation methodologies. Yet, the complexities inherent within the foreground categories of these remote sensing images present challenges in preserving prediction consistency. Moreover, remote sensing images possess more complex features, and different categories are confused within the feature space, making optimization based on the feature space challenging. To enhance model consistency and to optimize feature-based class categorization, this paper introduces a novel semi-supervised semantic segmentation framework based on Mean Teacher (MT). Unlike the conventional Mean Teacher that only introduces perturbations at the image level, we incorporate perturbations at the feature level. Simultaneously, to maintain consistency after feature perturbation, we employ contrastive learning for feature-level learning. In response to the complex feature space of remote sensing images, we utilize entropy threshold to assist contrastive learning, selecting feature key-values more precisely, thereby enhancing the accuracy of segmentation. Extensive experimental results on the ISPRS Potsdam dataset and the challenging iSAID dataset substantiate the superior performance of our proposed methodology.

Korean Pansori Vocal Note Transcription Using Attention-Based Segmentation and Viterbi Decoding

In this paper, first, we delved into the experiment by comparing various attention mechanisms in the semantic pixel-wise segmentation framework to perform frame-level transcription tasks. Second, the Viterbi algorithm was utilized by transferring the knowledge of the frame-level transcription model to obtain the vocal notes of Korean Pansori. We considered a semantic pixel-wise segmentation framework for frame-level transcription as the source task and a Viterbi algorithm-based Korean Pansori note-level transcription as the target task. The primary goal of this paper was to transcribe the vocal notes of Pansori music, a traditional Korean art form. To achieve this goal, the initial step involved conducting the experiments with the source task, where a trained model was employed for vocal melody extraction. To achieve the desired vocal note transcription for the target task, the Viterbi algorithm was utilized with the frame-level transcription model. By leveraging this approach, we sought to accurately transcribe the vocal notes present in Pansori performances. The effectiveness of our attention-based segmentation methods for frame-level transcription in the source task has been compared with various algorithms using the vocal melody task of the MedleyDB dataset, enabling us to measure the voicing recall, voicing false alarm, raw pitch accuracy, raw chroma accuracy, and overall accuracy. The results of our experiments highlight the significance of attention mechanisms for enhancing the performance of frame-level music transcription models. We also conducted a visual and subjective comparison to evaluate the results of the target task for vocal note transcription. Since there was no ground truth vocal note for Pansori, this analysis provides valuable insights into the preservation and appreciation of this culturally rich art form.