Semantic Object Research Articles

Class key feature extraction and fusion for 2D medical image segmentation.

The size variation, complex semantic environment and high similarity in medical images often prevent deep learning models from achieving good performance. To overcome these problems and improve the model segmentation performance and generalizability. We propose the key class feature reconstruction module (KCRM), which ranks channel weights and selects key features (KFs) that contribute more to the segmentation results for each class. Meanwhile, KCRM reconstructs all local features to establish the dependence relationship from local features to KFs. In addition, we propose the spatial gating module (SGM), which employs KFs to generate two spatial maps to suppress irrelevant regions, strengthening the ability to locate semantic objects. Finally, we enable the model to adapt to size variations by diversifying the receptive field. We integrate these modules into class key feature extraction and fusion network (CKFFNet) and validate its performance on three public medical datasets: CHAOS, UW-Madison, and ISIC2017. The experimental results show that our method achieves better segmentation results and generalizability than those of mainstream methods. Through quantitative and qualitative research, the proposed module improves the segmentation results and enhances the model generalizability, making it suitable for application and expansion.

“I hate all the children, especially mine”: Applying relational dialectics theory to examine the experiences of formerly childfree regretful parents

Guided by relational dialectics theory (RDT), we analyzed 85 first-person testimonials of parental regret written by users of the /r/childfree subreddit. We interrogated how competing discourses animate what it means to be a parent (our semantic object). Contrapuntal analysis revealed dominant and marginalized Discourses of Parenting as Heaven (DPHN) and Parenting as Hell on Earth (DPHL), respectively, as well as a third Discourse of Parenting as (the Only) Choice (DPOC). We identified three kinds of dialogically contractive practices including a new form we call fear of missing out (FOMO), two forms of diachronic separation, four forms of synchronic interplay including a new form we introduce (i.e., allying) that is useful when more than two discourses compete, and one form of dialogic transformation. We argue that FOMO serves as a catalyst for diachronic separation. We offer practical implications for fencesitters (those “on the fence” about having children), counselors, and policymakers.

ArrangementNet: Learning Scene Arrangements for Vectorized Indoor Scene Modeling

We present a novel vectorized indoor modeling approach that converts point clouds into building information models (BIM) with concise and semantically segmented polygonal meshes. Existing methods detect planar shapes and connect them to complete the scene. Some focus on floor plan reconstruction as a simplified problem to better analyze connectivity between planes of floors and walls. However, the connectivity analysis is still challenging and ill-posed with incomplete point clouds as input. We propose ArrangementNet to estimate scene arrangements from an incomplete point cloud, which we can easily convert into a BIM model. ArrangementNet is a novel graph neural network that consumes noisy over-partitioned initial arrangements extracted through non-learning techniques and outputs high-quality scene arrangement. The core of ArrangementNet is an extended graph convolution that leverages co-linear and co-face relationships in the arrangement and improves the quality of prediction in complex scenes. We apply ArrangementNet to improve floor plan and ceiling arrangements and enrich them with semantic objects as scene arrangements for scene generation. Our approach faithfully models challenging scenes obtained from laser scans or multiview stereo and shows significant improvement in BIM model reconstruction compared to the state-of-the-art. Our code is available at https://github.com/zssjh/ArrangementNet.

Free viewing biases for complex scenes in preschoolers and adults

Adult gaze behaviour towards naturalistic scenes is highly biased towards semantic object classes. Little is known about the ontological development of these biases, nor about group-level differences in gaze behaviour between adults and preschoolers. Here, we let preschoolers (n = 34, age 5 years) and adults (n = 42, age 18–59 years) freely view 40 complex scenes containing objects with different semantic attributes to compare their fixation behaviour. Results show that preschool children allocate a significantly smaller proportion of dwell time and first fixations on Text and instead fixate Faces, Touched objects, Hands and Bodies more. A predictive model of object fixations controlling for a range of potential confounds suggests that most of these differences can be explained by drastically reduced text salience in pre-schoolers and that this effect is independent of low-level salience. These findings are in line with a developmental attentional antagonism between text and body parts (touched objects and hands in particular), which resonates with recent findings regarding ‘cortical recycling’. We discuss this and other potential mechanisms driving salience differences between children and adults.

SPA-Net: A Deep Learning Approach Enhanced Using a Span-Partial Structure and Attention Mechanism for Image Copy-Move Forgery Detection.

With the wide application of visual sensors and development of digital image processing technology, image copy-move forgery detection (CMFD) has become more and more prevalent. Copy-move forgery is copying one or several areas of an image and pasting them into another part of the same image, and CMFD is an efficient means to expose this. There are improper uses of forged images in industry, the military, and daily life. In this paper, we present an efficient end-to-end deep learning approach for CMFD, using a span-partial structure and attention mechanism (SPA-Net). The SPA-Net extracts feature roughly using a pre-processing module and finely extracts deep feature maps using the span-partial structure and attention mechanism as a SPA-net feature extractor module. The span-partial structure is designed to reduce the redundant feature information, while the attention mechanism in the span-partial structure has the advantage of focusing on the tamper region and suppressing the original semantic information. To explore the correlation between high-dimension feature points, a deep feature matching module assists SPA-Net to locate the copy-move areas by computing the similarity of the feature map. A feature upsampling module is employed to upsample the features to their original size and produce a copy-move mask. Furthermore, the training strategy of SPA-Net without pretrained weights has a balance between copy-move and semantic features, and then the module can capture more features of copy-move forgery areas and reduce the confusion from semantic objects. In the experiment, we do not use pretrained weights or models from existing networks such as VGG16, which would bring the limitation of the network paying more attention to objects other than copy-move areas.To deal with this problem, we generated a SPANet-CMFD dataset by applying various processes to the benchmark images from SUN and COCO datasets, and we used existing copy-move forgery datasets, CMH, MICC-F220, MICC-F600, GRIP, Coverage, and parts of USCISI-CMFD, together with our generated SPANet-CMFD dataset, as the training set to train our model. In addition, the SPANet-CMFD dataset could play a big part in forgery detection, such as deepfakes. We employed the CASIA and CoMoFoD datasets as testing datasets to verify the performance of our proposed method. The Precision, Recall, and F1 are calculated to evaluate the CMFD results. Comparison results showed that our model achieved a satisfactory performance on both testing datasets and performed better than the existing methods.

PROPERTIES OF HYPERGRAPHS OF MODELS OF WEAKLY O-MINIMAL THEORIES

In this paper, we study the notions of relative H-freedom and relative H-independence for hypergraphs of models of weakly o-minimal theories. Hypergraphs of models of a theory are derived objects that allow obtaining essential structural information both about the theories themselves and about related semantic objects. Recall that a hypergraph is any pair of sets (X, Y), where Y is some subset of the Boolean P(X) of a set X. In this case, the set X is called the support of the hypergraph (X, Y), and elements from Y are called edges of the hypergraph (X, Y). Weak o-minimality was originally deeply investigated by D. Macpherson, D. Marker, and C. Steinhorn. In the nineties of the last century, Kazakhstan scientists successfully joined the study of this concept, solving a number of problems posed by the authors. In this paper, we continue the study of model-theoretic properties of weakly o-minimal structures. A criterion for relative H-freedom of the set of realizations of non-algebraic 1-type in almost omega-categorical weakly o-minimal theories is obtained in terms of convexity rank. We also establish a criterion for relative H-independence of the sets of realizations of two non-algebraic 1-types in almost omega-categorical weakly o-minimal theories in terms of weak orthogonality of 1-types.

Global Dilated Attention and Target Focusing Network for Robust Tracking

Self Attention has shown the excellent performance in tracking due to its global modeling capability. However, it brings two challenges: First, its global receptive field has less attention on local structure and inter-channel associations, which limits the semantics to distinguish objects and backgrounds; Second, its feature fusion with linear process cannot avoid the interference of non-target semantic objects. To solve the above issues, this paper proposes a robust tracking method named GdaTFT by defining the Global Dilated Attention (GDA) and Target Focusing Network (TFN). The GDA provides a new global semantics modeling approach to enhance the semantic objects while eliminating the background. It is defined via the local focusing module, dilated attention and channel adaption module. Thus, it promotes semantics by focusing local key information, building long-range dependencies and enhancing the semantics of channels. Subsequently, to distinguish the target and non-target objects both with rich semantics, the TFN is proposed to accurately focus the target region. Different from the present feature fusion, it uses the template as the query to build a point-to-point correlation between the template and search region, and finally achieves part-level augmentation of target feature in the search region. Thus, the TFN efficiently augments the target embedding while weakening the non-target objects. Experiments on challenging benchmarks (LaSOT, TrackingNet, GOT-10k, OTB-100) demonstrate that the GdaTFT outperforms many state-of-the-art trackers and achieves leading performance. Code will be available.

Robust object tracking via ensembling semantic‐aware network and redetection

AbstractMost Siamese‐based trackers use classification and regression to determine the target bounding box, which can be formulated as a linear matching process of the template and search region. However, this only takes into account the similarity of features while ignoring the semantic object information, resulting in some cases in which the regression box with the highest classification score is not accurate. To address the lack of semantic information, an object tracking approach based on an ensemble semantic‐aware network and redetection (ESART) is proposed. Furthermore, a DarkNet53 network with transfer learning is used as our semantic‐aware model to adapt the detection task for extracting semantic information. In addition, a semantic tag redetection method to re‐evaluate the bounding box and overcome inaccurate scaling issues is proposed. Extensive experiments based on OTB2015, UAV123, UAV20L, and GOT‐10k show that our tracker is superior to other state‐of‐the‐art trackers. It is noteworthy that our semantic‐aware ensemble method can be embedded into any tracker for classification and regression task.

ГИДРОНИМЫ АДЫГЕИ КАК ИСТОЧНИК ЭТНОКУЛЬТУРНОЙ ИНФОРМАЦИИ

The article is devoted to the study of a group of regional hydronyms that have lost their connection with the appellative. It is established that the study of the etymology, semantics of a hydronym, the definition of its original appearance is impossible without the involvement of ethnocultural material, which confirms the need to use an integrated approach to the study of toponyms, since such an approach makes it possible to correctly describe units and allows for the «reconstruction» of the regional toponymic space, toponymic field. Purpose: The description of the toponymic space of the region (the Republic of Adygea) and hydronyms in particular involves a detailed analysis of all units, allowing to reflect the entire toponymic picture. In the toponymy of the region, there are names that have lost their connection with appellatives. The establishment of etymology, semantics and the initial appearance of such units is impossible without the involvement of ethno-cultural material. Such an approach provides an opportunity that makes it possible to carry out a correct description of units and allows for the «reconstruction» of the regional toponymic space. Methods: The basis for the correct description of the etymology and semantics of names of specific geographical objects is the conservatism of toponymic models characteristic of the language. The research was conducted using phonetic, word-formation, etymological, semantic analysis. The basis for solving ethnolinguistic problems was the combination of onomasiological and semasiological aspects of research. Results: To establish the etymology of a toponym, it is necessary to take into account the principles of nomination characteristic of the self-consciousness and worldview of the people inhabiting a certain territory, reflected in culture and language, as well as the place of a specific name in the systemic organization of the regional toponymic field. Ethnocultural information captured in toponymy is what helps a linguist researcher to determine the principles of nomination characteristic of a particular language reflecting the worldview of the people. Discussion: The description of the toponymic space of the region (not only the Republic of Adygea), taking into account the system relations and the field approach, the identification of the main nomination techniques, clarification of the etymology, semantics of individual geographical objects traditionally associated with the regional landscape and being components of the regional onomastic field, seems very relevant and promising. The detailed analysis of hydronyms allows reconstructing the entire toponymic space of the region, makes it possible to consider the entire system as a whole.

Procedural Generation of Narrative Worlds

A Narrative World typically consists of several interrelated locations that, all together, fully support enacting a given story. For this, each location in a narrative world features all the objects as required there by the narrative, as well as a variety of other objects that plausibly describe or decorate the location. Procedural generation of narrative worlds poses many challenges, including that (i) it cannot lean only on domain knowledge (e.g. patterns of objects commonly found in typical locations), and (ii) it involves a temporal dimension, which introduces dynamic fluctuations of objects between locations. We present a novel approach for the procedural generation of narrative worlds, following two stages: first, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Narrative World Mold</i> is generated (only once) for a given story; second, the narrative world mold is used to create one (or more) possible narrative worlds for that story. For each story, its narrative world mold integrates spatio-temporal descriptions of its locations with the object semantics and the domain knowledge previously acquired on typical locations. We describe how a narrative world mold can be generated, as well as how it can be fed to existing procedural generation methods, to create a variety of narrative worlds that fit that narrative. We evaluate our own implementation of this approach, performing a number of experiments that illustrate both the expressive power of narrative world molds and their ability to steer the generation of narrative worlds.

Complementary Feature Pyramid Network for Object Detection

The way of constructing a robust feature pyramid is crucial for object detection. However, existing feature pyramid methods, which aggregate multi-level features by using element-wise sum or concatenation, are inefficient to construct a robust feature pyramid. The reason is that these methods cannot be effective in discriminating the relevant semantics of objects. In this article, we propose a Complementary Feature Pyramid Network (CFPN) to aggregate multi-level features selectively and efficiently by exploring complementary information between multi-level features. Specifically, a Spatial Complementary Module (SCM) and a Channel Complementary Module (CCM) are designed and embedded in CFPN to enhance useful information and suppress irrelevant information during feature fusions along spatial and channel dimensions, respectively. CFPN is a generic feature extractor, as evidenced by its seamless integration into single-stage, two-stage, and end-to-end object detectors. Experiments conducted on the COCO and Pascal VOC datasets demonstrate that integrating our CFPN into RetinaNet, Faster RCNN, Cascade RCNN, and Sparse RCNN obtains consistent performance improvements with negligible overheads. Code and models are available at: https://github.com/VIPLab-CQU/CFPN .

Munchʼs “Scream” as a sense-giving object for the conceptual entity

A conceptual entity is a phenomenon that only exists in our thoughts as knowledge, linguistic, cultural, iconic, etc. All objects falling under this category are those that were created only by human intellect (power of eason or emotions). The article focuses on how a conceptual idea about the object chosen or given for in-depth discussion or study in an academic setting might be constructed and how the sense-giving potential of this object forms the conceptual entity. In this context, the object acts as a symbol for the idea and transforms into a sense-making tool that affects how linguistic or visual patterns manifest in the human mind. The articleʼs goal is to illustrate the problem of the expansion of every field of knowledge through the development of new concepts. This goal involves visual art as a reference point, notably Edvard Munchʼs Scream series becoming an object for idea generation. Practically, conceptualization strategies can make teaching a foreign language easier. The use of visual art images as nonverbal communication tools gives teachers a chance to improve studentsʼ discourse skills. The cognitive approach to understanding the scream image in the artwork enables us to connect it to symbolic qualities that heighten its emotional resonance and give it fundamentally indivisible characteristics. This article takes a cognitive approach to communication. Hence, teaching it involves developing mental representations of the conversational subject and its linguistic realizations. As the result, considering the object (image) as a conceptual phenomenon of visual culture gives the following sequence of causal modes of its cognition: stimulation – intentional stances and cognitive attitude – mentalization / presence of mental content – ideation / reasoning / presence of substantial content – conceptualization. All of these are either postures or processes in which a kind of object appears, depending on the psychodynamic or psychoemotional state of its meditator, more specifically: stimulus object – intentional object – mental object – ideative object – substantial object / semantic object – conceptual object. So, Munchʼs conception of the scream has not assumed a stable character, we are not inclined to consider the scream as a concept and leave it to us to designate it as a conceptual sense-given object. Future studies might focus on creating conceptual frameworks for the language learning process.

Semantics lead all: Towards unified image registration and fusion from a semantic perspective

Infrared–visible image registration and fusion are closely related processes, and it is an attractive problem to implement coordinated registration and fusion in a unified framework. The registration accuracy of existing methods fails to satisfy the fusion needs in some scenarios, which affects the fusion visual performance. In addition, as an image preprocessing step, the speed of the network after cascading registration and fusion is not sufficient for more advanced tasks, thus restricting the usability of these methods. To solve the above problems, we propose a network that uses semantics to lead all, termed SemLA, capable of unifying the registration and fusion processes in an efficient and robust way. Our key idea is to explicitly embed semantic information at all stages of the network. In particular, SemLA employs a coordinated approach that involves joint training of registration and semantic features to ensure efficient network operation. The calibration of the semantic-aware maps and the description of their spatial structure information mutually reinforce each other to obtain more accurate registration. Additionally, the semantic-guided fusion process enhances the representation of complementary information within the semantic object while effectively suppressing visual interference caused by overlapping regional demarcation lines of the aligned image. The results of different experiments show that our SemLA has a better tradeoff between performance and efficiency compared to state-of-the-art methods and adapts to the semantic needs of advanced vision tasks. The source code is publicly available at https://github.com/xiehousheng/SemLA.

Concept-Enhanced Relation Network for Video Visual Relation Inference

Video visual relation inference aims at extracting the relation triplets in the form of < <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">subject-predicate-object</i> > in videos. With the development of deep learning, existing approaches are designed based on data-driven neural networks. But the datasets are always biased in terms of objects and relation triplets, which make relation inference challenging. Existing approaches often describe the relationships from visual, spatial, and semantic characteristics. The semantic description plays a key role to indicate the potential linguistic connections between objects, that are crucial to transfer knowledge across relationships, especially for the determination of novel relations. However, in these works, the semantic features are not emphasized, but simply obtained by mapping object labels, which can not reflect sufficient linguistic meanings. To alleviate the above issues, we propose a novel network, termed Concept-Enhanced Relation Network (CERN), to facilitate video visual relation inference. Thanks to the attributes and linguistic contexts implied in concepts, the semantic representations aggregated with related concept knowledge of objects are of benefit to relation inference. To this end, we incorporate retrieved concepts with local semantics of objects via the gating mechanism to generate the concept-enhanced semantic representations. Extensive experimental results show that our approach has achieved state-of-the-art performance on two public datasets: ImageNet-VidVRD and VidOR.

부사 ‘사실’의 주관성과 주관화 연구

Objectives The present study aims to explain the subjectivity and subjectivization of the adverb “Sasil”, analyzing how the meaning of the adverb “Sasil” changes from tis objective meaning to its subjective meaning, and finally forms its pragmatic meaning.&#x0D; Methods This study focuses on the objective and subjective usage of the adverb “Sasil” by taking the example sentences in “21st Century Sejong Corpus” as the research object. Through subjectivity and subjectivity theory, the process of strengthening subjectivity of the adverb ‘fact’ was analyzed. With the progress of subjectiveization, we looked at how it changes from the objective role described in the dictionary to the pragmatic use.&#x0D; Results The analysis shows that the objective semantics of “Sasil” are generally as follows: 1) Objective comparison and conversion of the former and latter contents, 2) The latter content of “Sasil” makes objective amendments to the former one, 3) The latter content supplements the former one. Situations with speaker subjectivity are generally interpreted as follows: 1) The speaker's subjective assessment of the objective situation, 2) Negative emotions such as dissatisfaction, disagreement and worry are added to the latter content, 3) The function of objective contrast and transformation as the basic meaning disappears and evolves into the subjective extension of the speaker, 4) The objective meaning disappears completely and is used as a pragmatic function of the speaker to maintain the subject.&#x0D; Conclusions It can be concluded that with the development of subjectivization, “Sasil” has gradually changed from typical meaning to abstract pragmatic meaning.

THINGSplus: New norms and metadata for the THINGS database of 1854 object concepts and 26,107 natural object images

To study visual and semantic object representations, the need for well-curated object concepts and images has grown significantly over the past years. To address this, we have previously developed THINGS, a large-scale database of 1854 systematically sampled object concepts with 26,107 high-quality naturalistic images of these concepts. With THINGSplus, we significantly extend THINGS by adding concept- and image-specific norms and metadata for all 1854 concepts and one copyright-free image example per concept. Concept-specific norms were collected for the properties of real-world size, manmadeness, preciousness, liveliness, heaviness, naturalness, ability to move or be moved, graspability, holdability, pleasantness, and arousal. Further, we provide 53 superordinate categories as well as typicality ratings for all their members. Image-specific metadata includes a nameability measure, based on human-generated labels of the objects depicted in the 26,107 images. Finally, we identified one new public domain image per concept. Property (M = 0.97, SD = 0.03) and typicality ratings (M = 0.97, SD = 0.01) demonstrate excellent consistency, with the subsequently collected arousal ratings as the only exception (r = 0.69). Our property (M = 0.85, SD = 0.11) and typicality (r = 0.72, 0.74, 0.88) data correlated strongly with external norms, again with the lowest validity for arousal (M = 0.41, SD = 0.08). To summarize, THINGSplus provides a large-scale, externally validated extension to existing object norms and an important extension to THINGS, allowing detailed selection of stimuli and control variables for a wide range of research interested in visual object processing, language, and semantic memory.

Object detection analysis based on Machine Learning Algorithms

The branch of artificial intelligence known as computer vision focuses on developing and utilizing digital systems to process, examine, and interpret visual data. Computer vision aims to make it possible for computing equipment to accurately recognise an object's digital image and perform the necessary action.The field of transportation and artificial intelligence includes vehicle discovery, which is pivotal. Intelligent transport system (ITS) is a fashion used to produce an effective road transportation system. The Automated Driving System which has made significant advancements in recent times, heavily relies on vehicle identification in its armature. The fashion of detecting vehicles on the road is used for objects similar as vehicle shadowing, counts, and average speed of each vehicle, business analysis, and vehicle categorization and can be applied in a variety of settings. A wide, active, and grueling field of computer vision is real- time object discovery. This recognizes semantic objects of a particular class in digital prints and pictures. For face recognition, vehicle recognition, rambler counting, web image analysis, security systems, and tone- driving motorcars, object discovery is constantly employed. Key Words: Vehicle dataset, ITS, Vehicle detection, Highway management

Speakers prioritise affordance-based object semantics in scene descriptions

ABSTRACT This work investigates the linearisation strategies used by speakers when describing real-world scenes to better understand production plans for multi-utterance sequences. In this study, 30 participants described real-world scenes aloud. To investigate which semantic features of scenes predict order of mention, we quantified three features (meaning, graspability, and interactability) using two techniques (whole-object ratings and feature map values). We found that object-level semantic features, namely those affordance-based, predicted order of mention in a scene description task. Our findings provide the first evidence for an object-related semantic feature that guides linguistic ordering decisions and offers theoretical support for the role of object semantics in scene viewing and description.

“I am a binary trans man and I love being pregnant”: Making meaning of pregnancy in seahorse dad narratives

Although there are many ways one might start a family, the culture in the United States considers pregnancy one of a woman’s most important life achievements, privileging biological ties above all else. These cultural assumptions stigmatize seahorse dads, or transmen and nonbinary individuals who become pregnant. Using relational dialectics theory (RDT), this study examined posts on Reddit to highlight how marginalized discourses resist dominant discourses to make meaning of a semantic object. RDT’s corresponding method, contrapuntal analysis, revelated three discourses that competed to make meaning of seahorse dads: the discourse of pregnancy as a woman’s burden (DWB), the discourse of pregnancy as a woman’s privilege (DWP), and the discourse of pregnancy as an independent process (DIP). These discourses interacted through contractive practices (i.e., disqualification, naturalization, and neutralization), synchronic interplay (i.e., entertaining, countering, and negating), and dialogic transformation (i.e., hybridization and an aesthetic moment). Findings revealed theoretical implications and practical applications.

Reconstructing Three-dimensional geological structures by the Multiple-point statistics method coupled with a deep neural network: A case study of a metro station in Guangzhou, China

A credible large-scale three-dimensional (3D) geological model constructed from a small amount of local high-density data provides a vital data infrastructure for the design and construction of metro lines. The multiple-point statistics (MPS) method has successfully generated 3D geological models in many fields. However, the global cognition of the spatial relations between geological objects is difficult to reconstruct using a local optimization strategy with moving templates in the MPS simulation. This study presented an MPS simulation approach for constructing 3D geological structures with two-dimensional (2D) cross-sections, where the MPS process was coupled with a fully connected deep neural network (FCDNN) simulation. The core idea of the proposed method was that the global features of each geological object were generated from the trained FCDNN, the input parameter of which was the subsurface depth. Then, the entire model was constructed on the initial model using the following iterative MPS process: the semantic relationship of geological objects was extracted from 2D cross-sections and used as a constraint in the modeling process, which ensured the order of the stratigraphic sequence and rationality of the geological structure in the final model. The joint objective function of the proposed method was composed of the loss function of the FCDNN and conventional MPS objective function constrained by the spatial semantics of geological objects. The concrete example in the Jiangtai Road Metro Station of Guangzhou Line 11, China illustrated that the details of the complex geological structures could be obtained accurately by the presented method. The global features from the trained FCDNN provided the basic geometry for the final model, which overcame the limitations of the conventional MPS method.