Semantic Localization Research Articles

SpaDE: Semantic Locality Preserving Biclustering for Neuroimaging Data.

The most discriminative and revealing patterns in the neuroimaging population are often confined to smaller subdivisions of the samples and features. Especially in neuropsychiatric conditions, symptoms are expressed within micro subgroups of individuals and may only underly a subset of neurological mechanisms. As such, running a whole-population analysis yields suboptimal outcomes leading to reduced specificity and interpretability. Biclustering is a potential solution since subject heterogeneity makes one-dimensional clustering less effective in this realm. Yet, high dimensional sparse input space and semantically incoherent grouping of attributes make post hoc analysis challenging. Therefore, we propose a deep neural network called semantic locality preserving auto decoder (SpaDE), for unsupervised feature learning and biclustering. SpaDE produces coherent subgroups of subjects and neural features preserving semantic locality and enhancing neurobiological interpretability. Also, it regularizes for sparsity to improve representation learning. We employ SpaDE on human brain connectome collected from schizophrenia (SZ) and healthy control (HC) subjects. The model outperforms several state-of-the-art biclustering methods. Our method extracts modular neural communities showing significant (HC/SZ) group differences in distinct brain networks including visual, sensorimotor, and subcortical. Moreover, these bi-clustered connectivity substructures exhibit substantial relations with various cognitive measures such as attention, working memory, and visual learning.

T-UNet: triplet UNet for change detection in high-resolution remote sensing images

ABSTRACT Remote sensing image change detection aims to identify differences between images acquired at different times in the same area, crucial for land management, environmental monitoring, and disaster assessment. Current change detection methods mainly use Siamese or early fusion structures. Siamese networks focus on object features at different times but lack attention to change information, leading to false alarms and missed detections. Early fusion structures focus on fused features but neglect temporal object features, hindering accurate change detection. To address these issues, we propose a novel network, Triplet UNet (T-UNet), employing a triplet encoder to simultaneously extract object features and change features between the pre- and post-time-phase images. To effectively interact features extracted from the three branches of triplet encoder, we propose a multi-branch spatial-spectral cross-attention module. In the decoder, we employ channel attention and spatial attention mechanisms to fully mine and integrate detailed texture and semantic localization information. The proposed T-UNet surpasses seven other state-of-the-art methods on three publicly available datasets. Extensive experiments verify the effectiveness of the proposed structure and modules as well as the superiority of the proposed T-UNet. The source code for the proposed T-UNet is accessible at https://github.com/Pl-2000/T-UNet.

SLMSF-Net: A Semantic Localization and Multi-Scale Fusion Network for RGB-D Salient Object Detection.

Salient Object Detection (SOD) in RGB-D images plays a crucial role in the field of computer vision, with its central aim being to identify and segment the most visually striking objects within a scene. However, optimizing the fusion of multi-modal and multi-scale features to enhance detection performance remains a challenge. To address this issue, we propose a network model based on semantic localization and multi-scale fusion (SLMSF-Net), specifically designed for RGB-D SOD. Firstly, we designed a Deep Attention Module (DAM), which extracts valuable depth feature information from both channel and spatial perspectives and efficiently merges it with RGB features. Subsequently, a Semantic Localization Module (SLM) is introduced to enhance the top-level modality fusion features, enabling the precise localization of salient objects. Finally, a Multi-Scale Fusion Module (MSF) is employed to perform inverse decoding on the modality fusion features, thus restoring the detailed information of the objects and generating high-precision saliency maps. Our approach has been validated across six RGB-D salient object detection datasets. The experimental results indicate an improvement of 0.20~1.80%, 0.09~1.46%, 0.19~1.05%, and 0.0002~0.0062, respectively in maxF, maxE, S, and MAE metrics, compared to the best competing methods (AFNet, DCMF, and C2DFNet).

An efficient unsupervised approach for textile defect detection through unifying texture reconstruction and semantic localization

Deep learning has shown promise in textile defect detection, but its reliance on large high-quality labeled datasets poses challenges in real-world industrial applications. This study presents a novel unsupervised defect detection framework that effectively detects various types of texture defects with limited defect-free texture samples. The framework integrates texture and semantic information using a bilateral-branch network architecture (TSUBB-Net). Specifically, TSUBB-Net employs a weighted centering loss to cluster complex texture units, emphasizing semantic information within defect regions through a channel attention mechanism. It further fuses contextual semantic information to achieve precise defect localization. Thus, the efficient fusion method combines texture and semantic information, enabling the representation of complex texture structures and mitigating the impact of image acquisition quality on defect recognition. To evaluate the effectiveness of our proposed method, we build a unique dedicated database of textile defect image segmentation, which serves as the benchmark for textile defect detection. Experimental results demonstrate that TSUBB-Net surpasses state-of-the-art methods, exhibiting excellent performance in textile defect detection. The proposed framework holds significant potential for practical applications in the textile industry, improving defect detection capabilities.

Pairwise Guided Multilayer Cross-Fusion Network for Bird Image Recognition

Bird identification is the first step in collecting data on bird diversity and abundance, which also helps research on bird distribution and population measurements. Most research has built end-to-end training models for bird detection task via CNNs or attentive models, but many perform unsatisfactorily in fine-grained bird recognition. Bird recognition tasks are highly influenced by factors, including the similar appearance of different subcategories, diverse bird postures, and other interference factors such as tree branches and leaves from the background. To tackle this challenge, we propose the Progressive Cross-Union Network (PC-Net) to capture more subtle parts with low-level attention maps. Based on cross-layer information exchange and pairwise learning, the proposed method uses two modules to improve feature representation and localization. First, it utilizes low- and high-level information for cross-layer feature fusion, which enables the network to extract more comprehensive and discriminative features. Second, the network incorporates deep semantic localization to identify and enhance the most relevant regions in the images. In addition, the network is designed with a semantic guidance loss to improve its generalization for variable bird poses. The PC-Net was evaluated on an extensively used birds dataset (CUB-200-2011), which contains 200 birds subcategories. The results demonstrate that the PC-Net achieved an impressive recognition accuracy of 89.2%, thereby outperforming maintained methods in bird subcategory identification. We also achieved competitive results on two other datasets with data on cars and airplanes. The results indicated that the PC-Net improves the accuracy of diverse bird recognition, as well as other fine-grained recognition scenarios.

AMC: Adaptive Multi-expert Collaborative Network for Text-guided Image Retrieval

Text-guided image retrieval integrates reference image and text feedback as a multimodal query to search the image corresponding to user intention. Recent approaches employ multi-level matching, multiple accesses, or multiple subnetworks for better performance regardless of the heavy burden of storage and computation in the deployment. Additionally, these models not only rely on expert knowledge to handcraft image-text composing modules but also do inference by the static computational graph. It limits the representation capability and generalization ability of networks in the face of challenges from complex and varied combinations of reference image and text feedback. To break the shackles of the static network concept, we introduce the dynamic router mechanism to achieve data-dependent expert activation and flexible collaboration of multiple experts to explore more implicit multimodal fusion patterns. Specifically, we construct AMC, our A daptive M ulti-expert C ollaborative network, by using the proposed router to activate the different experts with different levels of image-text interaction. Since routers can dynamically adjust the activation of experts for the current samples, AMC can achieve the adaptive fusion mode for the different reference image and text combinations and generate dynamic computational graphs according to varied multimodal queries. Extensive experiments on two benchmark datasets demonstrate that due to benefits from the image-text composing representation produced by an adaptive multi-expert collaboration mechanism, AMC has better retrieval performance and zero-shot generalization ability than the state-of-the-art method while keeping the lightweight model and fast retrieval speed. Moreover, we analyze the visualization of path activation, attention map, and retrieval results to further understand the routing decisions and semantic localization ability of AMC. The codes and pretrained models are available at https://github.com/KevinLight831/AMC .

Epistemic uncertainty aware semantic localization and mapping for inference and belief space planning

We investigate the problem of autonomous object classification and semantic SLAM, which generally exhibits a tight coupling between classification, metric SLAM, and planning under uncertainty. We contribute a unified framework for inference and belief space planning (BSP) that addresses prominent sources of uncertainty in this context: classification aliasing (classifier cannot distinguish between candidate classes from certain viewpoints), classifier epistemic uncertainty (classifier receives data “far” from its training set), and localization uncertainty (camera and object poses are uncertain). Specifically, two methods are developed for maintaining a joint distribution over robot and object poses, and over a posterior class probability vector that considers epistemic uncertainty in a Bayesian fashion. The first approach is Multi-Hybrid (MH), where multiple hybrid beliefs over poses and classes are maintained to approximate the joint belief over poses and posterior class probability. The second approach is Joint Lambda Pose (JLP), where the joint belief is maintained directly using a novel JLP factor. Furthermore, we extend both methods to BSP, while reasoning about future posterior epistemic uncertainty indirectly or directly via a novel information-theoretic reward function. Both inference methods utilize a novel viewpoint-dependent classifier uncertainty model that leverages the coupling between poses and classification scores and predicts the epistemic uncertainty from certain viewpoints. In addition, this model is used to generate predicted measurements during planning. To the best of our knowledge, this is the first work that reasons about classifier epistemic uncertainty within semantic SLAM and BSP. We extensively evaluate our inference and BSP approaches in simulation using real image data from the Active Vision Dataset. Results clearly indicate superior classification performance of our methods compared to an approach that is not epistemic uncertainty aware.

SeLo v2: Toward for Higher and Faster Semantic Localization

Semantic localization (SeLo) refers to locating the most relevant position in the Remote Sensing (RS) image based on the semantic information contained in the retrieved text, which is an emerging task based on cross-modal retrieval. SeLo achieves pixel-level semantic retrieval through caption-level annotations, which plays an important role in RS image-text retrieval. Although there are some researches on SeLo tasks at present, the current SeLo framework is still quite violent, which consumes a lot of resources. In this paper, based on the existing work, we conduct a more in-depth exploration and propose SeLo v2, which greatly improves the speed and accuracy of the SeLo task. First, based on the characteristics of regional consistency of remote sensing images, a multilevel likelihood expansion (MLE) is proposed to reduce the number of cross-modal similarity calculations, and the speed of SeLo task is greatly improved by combining single-scale clipping with multi-point multi-scale expansion. Next, to enhance the accuracy, we come up with the chaotic self-feeding mechanism (CSM), which performs semantic frequency enhancement to fully extract the semantic information contained in the caption, making the generated SeLo map more detailed and accurate. Compared with the initial SeLo framework, SeLo v2 has better semantic localization performance with only half the time consumed, which shows the great advantages of SeLo v2 in terms of time and accuracy. The proposed SeLo v2 is available at Link.

Lightweight Semantic-Aided Localization With Spinning LiDAR Sensor

Autonomous driving demands robust and precise vehicle localization in complex environments with limited on-board computational resources. Incorporating reliable semantic information with localization algorithms can increase accuracy remarkably, however, the process of extracting semantic information from LiDAR point clouds and matching it to semantic maps is computationally intensive. Moreover, pure semantic localization cannot achieve the robustness requirements for safe self-driving as the necessary quantity of semantic landmarks cannot be guaranteed under extreme conditions. In this paper, we present a lightweight semantic-aided localization method that improves upon traditional techniques in two ways. First, we propose a highly efficient pipeline to extract three semantic classes from a LiDAR scan. Second, instead of semantic 3D point cloud registration, map matching is performed through 2D key point matching. We then integrate these two functions into a dynamic semantic aided localization framework. Our on-road experiments demonstrate that the proposed method achieves both the high accuracy of semantic localization and the robustness of non-semantic localization. With our algorithm consuming under 10% of CPU resources, we observe reduced positioning error, especially peak error, when comparing to non-semantic counterparts.

1921. Leveraging Bluetooth Low-Energy Technology to Improve Contact Tracing in Healthcare Settings during the COVID-19 Pandemic

Abstract Background COVID-19 rapidly evolved into a global pandemic. Contact tracing with isolation and quarantine contribute to epidemic control but they are time consuming, costly and may be incomplete. We set out to assess the usability and performance characteristics of Bluetooth Low-Energy (BLE) wireless technology for indoor localization applied to contact tracing in healthcare settings. Methods Consented healthcare workers (HCW) from 2 designated COVID-19 wards (one intensive care unit (ICU) and one medical ward) were equipped with coin-sized BLE- emitting beacons. The signal was captured by small embedded computers (anchors) placed at designated locations, time-stamped and transmitted to an edge server via secure Wi-Fi where data were stored and real time contact algorithms were run (Fig.1). We developed experiments mimicking clinical scenarios and tested indoor localization during observed clinical activity for 6 months. We constructed our algorithms based on room structure (e.g. open spaces vs computer rooms) and activity characteristics (e.g. rounding in a large group vs 2 healthcare workers sitting together). We used 1) radio fingerprint localization where an initial virtual radio map was developed, 2) semantic localization which carries additional information such as proximity to a computer to define indirect transmission via fomites, and 3) clustering contact tracing to identify individuals rounding together. Close contact was defined as per the CDC guidelines. Fig. 1System configuration Results Consent rate was 43.3% with 187 HCW enrolled in the study. Consent rate was higher in the ICU and among attendings. All participants were compliant with wearing the beacons for the duration of the study. The performance characteristics for contact tracing using fingerprinting methods were AUROC 0.93, AUPRC 0.96, sensitivity 0.9, specificity 0.77 with F1 score of 0.89 and overall accuracy of 0.85. The clustering contact tracing registered a sensitivity of 0.86, specificity 0.89, F1 score 0.91 and accuracy 0.87. Computation time necessary to generate a list of close contacts as per specified criteria was less than 30 minutes. Conclusion We have developed and tested a reliable and accurate, low-cost and easily deployable system based on BLE technology to improve contact tracing among healthcare workers. Disclosures M Cristina Vazquez Guillamet, MD, AUPH: Stocks/Bonds|BNGO: Stocks/Bonds|OCGN: Stocks/Bonds|SESN: Stocks/Bonds.

SMURF: Efficient and Scalable Metadata Access for Distributed Applications

In parallel with big data processing and analysis dominating the usage of distributed and Cloud infrastructures, the demand for distributed metadata access and transfer has increased. The volume of data generated by many application domains exceeds petabytes, while the corresponding metadata amounts to terabytes or even more. This article proposes a novel solution for efficient and scalable metadata access for distributed applications across wide-area networks, dubbed SMURF. Our solution combines novel pipelining and concurrent transfer mechanisms with reliability, provides distributed continuum caching and semantic locality-aware prefetching strategies to sidestep fetching latency, and achieves scalable and high-performance metadata fetch/prefetch services in the Cloud. We incorporate the phenomenon of semantic locality awareness for increased prefetch prediction rate using real-life application I/O traces from Yahoo! Hadoop audit logs and propose a novel prefetch predictor. By effectively caching and prefetching metadata based on the access patterns, our continuum caching and prefetching mechanism significantly improves the local cache hit rate and reduces the average fetching latency. We replay approximately 20 Million metadata access operations from real audit traces, where SMURF achieves 90% accuracy during prefetch prediction and reduced the average fetch latency by 50% compared to the state-of-the-art mechanisms.

Semantic localization on BIM-generated maps using a 3D LiDAR sensor

Conventional sensor-based localization relies on high-precision maps, which are generally built using specialized mapping techniques involving high labor and computational costs. In the architectural, engineering and construction industry, Building Information Models (BIM) are available and can provide informative descriptions of environments. This paper explores an effective way to localize a mobile 3D LiDAR sensor on BIM-generated maps considering both geometric and semantic properties. First, original BIM elements are converted to semantically augmented point cloud maps using categories and locations. After that, a coarse-to-fine semantic localization is performed to align laser points to the map based on iterative closest point registration. The experimental results show that the semantic localization can track the pose successfully with only one LiDAR sensor, thus demonstrating the feasibility of the proposed mapping-free localization framework. The results also show that using semantic information can help reduce localization errors on BIM-generated maps.

Indoor Positioning and Navigation Model Based on Semantic Grid

Traditional GPS positioning technology cannot be used in indoor space. With the development of the new positioning technology and the Internet of things, the indoor mobile object positioning and navigation model have been the focus of the relevant research institutions at home and abroad. Based on this, indoor positioning technology was studied starting from Wi-Fi, RFID, and iBeacon technology in this paper. However, the accuracy of indoor positioning and navigation needs to be further improved. This paper presents a semantic space model based on artificial intelligence technology, through semantic pattern matching, semantic concept extension, semantic reasoning and semantic mapping, and interior semantic localization is realized. The indoor semantic network and indoor grid navigation model are constructed, and the indoor semantic path is modeled from time, location, user, and congestion. At the same time, the improved Term Frequency-Inverse Document Frequency is combined with the Hidden Markov Model to improve the accuracy of matching the stay area with the most likely location to visit and improve the accuracy of semantic annotation. It was found that the research on the indoor positioning and navigation model based on the semantic grid can realize the uniform expression of the complex spatial semantics of the theme, geometry, connectivity, and distance, which can promote the development of indoor positioning and navigation.

Reasoning about location of robot-operated object based on probabilistic ontologies

Background: A robot needs to acquire the location of objects when performing daily tasks. Compared to industrial robots, a service robot faces a more complex and unstructured working environment where the location of the target object is usually uncertain. For example, due to diversity in personal habits and seasons, apples may be located in refrigerators, tables, or other places in the living environment. Methods: We propose a novel method for semantic localization of the robot-operated object based on probabilistic ontologies (PR-OWL) and multi-entity Bayesian networks (MEBN). The probabilistic web ontology language is used to describe and model the highly uncertain knowledge about the storage location of objects in the human household environment. Furthermore, the target location is inferred based on the multi-entity Bayesian network. Results: The proposed method is capable to adapting to environmental changes and achieves reliable probability estimation of object location. Experiments on simulated robotic tasks verify the effectiveness of the method. Conclusions: We show that applying PR-OWL combined with MEBN to locate the target object for the robot is feasible， which can improve the cognitive and self-adaptive ability of the robot.

A Lightweight Multi-Scale Crossmodal Text-Image Retrieval Method in Remote Sensing

Remote sensing (RS) crossmodal text-image retrieval has become a research hotspot in recent years for its application in semantic localization. However, since multiple inferences on slices are demanded in semantic localization, designing a crossmodal retrieval model with less computation but well performance becomes an emergent and challenging task. In this article, considering the characteristics of multi-scale and target redundancy in RS, a concise but effective crossmodal retrieval model (LW-MCR) is designed. The proposed model incorporates multi-scale information and dynamically filters out redundant features when encoding RS image, while text features are obtained via lightweight group convolution. To improve the retrieval performance of LW-MCR, we come up with a novel hidden supervised optimization method based on knowledge distillation. This method enables the proposed model to acquire dark knowledge of the multi-level layers and representation layers in the teacher network, which significantly improves the accuracy of our lightweight model. Finally, on the basis of contrast learning, we present a method employing unlabeled data to boost the performance of RS retrieval model further. The experiment results on four RS image-text datasets demonstrate the efficiency of LW-MCR in RS crossmodal retrieval (RSCR) tasks. We have released some codes of the semantic localization and made it open to access at <uri>https://github.com/xiaoyuan1996/retrievalSystem</uri>.

Spatial and temporal vocabulary in Serbian public discourse during the COVID pandemic

This paper investigates the new lexicon of the COVID pandemic period which has semantic components of spatiality and temporality, which is to say, it belongs to the semantic fields of space and time. The aim of this investigation is to connect the categories of space and time that are lexically manifested in the pandemic period, and demonstrate the lexical development of the categories of space and time, as well as the semantic relations between them. Analytical, descriptive, and synthetic methods were used for this purpose, while the theories of semantic localization and conceptual metaphors were applied when necessary. The thematic dictionaries - the Dictionary of Terms from the COVID Epidemic Period and the Dictionary of COVID: Thematic and Associative acted as sources of the corpus, while the electronic corpora formed for the purpose of creating the aforementioned dictionaries served as a control corpus. This paper contributes to the study of the so-called corona lexicon, which the author has been investigating from the onset of the pandemic, as well as of the impact of the pandemic on the Serbian language. The paper shows the development of the semantic fields of space and time through the multiplicity of nominations belonging to said semantic categories that saw prominent use in the COVID pandemic, which speaks to their exceptional importance for human functioning in society, as well as to their universality.

Learning to Evaluate Performance of Multimodal Semantic Localization

Semantic localization (SeLo) refers to the task of obtaining the most relevant locations in large-scale remote sensing (RS) images using semantic information such as text. As an emerging task based on cross-modal retrieval, SeLo achieves semantic-level retrieval with only caption-level annotation, which demonstrates its great potential in unifying downstream tasks. Although SeLo has been carried out successively, but there is currently no work has systematically explores and analyzes this urgent direction. In this paper, we thoroughly study this field and provide a complete benchmark in terms of metrics and testdata to advance the SeLo task. Firstly, based on the characteristics of this task, we propose multiple discriminative evaluation metrics to quantify the performance of the SeLo task. The devised significant area proportion, attention shift distance, and discrete attention distance are utilized to evaluate the generated SeLo map from pixel-level and region-level. Next, to provide standard evaluation data for the SeLo task, we contribute a diverse, multi-semantic, multi-objective Semantic Localization Testset (AIR-SLT). AIR-SLT consists of 22 large-scale RS images and 59 test cases with different semantics, which aims to provide a comprehensive evaluations for retrieval models. Finally, we analyze the SeLo performance of RS cross-modal retrieval models in detail, explore the impact of different variables on this task, and provide a complete benchmark for the SeLo task. We have also established a new paradigm for RS referring expression comprehension, and demonstrated the great advantage of SeLo in semantics through combining it with tasks such as detection and road extraction. The proposed evaluation metrics, semantic localization testsets, and corresponding scripts have been open to access at https://github.com/xiaoyuan1996/SemanticLocalizationMetrics.

Interpretable COVID-19 Chest X-Ray Classification via Orthogonality Constraint

Deep neural networks have increasingly been used as an auxiliary tool in healthcare applications, due to their ability to improve performance of several diagnosis tasks. However, these methods are not widely adopted in clinical settings due to the practical limitations in the reliability, generalizability, and interpretability of deep learning based systems. As a result, methods have been developed that impose additional constraints during network training to gain more control as well as improve interpretabilty, facilitating their acceptance in healthcare community. In this work, we investigate the benefit of using Orthogonal Spheres (OS) constraint for classification of COVID-19 cases from chest X-ray images. The OS constraint can be written as a simple orthonormality term which is used in conjunction with the standard cross-entropy loss during classification network training. Previous studies have demonstrated significant benefits in applying such constraints to deep learning models. Our findings corroborate these observations, indicating that the orthonormality loss function effectively produces improved semantic localization via GradCAM visualizations, enhanced classification performance, and reduced model calibration error. Our approach achieves an improvement in accuracy of 1.6% and 4.8% for two- and three-class classification, respectively; similar results are found for models with data augmentation applied. In addition to these findings, our work also presents a new application of the OS regularizer in healthcare, increasing the post-hoc interpretability and performance of deep learning models for COVID-19 classification to facilitate adoption of these methods in clinical settings. We also identify the limitations of our strategy that can be explored for further research in future.

Spatial and semantic interference on an auditory Stroop test: comparison of young and older adults

Background and Aim: Age related changes in cognitive functioning have been shown to vary depending on the task used. Thus, the study aimed to compare the responses of young and older adults to an auditory Stroop test that asse­ssed spatial (responses to location of the stimuli) and semantic (responses to meaning of the sti­muli) localization. Methods: The “Auditory spatial and semantic localization Stroop test”, developed as a part of the study was administered on 30 young adults aged 18 to 30 years and 30 older adults aged 58 to 70 years having normal hearing. The res­ponse accuracy and reaction time of the parti­cipants were determined for the words “right”, “left”, “front”, and “back.” Results: The older adults had significantly poo­rer response accuracy and reaction time than the young adults for both spatial and semantic loca­lization tasks. Within each participant group, semantic localization had better response accu­racy than spatial localization, while such diffe­rences in reaction time were found only in the older adults. In both groups, a congruency effect was seen for spatial but not for semantic loca­lization when response accuracy was calculated, whereas it was observed only for semantic and not for spatial localization when reaction time was measured. Conclusion: The auditory Stroop test, which measures stimulus interference and cognitive skills, could be used as a simple tool to assess the same for stimuli presented through the audi­tory modality. This would be especially helpful in older adults who may demonstrate cognitive decline with ageing to auditory stimuli. Keywords: Spatial localization; semantic localization; auditory Stroop test; age related changes

Locater

This paper explores the data cleaning challenges that arise in using WiFi connectivity data to locate users to semantic indoor locations such as buildings, regions, rooms. WiFi connectivity data consists of sporadic connections between devices and nearby WiFi access points (APs), each of which may cover a relatively large area within a building. Our system, entitled semantic LOCATion cleanER (LOCATER), postulates semantic localization as a series of data cleaning tasks - first, it treats the problem of determining the AP to which a device is connected between any two of its connection events as a missing value detection and repair problem. It then associates the device with the semantic subregion (e.g., a conference room in the region) by postulating it as a location disambiguation problem. LOCATER uses a bootstrapping semi-supervised learning method for coarse localization and a probabilistic method to achieve finer localization. The paper shows that LOCATER can achieve significantly high accuracy at both the coarse and fine levels.