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检索条件"任意字段=IEEE Information Visualization Conference/IEEE Visualization Conference"

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Indoor Topological Mapping Framework for Semantic-enabled Navigation based on Omnidirectional Images 7

Indoor Topological Mapping Framework for Semantic-enabled Na...

引用

7th ieee International conference on Advanced Robotics and Mechatronics

作者： Men, Guannan Tomita, Kohji Kamimura, Akiya Natl Inst Adv Ind Sci & Technol Tsukuba Ibaraki 3058568 Japan

ISBN: (数字)9781665483063

ISBN: (纸本)9781665483063

Compared to metric mapping, which often requires detailed information, topological mapping based on human knowledge of the environment is considered a practical method for helping robots recognize spatial environment, thereby providing navigation assistance. This study proposes a novel feature topological mapping technique for indoor navigation, which generates a description of navigation tasks and can be applied to semantic navigation. The framework of the proposed approach comprises two steps: node perception and topological map building. Node perception is performed using a visual landmark detector, which is responsible for identifying specific landmarks in visual data via YOLOv3, and a turning detector, which is responsible for detecting the moving state of the mobile platform using optical flow. Based on the results of node perception, a feature topology map is generated and updated using a hybrid vector of locally aggregated descriptors (VLAD) for loop-closure detection. Further, experiments using a sequence of omnidirectional images are conducted and the results show that the proposed approach can construct the topology of the experimental indoor environment and perform semantic navigation.

关键词： Measurement visualization Mechatronics Semantics Detectors Turning Topology

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Modulating Bottom-Up and Top-Down Visual Processing via Language-Conditional Filters

Modulating Bottom-Up and Top-Down Visual Processing via Lang...

引用

ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Kesen, Ilker Can, Ozan Arkan Erdem, Erkut Erdem, Aykut Yuret, Deniz Koc Univ KUIS AI Ctr Istanbul Turkey Koc Univ Comp Engn Dept Istanbul Turkey Amazon Search Istanbul Turkey Hacettepe Univ Comp Engn Dept Ankara Turkey

ISBN: (纸本)9781665487399

How to best integrate linguistic and perceptual processing in multi-modal tasks that involve language and vision is an important open problem. In this work, we argue that the common practice of using language in a topdown manner, to direct visual attention over high-level visual features, may not be optimal. We hypothesize that the use of language to also condition the bottom-up processing from pixels to high-level features can provide benefits to the overall performance. To support our claim, we propose a U-Net-based model and perform experiments on two language-vision dense-prediction tasks: referring expression segmentation and language-guided image colorization. We compare results where either one or both of the top-down and bottom-up visual branches are conditioned on language. Our experiments reveal that using language to control the filters for bottom-up visual processing in addition to top-down attention leads to better results on both tasks and achieves competitive performance. Our linguistic analysis suggests that bottom-up conditioning improves segmentation of objects especially when input text refers to low-level visual concepts.

关键词： visualization Image segmentation Image color analysis Grounding Computational modeling Process control Predictive models

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Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and Beyond

Appearance and Structure Aware Robust Deep Visual Graph Matc...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Ren, Qibing Bao, Qingquan Wang, Runzhong Yan, Junchi Shanghai Jiao Tong Univ Dept CSE Shanghai Peoples R China Shanghai Jiao Tong Univ MoE Key Lab Artif Intelligence Shanghai Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Despite the recent breakthrough of high accuracy deep graph matching (GM) over visual images, the robustness of deep GM models is rarely studied which yet has been revealed an important issue in modern deep nets, ranging from image recognition to graph learning tasks. We first show that an adversarial attack on keypoint localities and the hidden graphs can cause significant accuracy drop to deep GM models. Accordingly, we propose our defense strategy, namely Appearance and Structure Aware Robust Graph Matching (ASAR-GM). Specifically, orthogonal to de facto adversarial training (AT), we devise the Appearance Aware Regularizer (AAR) on those appearance-similar keypoints between graphs that are likely to confuse. Experimental results show that our ASAR-GM achieves better robustness compared to AT. Moreover, our locality attack can serve as a data augmentation technique, which boosts the state-of-the-art GM models even on the clean test dataset. Code is available at https://***/Thinklab-SJTU/RobustMatch.

关键词： Training visualization Computer vision Image recognition Computational modeling Robustness Data models

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visualization of Traffic Density on Graph using Trajectory Data 4

Visualization of Traffic Density on Graph using Trajectory D...

引用

4th International conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： More, Sahil Matange, Ashwini Abraham, Jibi Technology Coep Technological University Dept. of Computer Engineering and Information Pune India

ISBN: (纸本)9781665474368

Several applications collect users' trajectory data to provide better location-based services. Since the data reveals the mobility of the users as a group and as an individual, publishing this trajectory data may pose a threat to one's privacy. The current trajectory data mining procedures take all the raw information from the trajectory data without considering the users' privacy. This type of data mining can be prone to linkage attacks. This research proposes a novel mechanism that uses OpenStreetMap and its geo-entities recognition feature to extract and cluster the traffic signals to form vertices of a graph. From the trajectory data, vehicle type and the edge in the graph is determined. The vehicle count for each vehicle type on the edge of a graph is iteratively updated and it is visually represented on the respective road edge between two junction vertices. The visualization of the weighted road network graph enables analysts to infer useful information as the road graph itself preserves the statistical information of the road density but hides the individual users' trajectory. © 2022 ieee.

关键词： Location based services

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Testing MPT-GRE Multipath Solution in Vehicular Network V2I Communication 2

Testing MPT-GRE Multipath Solution in Vehicular Network V2I ...

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2nd ieee conference on information Technology and Data Science, CITDS 2022

作者： Szilagyi, Szabolcs Kovacs, Laszlo University of Debrecen Faculty of Informatics Department of It Systems and Networks Debrecen Hungary University of Debrecen Faculty of Informatics Nvidia Deep Learning Institute Certified Instructor Department of Data Science and Visualization Debrecen Hungary

ISBN: (数字)9781665496537

ISBN: (纸本)9781665496537

A vehicular network is a communication system comprising of vehicles equipped with radio-interfaces, where the endpoints are capable of exchanging data and communication between each other (Vehicle-to-Vehicle, V2V), as well as with another mobile network or fixed infrastructure (Vehicle-to-Infrastructure, V2I). The numerous applications used in vehicles typically require seamless and increasingly fast and reliable network connections, which poses a challenge for the wireless network technologies at our disposal today. MPT-GRE, developed at the University of Debrecen, is a multi-interface access technology, which could offer a novel solution to satisfy the requirements of the services used in vehicular networking applications. Given that MPT-GRE enables the simultaneous usage of multiple network interfaces and IP-routes for vehicles, it promises to be an effective solution for vehicular networks. In this paper, we are examining the efficiency of MPT-GRE using a self-driving car model in a dual-interface Wi-Fi environment. © 2022 ieee.

关键词： Model automobiles

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Image Segmentation Using Text and Image Prompts

Image Segmentation Using Text and Image Prompts

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Lueddecke, Timo Ecker, Alexander Univ Gottingen Inst Comp Sci Gottingen Germany Univ Gottingen CIDAS Gottingen Germany MPI Dynam & Self Org Gottingen Germany

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Image segmentation is usually addressed by training a model for a fixed set of object classes. Incorporating additional classes or more complex queries later is expensive as it requires re-training the model on a dataset that encompasses these expressions. Here we propose a system that can generate image segmentations based on arbitrary prompts at test time. A prompt can be either a text or an image. This approach enables us to create a unified model (trained once) for three common segmentation tasks, which come with distinct challenges: referring expression segmentation, zero-shot segmentation and one-shot segmentation. We build upon the CLIP model as a backbone which we extend with a transformer-based decoder that enables dense prediction. After training on an extended version of the PhraseCut dataset, our system generates a binary segmentation map for an image based on a free-text prompt or on an additional image expressing the query. We analyze different variants of the latter image-based prompts in detail. This novel hybrid input allows for dynamic adaptation not only to the three segmentation tasks mentioned above, but to any binary segmentation task where a text or image query can be formulated. Finally, we find our system to adapt well to generalized queries involving affordances or properties. Code is available at https://***/code/clipseg

关键词： Training Image segmentation visualization Affordances Machine vision Predictive models Transformers

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VALHALLA: Visual Hallucination for Machine Translation

VALHALLA: Visual Hallucination for Machine Translation

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Li, Yi Panda, Rameswar Kim, Yoon Chen, Chun-Fu Richard Feris, Rogerio Cox, David Vasconcelos, Nuno Univ Calif San Diego La Jolla CA 92093 USA MIT IBM Watson AI Lab Cambridge MA USA MIT CSAIL Cambridge MA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Designing better machine translation systems by considering auxiliary inputs such as images has attracted much attention in recent years. While existing methods show promising performance over the conventional text-only translation systems, they typically require paired text and image as input during inference, which limits their applicability to real-world scenarios. In this paper, we introduce a visual hallucination framework, called VALHALLA, which requires only source sentences at inference time and instead uses hallucinated visual representations for mult-imodal machine translation. In particular, given a source sentence an autoregressive hallucination transformer is used to predict a discrete visual representation from the input text, and the combined text and hallucinated representations are utilized to obtain the target translation. We train the hallucination transformer jointly with the translation transformer using standard backpropagation with cross-entropy losses while being guided by an additional loss that encourages consistency between predictions using either ground-truth or hallucinated visual representations. Extensive experiments on three standard translation datasets with a diverse set of language pairs demonstrate the effectiveness of our approach over both text-only baselines and state-of-the-art methods. Project page: http: //***/projects/valhalla.

关键词： Backpropagation visualization Computer vision Transformers Pattern recognition Machine translation Standards

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A ConvNet for the 2020s

A ConvNet for the 2020s

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Liu, Zhuang Mao, Hanzi Wu, Chao-Yuan Feichtenhofer, Christoph Darrell, Trevor Xie, Saining Facebook AI Res FAIR Menlo Pk CA 94025 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model. A vanilla ViT, on the other hand, faces difficulties when applied to general computer vision tasks such as object detection and semantic segmentation. It is the hierarchical Transformers (e.g., Swin Transformers) that reintroduced several ConvNet priors, making Transformers practically viable as a generic vision backbone and demonstrating remarkable performance on a wide variety of vision tasks. However, the effectiveness of such hybrid approaches is still largely credited to the intrinsic superiority of Transformers, rather than the inherent inductive biases of convolutions. In this work, we reexamine the design spaces and test the limits of what a pure ConvNet can achieve. We gradually "modernize" a standard ResNet toward the design of a vision Transformer, and discover several key components that contribute to the performance difference along the way. The outcome of this exploration is a family of pure ConvNet models dubbed ConvNeXt. Constructed entirely from standard ConvNet modules, ConvNeXts compete favorably with Transformers in terms of accuracy and scalability, achieving 87.8% ImageNet top-1 accuracy and outperforming Swin Transformers on COCO detection and ADE20K segmentation, while maintaining the simplicity and efficiency of standard ConvNets.

关键词： Computer vision Image segmentation visualization Computational modeling Scalability Semantics Transformers

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Explainable System for Suggestion Mining using attention 8

Explainable System for Suggestion Mining using attention

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8th International conference on Advanced Computing and Communication Systems, ICACCS 2022

作者： Laskari, Naveen Kumar Sanampudi, Suresh Kumar IT Bvrit Hyderabad Cew Telangana India Jntu Hyderabad Telangana India It Jntuh College of Engineering Jagtial Nachupally Telangana India

ISBN: (纸本)9781665408165

Deep Neural Network (DNN) models have shown remarkable results in Natural Language Processing(NLP) and Computer Vision(CV) applications. Convolutional Neural Networks (CNN), variants of Recurrent Neural Networks (RNN), and Transformer models are the architectures used for this. RNNs can capture the long-term dependencies but suffer from vanishing gradient problems. Long short-term memory networks (LSTM) and gated recurrent units (GRU) are introduced to overcome the limitations of traditional recurrent networks. Bi-directional information processing in combination with recurrent structure made to capture the past and future. All the components of the input sequence may not be relevant and essential in generating the target. An attention mechanism has been used to identify the crucial chunks from the input. visualization of attention weights can describe the interpretability of model learning. Suggestion mining is treated as a text classification task to classify the given sentence into a suggestion or non-suggestion. This paper presents explainable systems using different RNNs and hybrid models for suggestion mining with and without attention components in the model. The experimental results on the standard datasets demonstrate that the local and global attention model outperforms. The heat map visualization on the input sentence also shows that the Bi-LSTM model with attention mechanism correctly captures the suggestive intent words. © 2022 ieee.

关键词： visualization

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Credit Card Fraud Detection Using Symbolic Aggregate Approximation 6

Credit Card Fraud Detection Using Symbolic Aggregate Approxi...

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6th International conference on Blockchain Computing and Applications, BCCA 2024

作者： Zerea, Snit D. Woldemichael, Belsabel T. Araya, Feven B. Ikuesan, Richard A. College of Technological Innovation Zayed University Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350351538

In the current digital age, credit card fraud is a cybercrime that costs billions of dollars every year through fraudulent transactions. This study aims to identify fraudulent credit card transactions using Symbolic Aggregate Approximation (SAX) and anomaly discovery through motifs and discords. To do this, credit card data is used as time series data and then divided into equal-length segments. Each segment is then converted into a symbolic representation using a pre-defined threshold. A web-based interface is further developed to load a collection of credit card transaction data, based on the proposed SAX method. The preliminary results show that the proposed approach is capable of identifying fraudulent transactions within a set of transactions. By integrating this approach with other fraud detection mechanisms, a near real-time fraud detection process can be developed. This approach therefore provides a potential for context-independent fraud detection which is a major challenge in the fraud detection domain. © 2024 ieee.

关键词： Credit Card Fraud Detection Fraud visualization Motif Discovery Symbolic Aggregate Approximation

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