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检索条件"任意字段=2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005"

共 6538 条记录，以下是301-310 订阅

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Lidar Positioning for Indoor Precision Navigation

Lidar Positioning for Indoor Precision Navigation

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Holmberg, Max Karlsson, Oskar Tulldahl, Michael Swedish Def Res Agcy FOI Linkoping Sweden

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Lidar based simultaneous localization and mapping methods can be adapted for deployment on small autonomous vehicles operating in unmapped indoor environments. For this purpose, we propose a method which combines inertial data, low-drift lidar odometry, planar primitives, and loop closing in a graph-based structure. The accuracy of our method is experimentally evaluated, using a high-resolution lidar, and compared to the state-of-the-art methods LIO-SAM and Cartographer. We specifically address the lateral positioning accuracy when passing through narrow openings, where high accuracy is a prerequisite for safe operation of autonomous vehicles. The test cases include doorways, slightly wider reference passages, and a larger corridor environment. We observe a reduced lateral accuracy for all three methods when passing through the narrow openings compared to operation in larger spaces. Compared to state-of-the-art, our method shows better results in the narrow passages, and comparable results in the other environments with reasonably low usage of CPU and memory resources.

关键词： computer vision Laser radar Simultaneous localization and mapping Navigation conferences Random access memory pattern recognition

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ShapeFormer: Transformer-based Shape Completion via Sparse Representation

ShapeFormer: Transformer-based Shape Completion via Sparse R...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Yan, Xingguang Lin, Liqiang Mitra, Niloy J. Lischinski, Dani Cohen-Or, Daniel Huang, Hui Shenzhen Univ Shenzhen Peoples R China UCL London England Adobe Res London England Hebrew Univ Jerusalem Jerusalem Israel Tel Aviv Univ Tel Aviv Israel

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present ShapeFormer, a transformer-based network that produces a distribution of object completions, conditioned on incomplete, and possibly noisy, point clouds. The resultant distribution can then be sampled to generate likely completions, each exhibiting plausible shape details while being faithful to the input. To facilitate the use of transformers for 3D, we introduce a compact 3D representation, vector quantized deep implicit function (VQDIF), that utilizes spatial sparsity to represent a close approximation of a 3D shape by a short sequence of discrete variables. Experiments demonstrate that ShapeFormer outperforms prior art for shape completion from ambiguous partial inputs in terms of both completion quality and diversity. We also show that our approach effectively handles a variety of shape types, incomplete patterns, and real-world scans.

关键词： Point cloud compression computer vision Three-dimensional displays Limiting Shape computer architecture Diversity methods

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ScanpathNet: A Recurrent Mixture Density Network for Scanpath Prediction

ScanpathNet: A Recurrent Mixture Density Network for Scanpat...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： de Belen, Ryan Anthony Jalova Bednarz, Tomasz Sowmya, Arcot Univ New South Wales Sydney NSW Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Understanding the mechanisms underlying human visual attention is an important research problem in cognitive neuroscience and computer vision. While existing models predict salient regions (i.e., saliency maps) and temporal sequences of eye fixations (i.e., scanpaths) in images, their designs often partially follow theoretical frameworks. Here, we introduce ScanpathNet, a deep learning model inspired by the latest theoretical model in neuroscience. It is 'guided' by a dynamic priority map influenced by semantic content and fixation history. The model leverages convolutional neural networks to extract rich semantic features, convolutional long short-term memory networks to model the inhibition of return mechanism and sequential dependencies of fixations, and mixture density networks to predict probability distributions of fixations for each pixel. Simulated human scanpaths can then be generated by sequentially sampling the output of the proposed model. Despite its simplicity, ScanpathNet showed promising qualitative and quantitative scanpath prediction performance in extensive experiments on numerous eye-tracking benchmark datasets.

关键词： Deep learning Visualization computer vision Computational modeling Semantics Predictive models Visual systems

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Scene Representation in Bird's-Eye View from Surrounding Cameras with Transformers

Scene Representation in Bird's-Eye View from Surrounding Cam...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhao, Yun Zhang, Yu Gong, Zhan Zhu, Hong Inspur Elect Informat Ind Co Ltd Dept AI & HPC Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Scene representation in the bird's-eye-view (BEV) coordinate frame provides a succinct and effective way to understand surrounding environments for autonomous vehicles and robotics. In this work, we present an end-to-end architecture to generate the BEV representation from surrounding cameras. To generate the BEV representation, we propose a transformer-based encoder-decoder structure to translate the image features from different cameras into the BEV frame, which takes advantage of the context information in the individual image and the relationship between images in different views. We perform multiple semantic segmentation tasks using the BEV features. Experimental results show that our model outperforms the competitive baseline [20], which demonstrates the effectiveness and efficiency of our method.

关键词： Image segmentation Image coding Computational modeling Semantics Robot vision systems Predictive models Cameras

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Contrastive Learning-based Robust Object Detection under Smoky Conditions

Contrastive Learning-based Robust Object Detection under Smo...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wu, Wei Chang, Hao Zheng, Yonghua Li, Zhu Chen, Zhiwen Zhang, Ziheng Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China Univ Missouri Dept Comp Sci & Elect Engn Kansas City MO 64110 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Object detection is to effectively find out interested targets in images and then accurately determine their categories and positions. Recently many excellent methods have been developed to provide powerful detection capability. However, their performance may degrade significantly under severe weather such as smoky conditions. In this paper, we propose a contrastive learning-based robust object detection algorithm for smoke images. The proposed object detector consists of two modules: contrastive learning module and object bounding box prediction module. The first module learns representation vectors by maximizing agreement between different augmented views of the same smoke image. These representations are then sent to the second module to yield the bounding box for each object. In addition, we also propose a novel affine data augmentation method. Extensive experiments have been conducted on A2I2-Haze dataset which is the first real haze dataset with in-situ smoke measurement aligned to aerial and ground imagery. This dataset is also the only dataset used in the 5th UG(2)+ challenges of cvpr 2022 for both training and testing. Compared with state-of-the-art methods, evaluation results show the superiority of our proposed object detector.

关键词： Training computer vision conferences Object detection Detectors Transforms Prediction algorithms

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PhoneDepth: A Dataset for Monocular Depth Estimation on Mobile Devices

PhoneDepth: A Dataset for Monocular Depth Estimation on Mobi...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Benavides, Fausto Tapia Ignatov, Andrey Timofte, Radu Swiss Fed Inst Technol Zurich Switzerland JMU Wurzburg Wurzburg Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Monocular depth estimation has been studied as a classic and learning based computer vision problem for decades. However, little attention received the efficiency and the deployment of methods on mobile hardware. All publicly available datasets have severe limitations related to their applicability to camera data captured with real mobile devices. For instance, the main issues with current datasets include (but not exhaustively) low quality of images due the cameras or collection methods, domain specifically generated datasets as is the case for autonomous driving, small number of samples, sparse depthmaps, etc. In response, we introduce PhoneDepth, a novel dataset that aims to take advantage of modern phones hardware and professional stereo cameras. Depthmaps are acquired from three sources: Time of Flight sensor, Dual Pixel sensor and stereo camera;while the images correspond to mobile phone photos. We prove its high value by training neural networks with multiple depth supervision, fine-tuning on other datasets and for depth refinement. Along with the dataset we present benchmark models and a toolbox to facilitate the dataset usage.

关键词： Training computer vision Three-dimensional displays Estimation Cameras Mobile handsets Hardware

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Guiding Attention using Partial-Order Relationships for Image Captioning

Guiding Attention using Partial-Order Relationships for Imag...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Popattia, Murad Rafi, Muhammad Qureshi, Rizwan Nawaz, Shah Natl Univ Comp & Emerging Sci Karachi Pakistan Hamad Bin Khalifa Univ Doha Qatar Ist Italiano Tecnol IIT Pattern Anal & Comp Vis PAVIS Genoa Italy DESY Hamburg Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The use of attention models for automated image captioning has enabled many systems to produce accurate and meaningful descriptions for images. Over the years, many novel approaches have been proposed to enhance the attention process using different feature representations. In this paper, we extend this approach by creating a guided attention network mechanism, that exploits the relationship between the visual scene and text-descriptions using spatial features from the image, high-level information from the topics, and temporal context from caption generation, which are embedded together in an ordered embedding space. A pairwise ranking objective is used for training this embedding space which allows similar images, topics and captions in the shared semantic space to maintain a partial order in the visual-semantic hierarchy and hence, helps the model to produce more visually accurate captions. The experimental results based on MSCOCO dataset shows the competitiveness of our approach, with many state-of-the-art models on various evaluation metrics.

关键词： Training Measurement Visualization computer vision conferences Semantics computer architecture

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Can we trust bounding box annotations for object detection?

Can we trust bounding box annotations for object detection?

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Murrugarra-Llerena, Jeffri Kirsten, L. N. Jung, Claudio R. Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Object detection is a classical problem in computer vision, and the vast majority of approaches require large annotated datasets for training and evaluation purposes. The most popular representations are bounding boxes (BBs), usually defined as the minimal-area rectangle that encompasses the whole object region. However, the annotation process presents some subjectiveness (particularly when occlusions are present), and its quality might get degraded when the annotators get tired. Comparing BBs is crucial for evaluation purposes, and the Intersection-over-Union (IoU) is the standard similarity metric. In this paper, we provide theoretical and experimental results indicating that the IoU can be strongly affected even by small annotation discrepancies in popular datasets used for object detection. As a consequence, the Average Precision (AP) value commonly used to evaluate object detectors is also influenced by annotation bias or noise, particularly for small objects and tighter IoU thresholds.

关键词： Degradation Training computer vision Annotations Object detection Detectors Size measurement

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Reasoning with Multi-Structure Commonsense Knowledge in Visual Dialog

Reasoning with Multi-Structure Commonsense Knowledge in Visu...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Shunyu Jiang, Xiaoze Yang, Zequn Wan, Tao Qin, Zengchang Beihang Univ Sch ASEE Intelligent Comp & Machine Learning Lab Beijing Peoples R China Beihang Univ Beijing Adv Innovat Ctr Biomed Engn Sch BSME Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Visual Dialog requires an agent to engage in a conversation with humans grounded in an image. Many studies on Visual Dialog focus on the understanding of the dialog history or the content of an image, while a considerable amount of commonsense-required questions are ignored. Handling these scenarios depends on logical reasoning that requires commonsense priors. How to capture relevant commonsense knowledge complementary to the history and the image remains a key challenge. In this paper, we propose a novel model by Reasoning with Multi-structure Commonsense Knowledge (RMK). In our model, the external knowledge is represented with sentence-level facts and graph-level facts, to properly suit the scenario of the composite of dialog history and image. On top of these multi-structure representations, our model can capture relevant knowledge and incorporate them into the vision and semantic features, via graph-based interaction and transformer-based fusion. Experimental results and analysis on VisDial v1.0 and VisDialCK datasets show that our proposed model effectively outperforms comparative methods.

关键词： Visualization Fuses Semantics Knowledge based systems Oral communication Transformers pattern recognition

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Cascaded Siamese Self-supervised Audio to Video GAN

Cascaded Siamese Self-supervised Audio to Video GAN

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Aldausari, Nuha Sowmya, Arcot Marcus, Nadine Mohammadi, Gelareh Univ New South Wales Sch Comp Sci & Engn Sydney NSW Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Generating meaningful videos that are synchronised to audio signals is a complex synthesis task that requires generation of not only realistic videos but also coherent video motions that conform to the provided audio signals. While tremendous effort has been expended on audio-to-video generative models, these models rely heavily on supervised signals such as face/body key points or 3D meshes. However, key point annotation requires time and effort. Besides, some dataset domains do not have predictable structure, which makes the extraction of points of interest infeasible. Our proposed model consists of a cascaded generator-discriminator architecture that works at the pixel level to generate videos according to the associated soundtracks. It adopts a new self-supervised temporal augmentation technique to optimise the correlation between the audio signal and the generated video instead of relying on supervised signals. The proposed architecture has proven its effectiveness in extensive experiments that compared different models across two datasets.

关键词： Solid modeling Correlation Three-dimensional displays Computational modeling conferences computer architecture pattern recognition

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