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检索条件"任意字段=Conference on Image Reconstruction from Incomplete Data"

共 14747 条记录，以下是281-290 订阅

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imageInWords: Unlocking Hyper-Detailed image Descriptions

ImageInWords: Unlocking Hyper-Detailed Image Descriptions

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2024 conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Garg, Roopal Burns, Andrea Ayan, Burcu Karagol Bitton, Yonatan Montgomery, Ceslee Onoe, Yasumasa Bunner, Andrew Krishna, Ranjay Baldridge, Jason Soricut, Radu Google DeepMind United Kingdom University of Washington United States

ISBN: (纸本)9798891761643

Despite the longstanding adage "an image is worth a thousand words," generating accurate hyper-detailed image descriptions remains unsolved. Trained on short web-scraped image-text, vision-language models often generate incomplete descriptions with visual inconsistencies. We address this via a novel data-centric approach with imageInWords (IIW), a carefully designed human-in-the-loop framework for curating hyper-detailed image descriptions. Human evaluations on IIW data show major gains compared to recent datasets (+66%) and GPT-4V (+48%) across comprehensiveness, specificity, hallucinations, and more. We also show that fine-tuning with IIW data improves these metrics by +31% against models trained with prior work, even with only 9k samples. Lastly, we evaluate IIW models with text-to-image generation and vision-language reasoning tasks. Our generated descriptions result in the highest fidelity images, and boost compositional reasoning by up to 6% on ARO, SVO-Probes, and Winoground datasets. We release the IIW-Eval benchmark with human judgement labels, object and image-level annotations from our framework, and existing image caption datasets enriched via IIW-model. © 2024 Association for Computational Linguistics.

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Segmentation-Guided Neural Radiance Fields for Foreground Object 3D reconstruction

Segmentation-Guided Neural Radiance Fields for Foreground Ob...

引用

2024 International conference on Electronics, Information, and Communication, ICEIC 2024

作者： Kim, Seon Bin Ko, Byoung Chul Keimyung University Dept. of Computer Engineering Korea Republic of

ISBN: (纸本)9798350371888

In recent years, Neural Radiance Fields (NeRF) is attracting attention for its excellent performance in reconstructing 3D scenes from 2D images by capturing volumetric scene representations and radiance properties. However, NeRF is challenged with the concurrent rendering of both foreground and background components, which raises computational complexity issues for scene reconstruction. In response to these challenges, our approach combines image segmentation to delineate objects with precision. By focusing NeRF exclusively on foreground objects during training, we optimize its rendering capacity. This integration achieves contextually accurate 3D reconstructions, demonstrated with high-quality results. Through qualitative evaluation of various data, we show that removing the background improves 3D reconstruction precision as well as computation speed. © 2024 IEEE.

关键词： image segmentation

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Rotational Millimeter-Wave Shoe Scanner Using the Discrete Fourier Transform for Backprojection-Based image reconstruction 28

Rotational Millimeter-Wave Shoe Scanner Using the Discrete F...

引用

conference on Radar Sensor Technology XXVIII

作者： Clark, R. Trevor McDaid, Stephanie L. Sheen, David M. Pacific Northwest Natl Lab US DOE Battelle Mem Inst POB 999 Richland WA 99352 USA

ISBN: (纸本)9781510674158;9781510674141

An active 3D microwave / millimeter-wave shoe scanner was previously developed at the Pacific Northwest National Laboratory (PNNL) using two linear arrays scanned over a rectilinear aperture. The radar system chirps a frequency sweep from 10-40 GHz. These frequencies allow imaging through optically opaque material such as leather, rubber, plastics, and other dielectrics. The system was designed to detect concealed items in the soles of shoes while allowing people to leave their shoes on through a security checkpoint. To shrink the footprint of the system, a new iteration of the design has been developed that scans the two linear arrays over a circular aperture. This new footprint opens the possibility of it being installed in the floor of a cylindrical millimeter-wave body scanner. The backprojection-based multilayer dielectric image reconstruction developed at PNNL can easily handle arbitrary spatial sampling, accommodating the new rotational shoe scanner design. Commonly, the fast Fourier transform (FFT) is used to efficiently compute the range response from the data collected by the system as a preprocessing step to the backprojection algorithm. It was found that converting to range using the discrete Fourier transform (DFT) directly has some advantages over the FFT. For example, nonlinear and non-uniform frequency sweeps can easily be compensated for during the computation of the DFT and only the range bins of interest need to be computed and their spacing can be chosen arbitrarily. Because the range conversion step of the image reconstruction is the fastest part of the process there is very little speed penalty for using the DFT over the FFT and it can even increase the speed of image reconstruction when the ranges of interest are fewer than the total span that is calculated in the FFT.

关键词： Millimeter-wave imaging microwave imaging shoe scanner concealed-weapon detection security screening Discrete Fourier Transform backprojection

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Electromagnetic Inverse Scattering Problem Solved by DConvNet and Adapted Attention U-Net 12

Electromagnetic Inverse Scattering Problem Solved by DConvNe...

引用

12th IEEE International conference on Communication Systems and Network Technologies, CSNT 2023

作者： Saladi, Pravallika Kalepu, Yaswanth Kurnool India

ISBN: (纸本)9781665462617

A two-stage Deep Learning strategy for resolving the electromagnetic inverse scattering problem (ISP) is proposed in this paper. The two steps involve utilizing Deep Convolutional Neural Network (DConvNet) to draw out features from the Scattered field data and refine the image reconstruction using adapted version of Attention U-Net. We observed that our proposed method delivered better results in terms of image quality and reconstruction precision. Moreover, we observed the outcomes of Attention U-Net, DConvNet, and DRCNN for ISP and found that Attention U-Net gave better image refinement. © 2023 IEEE.

关键词： image reconstruction

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A Method of SAR Target data Augmentation Based on Component-Level Perturbation 2

A Method of SAR Target Data Augmentation Based on Component-...

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2nd IEEE International conference on Signal, Information and data Processing, ICSIDP 2024

作者： Jin, Rubo Cheng, Jianda Wang, Wei Xiang, Deliang Jiang, Chunyun Zhang, Jun National University of Defense Technology College of Electronic Science and Technology Changsha China Linyi University School of Automation and Electrical Engineering Shandong China Beijing University of Chemical Technology College of Information Science and Technology Beijing China

ISBN: (纸本)9798331515669

Large-scale, diverse Synthetic Aperture Radar (SAR) image data plays a crucial role in Automatic Target Recognition (ATR). Directly acquiring SAR image data faces significant challenges in practical operation and acquisition costs, while methods such as electromagnetic simulation and image processing for obtaining image data also have insurmountable difficulties. Therefore, intelligent augmentation methods based on Variational Autoencoder (VAE) and Generative Adversarial Network (GAN) have been widely applied in the field of SAR image data augmentation. However, these methods are based on pixel-level augmentation, which not only deviates from the scattering mechanism of SAR targets but also tends to lose the structure and semantic information of the targets. To address this issue, we propose a component-based SAR image data augmentation method. We extract features from real SAR images, decouple the component information of the targets and perturb it, and finally obtain augmented data through attention mechanisms and reconstruction networks. This method not only reduces the adverse effects caused by background interference and occlusion on the targets but also helps to enhance the discriminability between different categories of targets, increasing the recognition rate of the targets on the dataset introduced in this paper by 2.56%. © 2024 IEEE.

关键词： Synthetic aperture radar

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Occupancy Grid Artefact Removal and Error Correction using GANs 4

Occupancy Grid Artefact Removal and Error Correction using G...

引用

4th International conference on Computer, Control and Robotics (ICCCR)

作者： Davies, Leon Li, Baihua Saada, Mohamad Solvsten, Simon Meng, Qinggang Loughborough Univ Dept Comp Sci Loughborough Leics England Univ Southern Denmark European Ctr Risk & Resilience Studies Esbjerg Denmark

ISBN: (纸本)9798350373141;9798350373158

Occupancy Grid Mapping is a form of Simultaneous Localisation and Mapping (SLAM) in which the world around a robot is visually represented as a grid map. This form of map can be compared to a floor plan in which features within an environment such as walls are labelled in place. Certain issues such as noise, artefacts, linear error, angular error, and incomplete rooms make this representation difficult to appropriate. Generative Adversarial Networks (GAN) [1] in the past have proven successful in and reliable methods for noise reduction, artefact removal [2], and partial observation completion [3]. We demonstrate a novel data creation process to mass produce samples of erroneous and ideal occupancy grid maps. We use this data to build two GAN models based on well-known frameworks CycleGAN [4] and CUT [5] for the task of occupancy grid cleaning. We demonstrate the generalisability of our models through making predictions of 'clean' maps on samples of real data from the Radish dataset [6].

关键词： Machine Learning SLAM Deep Learning GAN Deep Reinforcement Learning image to image translation CycleGan CUT dataset

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JR2Net: Joint Monocular 3D Face reconstruction and Reenactment 37

JR2Net: Joint Monocular 3D Face Reconstruction and Reenactme...

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37th AAAI conference on Artificial Intelligence (AAAI) / 35th conference on Innovative Applications of Artificial Intelligence / 13th Symposium on Educational Advances in Artificial Intelligence

作者： Shang, Jiaxiang Zeng, Yu Qiao, Xin Wang, Xin Zhang, Runze Sun, Guangyuan Patel, Vishal Fu, Hongbo Hong Kong Univ Sci & Technol Hong Kong Peoples R China Johns Hopkins Univ Baltimore MD USA Tencent Shenzhen Peoples R China City Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781577358800

Face reenactment and reconstruction benefit various applications in self-media, VR, etc. Recent face reenactment methods use 2D facial landmarks to implicitly retarget facial expressions and poses from driving videos to source images, while they suffer from pose and expression preservation issues for cross-identity scenarios, i.e., when the source and the driving subjects are different. Current self-supervised face reconstruction methods also demonstrate impressive results. However, these methods do not handle large expressions well, since their training data lacks samples of large expressions, and 2D facial attributes are inaccurate on such samples. To mitigate the above problems, we propose to explore the inner connection between the two tasks, i.e., using face reconstruction to provide sufficient 3D information for reenactment, and synthesizing videos paired with captured face model parameters through face reenactment to enhance the expression module of face reconstruction. In particular, we propose a novel cascade framework named JR2Net for Joint Face reconstruction and Reenactment, which begins with the training of a coarse reconstruction network, followed by a 3D-aware face reenactment network based on the coarse reconstruction results. In the end, we train an expression tracking network based on our synthesized videos composed by image-face model parameter pairs. Such an expression tracking network can further enhance the coarse face reconstruction. Extensive experiments show that our JR2Net outperforms the state-of-the-art methods on several face reconstruction and reenactment benchmarks.

关键词： image reconstruction

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EyeIR: Single Eye image Inverse Rendering In the Wild 24

EyeIR: Single Eye Image Inverse Rendering In the Wild

引用

SIGGRAPH conference on Emerging Technologies

作者： Liang, Shijun Wang, Haofei Lu, Feng Beihang Univ State Key Lab VRTS Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798400705250

We propose a method to decompose a single eye region image in the wild into albedo, shading, specular, normal and illumination. This inverse rendering problem is particularly challenging due to inherent ambiguities and complex properties of the natural eye region. To address this problem, first we construct a synthetic eye region dataset with rich diversity. Then we propose a synthetic to real adaptation framework to leverage the supervision signals from synthetic data to guide the direction of self-supervised learning. We design region-aware self-supervised losses based on image formation and eye region intrinsic properties, which can refine each predicted component by mutual learning and reduce the artifacts caused by ambiguities of natural eye images. Particularly, we address the demanding problem of specularity removal in the eye region. We show high-quality inverse rendering results of our method and demonstrate its use for a number of applications.

关键词： Inverse rendering intrinsic image decomposition shape reconstruction domain adaptation unsupervised learning

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Stereo image-based Visual Servoing Towards Feature-based Grasping

Stereo Image-based Visual Servoing Towards Feature-based Gra...

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IEEE International conference on Robotics and Automation (ICRA)

作者： Enyedy, Albert Aswale, Ashay calli, Berk Gennert, Michael Worcester Polytech Inst Dept Robot Engn Worcester MA 01609 USA

ISBN: (纸本)9798350384581;9798350384574

This paper presents an image-based visual servoing scheme that can control robotic manipulators in 3D space using 2D stereo images without needing to perform stereo reconstruction. We use a stereo camera in an eye-to-hand configuration for controlling the robot to reach target positions by directly mapping image space errors to joint space actuation. We achieve convergence without a-priori knowledge of the target object, a reference 2D image, or 3D data. By doing so, we can reach targets in unstructured environments using high-resolution RGB images instead of utilizing relatively noisy depth data. We conduct several experiments on two different physical robots. The Panda 7DOF arm grasps a static target in 3D space, grasps a pitcher handle, and picks and places a box by determining the approach angle using 2D image features, demonstrating that this algorithm can be used for grasping practical objects in 3D space using only 2D image features for feedback. Our second platform, the Atlas humanoid robot, reaches a target from an unknown starting configuration, demonstrating that this controller achieves convergence to a target, even with the uncertainties introduced by walking to a new location. We believe that this algorithm is a step towards enabling intuitive interfaces that allow a user to initiate a grasp on an object by specifying a grasping point in a 2D image.

关键词： Visual servoing

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Object-level Scene Deocclusion 24

Object-level Scene Deocclusion

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SIGGRAPH conference on Emerging Technologies

作者： Liu, Zhengzhe Liu, Qing Chang, Chirui Zhang, Jianming Pakhomov, Daniil Zheng, Haitian Lin, Zhe Cohen-Or, Daniel Fu, Chi-Wing Chinese Univ Hong Kong Hong Kong Peoples R China Adobe San Jose CA USA Univ Hong Kong Hong Kong Peoples R China Tel Aviv Univ Tel Aviv Israel

ISBN: (纸本)9798400705250

Deoccluding the hidden portions of objects in a scene is a formidable task, particularly when addressing real-world scenes. In this paper, we present a new self-supervised PArallel visible-to-COmplete diffusion framework, named PACO, a foundation model for object-level scene deocclusion. Leveraging the rich prior of pre-trained models, we first design the parallel variational autoencoder, which produces a full-view feature map that simultaneously encodes multiple complete objects, and the visible-to-complete latent generator, which learns to implicitly predict the full-view feature map from partial-view feature map and text prompts extracted from the incomplete objects in the input image. To train PACO, we create a large-scale dataset with 500k samples to enable self-supervised learning, avoiding tedious annotations of the amodal masks and occluded regions. At inference, we devise a layer-wise deocclusion strategy to improve efficiency while maintaining the deocclusion quality. Extensive experiments on COCOA and various real-world scenes demonstrate the superior capability of PACO for scene deocclusion, surpassing the state of the arts by a large margin. Our method can also be extended to cross-domain scenes and novel categories that are not covered by the training set. Further, we demonstrate the deocclusion applicability of PACO in single-view 3D scene reconstruction and object recomposition. Project page: https://***/***/.

关键词： scene deocclusion object completion image recomposition

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