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Force Control During the Precision Grip Translates to Virtual Reality 44

Force Control During the Precision Grip Translates to Virtua...

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44th Annual International conference of the ieee-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Guenterl, Clara Liu, Yiming Leib, Raz Franklin, David Tech Univ Munich Dept Sport & Hlth Sci Neuromuscular Diagnost Munich Germany Tech Univ Munich Munich Inst Robot & Machine Intelligence Munich Germany

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

When grasping and manipulating objects we implicitly adapt grip forces according to the physical parameters of the object. We integrate visual, cutaneous, and force feedback to estimate these parameters and adapt our control accordingly. Using virtual reality, both feedback integration and control can be investigated in ways that are not possible using real-life objects. Here, we present our custom-built virtual reality setup and show its validity for use in human studies of fine motor control. Participants grasped and lifted virtual objects with different weights. We show that, consistent with lifting real objects, all participants adapt their grip forces to the object mass, and do so on a trial-by-trial basis. Compared to similar studies with real objects and full feedback, grip forces were increased, and adaptation required more trials. This study successfully demonstrated that grip force control in the precision grip translates to virtual reality. While our setup can be used for similar work in the future, subsequent virtual reality experiments should include a longer adaptation phase compared to classic setups.

关键词： visualization Motor drives Force feedback Virtual reality Grasping Biology Force control

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Stochastic Resonance Governs Memory Consolidation Accuracy in a Neural Network Model 44

Stochastic Resonance Governs Memory Consolidation Accuracy i...

引用

44th Annual International conference of the ieee-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Caston, Rose M. Wilson, Matthew G. Comeaux, Phillip D. Dorval, Alan D. Univ Utah Dept Neurosurg & Biomed Engn Salt Lake City UT 84112 USA Univ Utah Dept Biomed Engn Salt Lake City UT 84112 USA

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

The formation and recollection of memories is a multi-step neural process subject to errors. We propose a computational model of memory nodes receiving input from a colored tic-tac-toe board. We report memory errors during consolidation and reconsolidation when different noise levels are introduced into the model. The model is based on Hebbian plasticity and attempts to store the color and position of an X or O from the board. Memory nodes simulating neurons use an integrate-and-fire model to represent the correct or incorrect storage of the board information by scaling synaptic weights. We explored how baseline firing rate, which we considered analogous to noise in storing memory, impacted the creation of correct and incorrect memories. We found that a higher firing rate was associated with fewer accurate memories. Interestingly, the ideal amount of noise for correct memory storage was nonzero. This phenomenon is known as stochastic resonance, wherein random noise enhances processing. We also examined how many times our model could reactivate a memory before making an error. We found an exponentially decaying response, with a low firing rate yielding more stable memories. Even though our model incorporates only two memory nodes, it provides a basis for examining the consolidation and retrieval of memory storage based on the unique visual input of a tic-tac-toe board. Further work may incorporate different inputs, more nodes, and increased network complexity.

关键词： visualization Firing Stochastic resonance Computational modeling Neurons Brain modeling Behavioral sciences

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Less is More: Generating Grounded Navigation Instructions from Landmarks

Less is More: Generating Grounded Navigation Instructions fr...

引用

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

作者： Wang, Su Montgomery, Ceslee Orbay, Jordi Birodkar, Vighnesh Faust, Aleksandra Gur, Izzeddin Jaques, Natasha Waters, Austin Baldridge, Jason Anderson, Peter Google Res Mountain View CA 94043 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We study the automatic generation of navigation instructions from 360 degrees images captured on indoor routes. Existing generators suffer from poor visual grounding, causing them to rely on language priors and hallucinate objects. Our MARKY-MT5 system addresses this by focusing on visual landmarks;it comprises a first stage landmark detector and a second stage generator-a multimodal, multilingual, multitask encoder-decoder. To train it, we bootstrap grounded landmark annotations on top of the Room-across-Room (RxR) dataset. Using text parsers, weak supervision from RxR's pose traces, and a multilingual image-text encoder trained on 1.8b images, we identify 971k English, Hindi and Telugu landmark descriptions and ground them to specific regions in panoramas. On Room-to-Room, human wayfinders obtain success rates (SR) of 71% following MARKY-MT5 's instructions, just shy of their 75% SR following human instructions-and well above SRs with other generators. Evaluations on RxR's longer, diverse paths obtain 61-64% SRs on three languages. Generating such high-quality navigation instructions in novel environments is a step towards conversational navigation tools and could facilitate larger-scale training of instruction following agents.

关键词： Training visualization Navigation Grounding Focusing Detectors Multitasking

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UV Optimization for Game Ready 3D Assets 6

UV Optimization for Game Ready 3D Assets

引用

6th International conference on information Technologies in Engineering Education, Inforino 2022

作者： Fedotov, Vasiliy G. Fedotov, Gennady V. Synergy University Faculty of Graphic Design Moscow Russia National Research University MPEI Department of Applied Mathematics and Artificial Intelligence Power Engineering Design Department Moscow Russia

ISBN: (纸本)9781665405775

Graphic technology rapidly evolve nowadays. The content creation becomes a very important direction and number of people involved in it grows every year. There are three main industries which employs specialists in computer graphics: film, games and visualisation. In this article we will consider important aspect of models creation: optimization for game engines. That includes modern game industry requirements for UV layout optimization. © 2022 ieee.

关键词： Industries Solid modeling visualization Three-dimensional displays Layout Games Manuals

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Fast MRI Reconstruction: How Powerful Transformers Are? 44

Fast MRI Reconstruction: How Powerful Transformers Are?

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44th Annual International conference of the ieee-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Huang, Jiahao Wu, Yinzhe Wu, Huanjun Yang, Guang Imperial Coll London Natl Heart & Lung Inst London SW7 2AZ England Royal Brompton Hosp Cardiovasc Res Ctr London SW3 6NP England Imperial Coll London Dept Bioengn London SW7 2AZ England

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

Magnetic resonance imaging (MRI) is a widely used non-radiative and non-invasive method for clinical interrogation of organ structures and metabolism, with an inherently long scanning time. Methods by k-space undersampling and deep learning based reconstruction have been popularised to accelerate the scanning process. This work focuses on investigating how powerful transformers are for fast MRI by exploiting and comparing different novel network architectures. In particular, a generative adversarial network (GAN) based Swin transformer (ST-GAN) was introduced for the fast MRI reconstruction. To further preserve the edge and texture information, edge enhanced GAN based Swin transformer (EES-GAN) and texture enhanced GAN based Swin transformer (TES-GAN) were also developed, where a dual-discriminator GAN structure was applied. We compared our proposed GAN based transformers, standalone Swin transformer and other convolutional neural networks based GAN model in terms of the evaluation metrics PSNR, SSIM and FID. We showed that transformers work well for the MRI reconstruction from different undersampling conditions. The utilisation of GAN's adversarial structure improves the quality of images reconstructed when undersampled for 30% or higher. The code is publicly available at https://***/ayanglab/SwinGANMR.

关键词： Measurement visualization Magnetic resonance imaging Biological system modeling Network architecture Transformers Generative adversarial networks

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OnePose: One-Shot Object Pose Estimation without CAD Models

OnePose: One-Shot Object Pose Estimation without CAD Models

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

作者： Sun, Jiaming Wang, Zihao Zhang, Siyu He, Xingyi Zhao, Hongcheng Zhang, Guofeng Zhou, Xiaowei Zhejiang Univ Hangzhou Peoples R China SenseTime Res Shanghai Peoples R China TUM Munich Germany

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose a new method named OnePose for object pose estimation. Unlike existing instance-level or category-level methods, OnePose does not rely on CAD models and can handle objects in arbitrary categories without instance-or category-specific network training. OnePose draws the idea from visual localization and only requires a simple RGB video scan of the object to build a sparse SfM model of the object. Men, this model is registered to new query images with a generic feature matching network. To mitigate the slow runtime of existing visual localization methods, we propose a new graph attention network that directly matches 2D interest points in the query image with the 3D points in the SJM model, resulting in efficient and robust pose estimation. Coinbined with a feature-based pose tracker, OnePose is able to stably detect and track 6D poses of everyday household objects in real-time. We also collected a large-scale dataset that consists of 450 sequences of 150 objects. Code and data are available at the project page: https://***/onepose/.

关键词： Training Location awareness Solid modeling visualization Three-dimensional displays Runtime Pose estimation

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VisualGPT: Data-efficient Adaptation of Pretrained Language Models for Image Captioning

VisualGPT: Data-efficient Adaptation of Pretrained Language ...

引用

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

作者： Chen, Jun Guo, Han Yi, Kai Li, Boyang Elhoseiny, Mohamed King Abdullah Univ Sci & Technol KAUST Thuwal Saudi Arabia Carnegie Mellon Univ Pittsburgh PA 15213 USA Nanyang Technol Univ Singapore Singapore

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The limited availability of annotated data often hinders real-world applications of machine learning. To efficiently learn from small quantities of multimodal data, we leverage the linguistic knowledge from a large pre-trained language model (PLM) and quickly adapt it to new domains of image captioning. To effectively utilize a pretrained model, it is critical to balance the visual input and prior linguistic knowledge from pretraining. We propose VisualGPT, which employs a novel self-resurrecting encoderdecoder attention mechanism to quickly adapt the PLM with a small amount of in-domain image-text data. The proposed self-resurrecting activation unit produces sparse activations that prevent accidental overwriting of linguistic knowledge. When trained on 0.1%, 0.5% and 1% of the respective training sets, VisualGPT surpasses the best baseline by up to 10.0% CIDEr on MS COCO [43] and 17.9% CIDEr on Conceptual Captions [63]. Furthermore, VisualGPT achieves the state-of-the-art result on IU X-ray [15], a medical report generation dataset. Our code is available at https : // ***/vision-CAIR/VisualGPT.

关键词： Training Representation learning Adaptation models visualization Computational modeling Semantics Linguistics

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InOut : Diverse Image Outpainting via GAN Inversion

InOut : Diverse Image Outpainting via GAN Inversion

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

作者： Cheng, Yen-Chi Lin, Chieh Hubert Lee, Hsin-Ying Ren, Jian Tulyakov, Sergey Yang, Ming-Hsuan Carnegie Mellon Univ Pittsburgh PA 15213 USA UC Merced Merced CA USA Snap Inc Santa Monica CA USA Yonsei Univ Seoul South Korea Google Res Mountain View CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Image outpainting seeks for a semantically consistent extension of the input image beyond its available content. Compared to inpainting - filling in missing pixels in a way coherent with the neighboring pixels - outpainting can be achieved in more diverse ways since the problem is less constrained by the surrounding pixels. Existing image outpainting methods pose the problem as a conditional image-to-image translation task, often generating repetitive structures and textures by replicating the content available in the input image. In this work, we formulate the problem from the perspective of inverting generative adversarial networks. Our generator renders micro-patches conditioned on their joint latent code as well as their individual positions in the image. To outpaint an image, we seek for multiple latent codes not only recovering available patches but also synthesizing diverse outpainting by patch-based generation. This leads to richer structure and content in the outpainted regions. Furthermore, our formulation allows for outpainting conditioned on the categorical input, thereby enabling flexible user controls. Extensive experimental results demonstrate the proposed method performs favorably against existing in- and outpainting methods, featuring higher visual quality and diversity.

关键词： Representation learning visualization Computer vision Codes Generative adversarial networks Generators Filling

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WiSARD: A Labeled Visual and Thermal Image Dataset for Wilderness Search and Rescue

WiSARD: A Labeled Visual and Thermal Image Dataset for Wilde...

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ieee/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Broyles, Daniel Hayner, Christopher R. Leung, Karen Univ Washington Dept Aeronaut & Astronaut Seattle WA 98195 USA NVIDIA Santa Clara CA USA

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

Sensor-equipped unoccupied aerial vehicles (UAVs) have the potential to help reduce search times and alleviate safety risks for first responders carrying out Wilderness Search and Rescue (WiSAR) operations, the process of finding and rescuing person(s) lost in wilderness areas. Unfortunately, visual sensors alone do not address the need for robustness across all the possible terrains, weather, and lighting conditions that WiSAR operations can be conducted in. The use of multi-modal sensors, specifically visual-thermal cameras, is critical in enabling WiSAR UAVs to perform in diverse operating conditions. However, due to the unique challenges posed by the wilderness context, existing dataset benchmarks are inadequate for developing vision-based algorithms for autonomous WiSAR UAVs. To this end, we present WiSARD, a dataset with roughly 56,000 labeled visual and thermal images collected from UAV flights in various terrains, seasons, weather, and lighting conditions. To the best of our knowledge, WiSARD is the first large-scale dataset collected with multi-modal sensors for autonomous WiSAR operations. We envision that our dataset will provide researchers with a diverse and challenging benchmark that can test the robustness of their algorithms when applied to real-world (life-saving) applications. Link to dataset: https://***/***/wisard/

关键词： visualization Multimodal sensors Lighting Benchmark testing Robot sensing systems Cameras Robustness

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Deep Saliency Prior for Reducing Visual Distraction

Deep Saliency Prior for Reducing Visual Distraction

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

作者： Aberman, Kfir He, Junfeng Gandelsman, Yossi Mosseri, Inbar Jacobs, David E. Kohlhoff, Kai Pritch, Yael Rubinstein, Michael Google Res Mountain View CA 94043 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Using only a model that was trained to predict where people look at images, and no additional training data, we can produce a range of powerful editing effects for reducing distraction in images. Given an image and a mask specifying the region to edit, we backpropagate through a state-of-the-art saliency model to parameterize a differentiable editing operator, such that the saliency within the masked region is reduced. We demonstrate several operators, including: a recoloring operator, which learns to apply a color transform that camouflages and blends distractors into their surroundings;a warping operator, which warps less salient image regions to cover distractors, gradually collapsing objects into themselves and effectively removing them (an effect akin to inpainting);a GAN operator, which uses a semantic prior to fully replace image regions with plausible, less salient alternatives. The resulting effects are consistent with cognitive research on the human visual system (e.g., since color mismatch is salient, the recoloring operator learns to harmonize objects' colors with their surrounding to reduce their saliency). And importantly, all effects are achieved under a zero-shot learning scenario, solely through the guidance of the pretrained saliency model, with no supervised data of the effects. We present results on a variety of natural images and conduct a perceptual study to evaluate and validate the changes in viewers' eye-gaze between the original images and our edited results. Project Webpage: https://***/

关键词： visualization Computer vision Image color analysis Semantics Training data Transforms Visual systems

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