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Exemplar-based Pattern Synthesis with Implicit Periodic Field Network

Exemplar-based Pattern Synthesis with Implicit Periodic Fiel...

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

作者： Chen, Haiwei Liu, Jiayi Chen, Weikai Liu, Shichen Zhao, Yajie Univ Southern Calif Los Angeles CA 90089 USA USC Inst Creat Technol Los Angeles CA 90094 USA Tencent Game AI Res Ctr Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Synthesis of ergodic, stationary visual patterns is widely applicable in texturing, shape modeling, and digital content creation. The wide applicability of this technique thus requires the pattern synthesis approaches to be scalable, diverse, and authentic. In this paper, we propose an exemplar-based visual pattern synthesis framework that aims to model the inner statistics of visual patterns and generate new, versatile patterns that meet the aforementioned requirements. To this end, we propose an implicit network based on generative adversarial network (GAN) and periodic encoding, thus calling our network the Implicit Periodic Field Network (IPFN). The design of IPFN ensures scalability: the implicit formulation directly maps the input coordinates to features, which enables synthesis of arbitrary size and is computationally efficient for 3D shape synthesis. Learning with a periodic encoding scheme encourages diversity: the network is constrained to model the inner statistics of the exemplar based on spatial latent codes in a periodic field. Coupled with continuously designed GAN training procedures, IPFN is shown to synthesize tileable patterns with smooth transitions and local variations. Last but not least, thanks to both the adversarial training technique and the encoded Fourier features, IPFN learns high frequency functions that produce authentic, high-quality results. To validate our approach, we present novel experimental results on various applications in 2D texture synthesis and 3D shape synthesis.

关键词： Training Representation learning Deep learning visualization Three-dimensional displays Shape Scalability

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HiMODE: A Hybrid Monocular Omnidirectional Depth Estimation Model

HiMODE: A Hybrid Monocular Omnidirectional Depth Estimation ...

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

作者： Junayed, Masum Shah Sadeghzadeh, Arezoo Islam, Md Baharul Wong, Lai-Kuan Aydin, Tarkan Bahcesehir Univ Istanbul Turkey Amer Univ Malta Cospicua Malta Multimedia Univ Cyberjaya Malaysia

ISBN: (纸本)9781665487399

Monocular omnidirectional depth estimation is receiving considerable research attention due to its broad applications for sensing 360 degrees surroundings. Existing approaches in this field suffer from limitations in recovering small object details and data lost during the ground-truth depth map acquisition. In this paper, a novel monocular omnidirectional depth estimation model, namely HiMODE is proposed based on a hybrid CNN+Transformer (encoder-decoder) architecture whose modules are efficiently designed to mitigate distortion and computational cost, without performance degradation. Firstly, we design a feature pyramid network based on the HNet block to extract high-resolution features near the edges. The performance is further improved, benefiting from a self and cross attention layer and spatial/temporal patches in the Transformer encoder and decoder, respectively. Besides, a spatial residual block is employed to reduce the number of parameters. By jointly passing the deep features extracted from an input image at each backbone block, along with the raw depth maps predicted by the transformer encoder-decoder, through a context adjustment layer, our model can produce resulting depth maps with better visual quality than the ground-truth. Comprehensive ablation studies demonstrate the significance of each individual module. Extensive experiments conducted on three datasets;Stanford3D, Matterport3D, and SunCG, demonstrate that HiMODE can achieve state-of-the-art performance for 360 degrees monocular depth estimation. Complete project code and supplementary materials are available at https://***/himode5008/HiMODE.

关键词： visualization Three-dimensional displays Estimation Lighting Computer architecture Feature extraction Transformers

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LAVT: Language-Aware Vision Transformer for Referring Image Segmentation

LAVT: Language-Aware Vision Transformer for Referring Image ...

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

作者： Yang, Zhao Wang, Jiaqi Tang, Yansong Chen, Kai Zhao, Hengshuang Torr, Philip H. S. Univ Oxford Oxford England Shanghai AI Lab Shanghai Peoples R China Univ Hong Kong Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Referring image segmentation is a fundamental vision-language task that aims to segment out an object referred to by a natural language expression from an image. One of the key challenges behind this task is leveraging the referring expression for highlighting relevant positions in the image. A paradigm for tackling this problem is to leverage a powerful vision-language ("cross-modal") decoder to fuse features independently extracted from a vision encoder and a language encoder. Recent methods have made remarkable advancements in this paradigm by exploiting Transformers as cross-modal decoders, concurrent to the Transformer's overwhelming success in many other vision-language tasks. Adopting a different approach in this work, we show that significantly better cross-modal alignments can be achieved through the early fusion of linguistic and visual features in intermediate layers of a vision Transformer encoder network. By conducting cross-modal feature fusion in the visual feature encoding stage, we can leverage the well-proven correlation modeling power of a Transformer encoder for excavating helpful multi-modal context. This way, accurate segmentation results are readily harvested with a light-weight mask predictor. Without bells and whistles, our method surpasses the previous state-of-the-art methods on RefCOCO, RefCOCO+, and G-Ref by large margins.

关键词： Image segmentation visualization Image coding Shape Linguistics Transformers Feature extraction

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Sketch2Vis: Generating Data visualizations from Hand-drawn Sketches with Deep Learning 20

Sketch2Vis: Generating Data Visualizations from Hand-drawn S...

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20th ieee International conference on Machine Learning and Applications (ICMLA)

作者： Teng, Zhongwei Fu, Quchen White, Jules Schmidt, Douglas C. Vanderbilt Univ Dept Comp Sci Nashville TN 37235 USA

ISBN: (纸本)9781665443371

Data visualization has become a vital tool to help people understand the driving forces behind real-world phenomena. Although the learning curve of visualization tools have been reduced, domain experts still often require significant amounts of training to use them effectively. To reduce this learning curve even further, this paper proposes Sketch2Vis, a novel solution using deep learning techniques and tools to generate the source code for multi-platform data visualizations automatically from hand-drawn sketches provided by domain experts, which is similar to how an expert might sketch on a cocktail napkin and ask a software engineer to implement the sketched visualization. This paper explores key challenges (such as model training) in generating visualization code from hand-drawn sketches since acquiring a large dataset of sketches paired with visualization source code is often prohibitively complicated. We present solutions for these problems and conduct experiments on three baseline models that demonstrate the feasibility of generating visualizations from hand-drawn sketches. The best models tested reach a structural accuracy of 95% in generating correct data visualization code from hand-drawn sketches of visualizations.

关键词： Transformer visualization Automation

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DEFEAT: Deep Hidden Feature Backdoor Attacks by Imperceptible Perturbation and Latent Representation Constraints

DEFEAT: Deep Hidden Feature Backdoor Attacks by Imperceptibl...

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

作者： Zhao, Zhendong Chen, Xiaojun Xuan, Yuexin Dong, Ye Wang, Dakui Liang, Kaitai Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Delft Univ Technol Delft Netherlands

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Backdoor attack is a type of serious security threat to deep learning models. An adversary can provide users with a model trained on poisoned data to manipulate prediction behavior in test stage using a backdoor. The backdoored models behave normally on clean images, yet can be activated and output incorrect prediction if the input is stamped with a specific trigger pattern. Most existing backdoor attacks focus on manually defining imperceptible triggers in input space without considering the abnormality of triggers' latent representations in the poisoned model. These attacks are susceptible to backdoor detection algorithms and even visual inspection. In this paper, We propose a novel and stealthy backdoor attack - DEFEAT. It poisons the clean data using adaptive imperceptible perturbation and restricts latent representation during training process to strengthen our attack's stealthiness and resistance to defense algorithms. We conduct extensive experiments on multiple image classifiers using real-world datasets to demonstrate that our attack can I) hold against the state-of-the-art defenses, 2) deceive the victim model with high attack success without jeopardizing model utility, and 3) provide practical stealthiness on image data.

关键词： Training Resistance Representation learning Adaptation models visualization Toxicology Perturbation methods

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LiDAR-Forest Dataset: LiDAR Point Cloud Simulation Dataset for Forestry Application

LiDAR-Forest Dataset: LiDAR Point Cloud Simulation Dataset f...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), ieee conference on

作者： Yawen Lu Zhuoyang Sun Jinyuan Shao Qianyu Guo Yunhan Huang Songlin Fei Victor Chen Polytechnic Institute Purdue University USA Department of Forestry and Natural Resources Purdue University USA

ISBN: (数字)9798350374490

ISBN: (纸本)9798350374506

The popularity of LiDAR devices and sensor technology has gradually empowered users from autonomous driving to forest monitoring, and research on 3D LiDAR has made remarkable progress over the years. Unlike 2D images, whose focused area is visible and rich in texture information, understanding the point distribution can help companies and researchers find better ways to develop point-based 3D applications. In this work, we contribute an unreal-based LiDAR simulation tool and a 3D simulation dataset named LiDAR-Forest, which can be used by various studies to evaluate forest reconstruction, tree DBH estimation, and point cloud compression for easy visualization. The simulation is customizable in tree species, LiDAR types and scene generation, with low cost and high efficiency.

关键词： Point cloud compression Solid modeling visualization Laser radar Three-dimensional displays Computational modeling Forestry

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MLP-3D: A MLP-like 3D Architecture with Grouped Time Mixing

MLP-3D: A MLP-like 3D Architecture with Grouped Time Mixing

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

作者： Qiu, Zhaofan Yao, Ting Ngo, Chong-Wah Mei, Tao JD Explore Acad Beijing Peoples R China Singapore Management Univ Singapore Singapore

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Convolutional Neural Networks (CNNs) have been regarded as the go-to models for visual recognition. More recently, convolution-free networks, based on multi-head self-attention (MSA) or multi-layer perceptrons (MLPs), become more and more popular. Nevertheless, it is not trivial when utilizing these newly-minted networks for video recognition due to the large variations and complexities in video data. In this paper, we present MLP-3D networks, a novel MLP-like 3D architecture for video recognition. Specifically, the architecture consists of MLP-3D blocks, where each block contains one MLP applied across tokens (i.e., token-mixing MLP) and one MLP applied independently to each token (i.e., channel MLP). By deriving the novel grouped time mixing (GTM) operations, we equip the basic token-mixing MLP with the ability of temporal modeling. GTM divides the input tokens into several temporal groups and linearly maps the tokens in each group with the shared projection matrix. Furthermore, we devise several variants of GTM with different grouping strategies, and compose each variant in different blocks of MLP-3D network by greedy architecture search. Without the dependence on convolutions or attention mechanisms, our MLP-3D networks achieves 68.5%/81.4% top-1 accuracy on Something-Something V2 and Kinetics-400 datasets, respectively. Despite with fewer computations, the results are comparable to state-of-the-art widely-used 3D CNNs and video transformers.

关键词： visualization Computer vision Three-dimensional displays Computer architecture Transformers Pattern recognition Complexity theory

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Explainability Tools Enabling Deep Learning in Future In-Situ Real-Time Planetary Explorations

Explainability Tools Enabling Deep Learning in Future In-Sit...

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ieee Aerospace conference (AERO)

作者： Lundstrom, Daniel Huyen, Alexander Mevada, Arya Yun, Kyongsik Lu, Thomas Univ Southern Calif 3620 S Vermont AveKAP 104 Los Angeles CA 90089 USA CALTECH Jet Prop Lab 4800 Oak Grove Dr Pasadena CA 91109 USA CALTECH 1200 E Calif Blvd MSC 643 Pasadena CA 91125 USA

ISBN: (数字)9781665437608

ISBN: (纸本)9781665437608

Deep learning (DL) has proven to be an effective machine learning and computer vision technique. DL-based image segmentation, object recognition and classification will aid many in-situ Mars rover tasks such as path planning and artifact recognition/extraction. However, most of the Deep Neural Network (DNN) architectures are so complex that they are considered a 'black box'. In this paper, we used integrated gradients to describe the attributions of each neuron to the output classes. It provides a set of explainability tools (ET) that opens the black box of a DNN so that the individual contribution of neurons to category classification can be ranked and visualized. The neurons in each dense layer are mapped and ranked by measuring expected contribution of a neuron to a class vote given a true image label. The importance of neurons is prioritized according to their correct or incorrect contribution to the output classes and suppression or bolstering of incorrect classes, weighted by the size of each class. ET provides an interface to prune the network to enhance high-rank neurons and remove low-performing neurons. ET technology will make DNNs smaller and more efficient for implementation in small embedded systems. It also leads to more explainable and testable DNNs that can make systems easier for Validation & Verification. The goal of ET technology is to enable the adoption of DL in future in-situ planetary exploration missions.

关键词： Deep learning Space vehicles Mars visualization Neurons Neural networks Real-time systems

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Hypothesis Testing of Gaussian Naïve Bayes Classifier for Liver Disease Classification 2

Hypothesis Testing of Gaussian Naïve Bayes Classifier for L...

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2nd ieee International conference on Futuristic Technologies, INCOFT 2023

作者： Gill, Kanwarpartap Singh Anand, Vatsala Chauhan, Rahul Choudhary, Ankur Gupta, Rupesh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Uttarakhand Dehradun India Graphic Era Hill University Punjab India Graphic Era Deemed to Be University Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350308846

Liver classification often involves the systematic categorization of liver problems or illnesses, taking into account diverse aspects including aetiology, severity, histological characteristics, and clinical manifestations. In the context of scientific inquiry, liver classification pertains to the systematic grouping of liver tissues or disorders according to diverse criteria, with the aim of investigating and comprehending problems associated with the liver. The prevalence of liver disease among patients has shown a steady increase due to excessive alcohol use, exposure to hazardous gases, consumption of contaminated foods such as pickles and cucumbers, and availability of drugs in developing nations. In an effort to assist the pool of experts, this dataset was used to evaluate forecasting systems. Numerous approaches and tactics have been developed via extensive study to facilitate the detection of liver problems. The advancement of various processes and approaches aimed at aiding doctors in the accurate and expeditious diagnosis of liver problems has mostly emerged from research pertaining to the detection of hepatic ailments. The use of this intervention has resulted in enhanced patient outcomes and a better quality of life for individuals. The Gaussian Naive Bayes classifier proposed in this work demonstrates strong performance on the given dataset. Additionally, the findings are presented using scatter plots and joint points, which are essential methodologies in the field of machine learning. © 2023 ieee.

关键词： Confusion matrix Convolutional Neural Network Gaussian Naïve Bayes Liver Disease Classification visualization

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Autonomous Mobile Robot as a Cyber-Physical System Featuring Networked Deep Learning and Control

Autonomous Mobile Robot as a Cyber-Physical System Featuring...

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ieee/ASME International conference on Advanced Intelligent Mechatronics (AIM)

作者： Lin, Hong-Zhi Chen, Hsin-Hung Choophutthakan, Kerk Li, Chih-Hung G. Natl Taipei Univ Technol Grad Inst Mech Engn Taipei 10608 Taiwan Natl Taipei Univ Technol Grad Inst Mfg Technol Taipei 10608 Taiwan

ISBN: (数字)9781665413084

ISBN: (纸本)9781665413084

In this article, we proposed an autonomous mobile robot (AMR) capable of conducting multiple deep learning inferences by a computing network as a Cyber-Physical System (CPS). In view of industry 4.0, a five-layered CPS architecture including Component, Intelligence, Cyber, Configuration, and Deployment was devised. Mechatronic control of the two-wheeled self-balancing AMR presents the first challenge to embedded computing, whereas multiple deep learning modules for visual navigation and localization further escalate the stake. To meet the challenge of real-time and affordable computing, we constructed an embedded cluster based on Raspberry Pi4s and Intel NCS2 for simultaneous intelligent inference and data exchange. We tested the performances of three networking methods, Network File System (NFS), Socket Server with Multiple Clients (SSMC), and ROS TOPIC (ROS-T), and reported the comparison. We also conducted a series of field tests on autonomous corridor navigation, localization, object delivery, and object pick-and-place to verify the performance of the self-balancing AMR. The results showed high success rates with sufficient spatial precision, stability, and robust loop closure. The web-based console realizes the functions of the digital twin and provides connectivity to higher-level management systems.

关键词： Deep learning Location awareness visualization Mechatronics Navigation Computer architecture Supervisory control

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