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VANP: Learning Where to See for Navigation with Self-Supervised Vision-Action Pre-Training

VANP: Learning Where to See for Navigation with Self-Supervi...

引用

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

作者： Mohammad Nazeri Junzhe Wang Amirreza Payandeh Xuesu Xiao Department of Computer Science George Mason University

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Humans excel at efficiently navigating through crowds without collision by focusing on specific visual regions relevant to navigation. However, most robotic visual navigation methods rely on deep learning models pre-trained on vision tasks, which prioritize salient objects—not necessarily relevant to navigation and potentially misleading. Alternative approaches train specialized navigation models from scratch, requiring significant computation. On the other hand, self-supervised learning has revolutionized computer vision and natural language processing, but its application to robotic navigation remains underexplored due to the difficulty of defining effective self-supervision signals. Motivated by these observations, in this work, we propose a Self-Supervised Vision-Action Model for visual Navigation Pre-Training (VANP). Instead of detecting salient objects that are beneficial for tasks such as classification or detection, VANP learns to focus only on specific visual regions that are relevant to the navigation task. To achieve this, VANP uses a history of visual observations, future actions, and a goal image for self-supervision, and embeds them using two small Transformer Encoders. Then, VANP maximizes the information between the embeddings by using a mutual information maximization objective function. We demonstrate that most VANP-extracted features match with human navigation intuition. VANP achieves comparable performance as models learned end-to-end with half the training time and models trained on a large-scale, fully supervised dataset, i.e., imageNet, with only 0.08% data. 1

关键词： Training visualization Navigation Computational modeling Self-supervised learning Transformers Linear programming Data models Natural language processing Mutual information

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Flexible Autoencoder for Orthogonal Chirp Division Multiplexing communications

Flexible Autoencoder for Orthogonal Chirp Division Multiplex...

引用

ieee International conference on Signal and image processing (ICSIP)

作者： Min-Sung Koh Department of Computer Science and Electrical Engineering Eastern Washington University Cheney WA USA

This paper introduces a flexible autoencoder (AE) designed for orthogonal chirp division multiplexing (OCDM). The AE is based on a deep neural network (DNN) and a flexible structure of OCDM, which can accommodate various structures for, such as orthogonal frequency division multiplexing, chirp spread spectrum, affine frequency division multiplexing etc. The AE is designed by jointly training a mapper and a demapper made up of a DNN in the flexible structure. Comparing with traditional M-ary quadrature amplitude modulation (MQAM) systems, the trained AE shows different shapes of constellation for additive white Gaussian noise channels and outperforms the traditional MQAMs in bit error rates.

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Performance of Linear Coding and Transmission in Low-Latency Computer Vision Offloading

Performance of Linear Coding and Transmission in Low-Latency...

引用

ieee conference on Wireless communications and Networking

作者： Jakub Žadník Anthony Trioux Michel Kieffer Markku Mäkitalo François-Xavier Coudoux Patrick Corlay Pekka Jääskeläinen Tampere University Finland UMR 8520-IEMN UPHF CNRS Univ. LIlle. YNCREA Centrale Lille France Laboratoire des Signaux et Systèmes Univ. Paris-Saclay CNRS CentraleSupélec France

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

image communication increasingly involves machine-to-machine delivery. For example, images acquired by an autonomous drone can be compressed and sent to an edge server over a wireless network for resource-intensive processing. Traditional compression techniques involving transform, quantization, and entropy coding reach high compression efficiency, but channel conditions worse than expected may lead to a sharp decrease in the decoded image quality. As an alternative, Linear Coding and Transmission (LCT) systems have been proposed to avoid this digital cliff problem: The reconstructed image quality decreases gradually as channel conditions degrade. This paper presents a comprehensive evaluation of computer vision tasks with input images processed and transmitted using LCT. It also analyses the benefits of network retraining, accounting for impairments due to LCT and noisy channel. Considering object detection and semantic segmentation over images transmitted and received by LCT systems, we show that the task accuracy degrades smoothly when the channel quality decreases, avoiding the cliff effect. Retraining with noisy images processed by LCT restores detection mAP degradation from 23.8% to 4.4% and segmentation mIoU degradation from 43.2% to 8.1 % when the channel signal-to-noise ratio is 10 dB.

关键词： image quality image coding Accuracy Computational modeling Semantic segmentation Transforms Object detection

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The Method and Devices for Research the Parameters of the Human visual System to Video Quality Assessment

The Method and Devices for Research the Parameters of the Hu...

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2021 Systems of Signals Generating and processing in the Field of on Board communications, SOSG 2021

作者： Mozhaeva, A.I. Vlasuyk, I.V. Potashnikov, A.M. Cree, Michael J. Streeter, Lee Moscow Technical University of Communications and Informatics Moscow Russia The University of Waikato Hamilton New Zealand

ISBN: (纸本)9780738130897

The susceptibility of distortions introduced by compression from the point of view of human perception is an important area of research. The ultimate purpose of video quality assessment is to emulate the performance of the biological visual system. Perceptual distortion models have proven successful in describing spatial coding in all human visual systems. However, the question of how the visual regions of the human brain process temporal information have received significantly less attention. Based on the researches of limit to human vision, here we offer a method, software, and test equipment for research and measure the characteristics of human visual systems. This set forms and evaluates stimuli with a specified combination of spatial, temporal, and color characteristics, which differ in a narrowband enough spectrum for measurements. © 2021 ieee.

关键词： Software testing

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Improving Real-Time Near-Infrared Face Alignment With a Paired VIS-NIR Dataset and Data Augmentation Through image-to-image Translation

Improving Real-Time Near-Infrared Face Alignment With a Pair...

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ieee International conference on image processing

作者： Langning Miao Ryo Kakimoto Kaoru Ohishi Yoshihiro Watanabe Department of Information and Communications Engineering Tokyo Institute of Technology Japan Research Laboratories KOSÉ Corporation Japan

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Real-time near-infrared (NIR) face alignment holds significant importance across various domains, such as security, healthcare, and augmented reality. However, existing face alignment techniques tailored for visible-light (VIS) encounter a decline in accuracy when applied in NIR settings. This decline stems from the domain discrepancy between VIS and NIR facial domains and the absence of meticulously annotated NIR facial data. To address this issue, we introduce a system and strategy for gathering paired VIS-NIR facial images and meticulously annotating precise landmarks. Our system facilitates streamlined dataset preparation by utilizing automatic annotation transfer from VIS images to their corresponding NIR counterparts. Following our devised approach, we constructed an inaugural dataset comprising high-frame-rate paired VIS-NIR facial images with landmark annotations. Additionally, to enhance the diversity of facial data, we augment our dataset through VIS-NIR image-to-image (img2img) translation using publicly available facial landmark datasets. Through the retraining of face alignment models and subsequent evaluations, our findings demonstrate a noteworthy enhancement in the accuracy of face alignment under NIR conditions using our dataset. Furthermore, the augmented dataset exhibits refined accuracy, particularly notable in the case of different individuals’ facial features.

关键词： Accuracy Annotations Medical services Streaming media Data augmentation Real-time systems Robustness

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Expression Recognition Based on Attention Mechanism and Length Feature of Facial Landmark 13

Expression Recognition Based on Attention Mechanism and Leng...

引用

13th International conference on Wireless communications and Signal processing, WCSP 2021

作者： Yang, Qian Liu, Feng Zhao, Zhenglai Nanjing University of Posts and Telecommunications Nanjing China Nanjing University of Posts and Telecommunications Jiangsu Key Lab of Image Processing Image Communications Nanjing China

ISBN: (纸本)9781665407854

In view of the fact that the proportion of face in the whole image is small and the face posture is skew, which leads to the low accuracy of facial expression recognition, this paper proposes a two-branch neural network based on attention mechanism and length feature of facial landmark. The first branch is based on the modified VGG19 and the integration of the convolutional block attention module (CBAM), which focuses on the extraction of important features of expression. The second branch extracts 68 feature points from the face image, and calculates the Euler distance between these feature points to acquire the length information of 2278 dimensions, and is finally normalized and fed into the full connection layer. The network composed of the first branch and the second branch is trained by joint fine-tuning, where L2 regularization is added to prevent overfitting. The proposed method is tested and verified on Jaffe, CK + and FER +, and the highest accuracy is 93.48%,96.12% and 84.73% respectively. The performance of the proposed method is more advance than many existing algorithms, which shows that the proposed model has good accuracy and robustness. © 2021 ieee.

关键词： Face recognition

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CasDyF-Net: image Dehazing via Cascaded Dynamic Filters

CasDyF-Net: Image Dehazing via Cascaded Dynamic Filters

引用

International conference on Vision, image and Signal processing (ICVISP)

作者： Yinglong Wang Bin He School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

image dehazing aims to restore image clarity and visual quality by reducing atmospheric scattering and absorption effects. While deep learning has made significant strides in this area, more and more methods are constrained by network depth. Consequently, lots of approaches have adopted parallel branching strategies. however, they often prioritize aspects such as resolution, receptive field, or frequency domain segmentation without dynamically partitioning branches based on the distribution of input features. Inspired by dynamic filtering, we propose using cascaded dynamic filters to create a multi-branch network by dynamically generating filter kernels based on feature map distribution. To better handle branch features, we propose a residual multiscale block (RMB), combining different receptive fields. Furthermore, we also introduce a fusion method based attention mechanism to merge features from adjacent branches. Experiments on RESIDE, Haze4K, and O-Haze datasets validate our method's effectiveness, with our model achieving a PSNR of 43.21dB on the RESIDE-Indoor dataset. The code is available at https://***/dauing/CasDyF-Net.

关键词： visualization Filters Attention mechanisms image resolution Computational modeling Frequency-domain analysis Scattering Frequency diversity image restoration Kernel

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image based measurement of angular velocity of a turbine used for water surface wave energy conversion 19

Image based measurement of angular velocity of a turbine use...

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19th International Bhurban conference on Applied Sciences and Technology, IBCAST 2022

作者： Khan, Majid Janjua, Kashif Qureshi, Shafiq-Ur-Rehman Waqas, Adeel ESE Dept USPCSE USPCAS-E NUST H-12 Islamabad Pakistan Islamabad Pakistan Islamabad Pakistan ESE USPCASE USPCAS-E NUST H-12 Islamabad Pakistan

ISBN: (数字)9781665460514

ISBN: (纸本)9781665460514

Interest in using turbines for ocean wave energy conversion has recently raised owing to their numerous benefits in view of this application. However, the phenomenon of wave-turbine-interaction is complex and requires extensive experimental investigation. Out of the numerous physical parameters involved in the phenomenon, the rotational speed of the turbine carries a particular significance as it links the turbine power output with the characteristics of both the turbine and the waves. Therefore, it was examined with a particular focus in this study using a laboratory based wave flume. However, the angular velocity changes continually owing to variations in speed of water induced by the orbital motion of surface water caused by wave motion. Therefore, it was deemed necessary to measure the turbine instantaneous speed as a function of time. However, most of the commonly used equipment measure the number of complete revolutions per minute as opposed to the instantaneous speed of a rotating object. Consequently, a need arose to employ an unconventional approach in this case. After considering numerous options, the technique of image processing was used with encouraging results. The rotation of the turbine was captured in numerous video footages after affixing a marker to its end plate facing the 16 Mega Pixel camera having a frame capture rate of 30 per second. The results were compared with visual observations at key angular positions of the turbine which showed a good correlation. The use of image processing for measuring instantaneous rotational speed of an underwater turbine interacting with surface waves has not been reported in literature previously and can potentially play a fundamental role in further understanding the complex phenomenon of wave-turbine-interaction. It can therefore help in developing sustainable turbine based ocean waves energy conversion devices. © 2022 ieee.

关键词： image processing

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Bi-Predictive Intra Block Copy for Enhanced Video Coding Beyond VVC

Bi-Predictive Intra Block Copy for Enhanced Video Coding Bey...

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ieee International conference on image processing

作者： Yoshitaka Kidani Haruhisa Kato Kei Kawamura KDDI Research Inc. Fujimino-shi Saitama Japan

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Intra block copy (IBC), an intra coding tool with a single block vector (BV), has been exploited for significant coding gains of screen content (SC) in advanced video coding standards such as VVC. Several studies have applied IBC to camera-captured content (CC), such as the IBC with fractional-sample-precision BV, which was adopted into the reference software for exploring beyond VVC, i.e., the enhanced compression model (ECM). However, there is room to further achieve the coding gains of IBC because all the conventional methods are uni-predictive IBC with a single BV to generate prediction samples. This paper proposes a bi-predictive IBC using two BVs as a new IBC algorithm for CC and SC, realized by extending the number of BVs in BV storage. In addition, this paper proposes encoder early terminations of applying IBC for CC by comparing coefficients and distortions of the IBC and intra prediction to avoid encoder runtime increases while maintaining coding gains. Experimental results show that the proposed method brings $0.15 \%$ and $0.30 \%$ coding gains for CC and SC over ECM-9 under all-intra configuration, with negligible complexity increases. The proposed method has been adopted into ECM-10.

关键词： Video coding image coding Runtime Prediction algorithms Distortion Vectors Encoding

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Relational Graph-Bridged image-Text Interaction: A Novel Method for Multi-Modal Relation Extraction

Relational Graph-Bridged Image-Text Interaction: A Novel Met...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Zihao Zheng Tao He Ming Liu Zhongyuan Wang Ruiji Fu Bing Qin Harbin Institute of Technology Harbin China Peng Cheng Laboratory Shenzhen China Kuaishou Technology Beijing China

Multi-modal relation extraction (MRE) requires the integration of multi-modal information to identify relationships between entities. Although fine-grained correlations between visual objects and textual words have the potential to improve cross-modal interaction, they are typically modeled implicitly and hindered by the modality gap. This paper introduces a novel method called relational Graph-Bridged cross-modal InTeraction (GBIT). GBIT aims to model fine-grained cross-modal correlations into the interaction process explicitly. This is achieved by constructing a fine-grained cross-modal relational graph, which acts as a bridge for effective cross-modal interaction in multiple layers. Within GBIT, a gated interaction strategy and an adaptive integration module are proposed for irrelevance-filtered information exchange and final information collation. Through extensive experiments on the benchmark MRE, we demonstrate the superiority of our proposed method for MRE.

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