动态神经网络是深度学习中的新兴的研究课题。与具有推断阶段的固定计算图和参数的静态模型相比,动态网络可以使其结构或参数适应不同的输入,从而在本调查中的准确性,计算效率,适应性等方面的显着优势。我们全面地通过将动态网络分为三个主要类别:1)使用数据相关的架构或参数进行处理的实例 - Wise-Wise DiveS动态模型的速度开发区域2)关于图像数据的不同空间位置和3)沿着诸如视频和文本的顺序数据的时间维度执行自适应推断的时间明智的动态模型进行自适应计算的空间 - 方向动态网络。系统地审查了动态网络的重要研究问题,例如架构设计,决策方案,优化技术和应用。最后,我们与有趣的未来研究方向讨论了该领域的开放问题。
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人类自然有效地在复杂的场景中找到突出区域。通过这种观察的动机,引入了计算机视觉中的注意力机制,目的是模仿人类视觉系统的这一方面。这种注意机制可以基于输入图像的特征被视为动态权重调整过程。注意机制在许多视觉任务中取得了巨大的成功,包括图像分类,对象检测,语义分割,视频理解,图像生成,3D视觉,多模态任务和自我监督的学习。在本调查中,我们对计算机愿景中的各种关注机制进行了全面的审查,并根据渠道注意,空间关注,暂时关注和分支注意力进行分类。相关的存储库https://github.com/menghaoguo/awesome-vision-tions致力于收集相关的工作。我们还建议了未来的注意机制研究方向。
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数据冗余在深神经网络(DNN)的输入和中间结果中无处不在。它为提高DNN性能和效率提供了许多重要的机会,并在大量工作中探索了。这些研究在几年中都在许多场所散布。他们关注的目标范围从图像到视频和文本,以及他们用于检测和利用数据冗余的技术在许多方面也有所不同。尚无对许多努力进行系统的检查和摘要,使研究人员很难对先前的工作,最新技术,差异和共享原则以及尚未探索的领域和方向进行全面看法。本文试图填补空白。它调查了有关该主题的数百篇论文,引入了一种新颖的分类法,以将各种技术纳入一个单一的分类框架,对用于利用数据冗余的主要方法进行了全面描述,以改善数据的多种DNN,并指出一组未来探索的研究机会。
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深度学习技术在各种任务中都表现出了出色的有效性,并且深度学习具有推进多种应用程序(包括在边缘计算中)的潜力,其中将深层模型部署在边缘设备上,以实现即时的数据处理和响应。一个关键的挑战是,虽然深层模型的应用通常会产生大量的内存和计算成本,但Edge设备通常只提供非常有限的存储和计算功能,这些功能可能会在各个设备之间差异很大。这些特征使得难以构建深度学习解决方案,以释放边缘设备的潜力,同时遵守其约束。应对这一挑战的一种有希望的方法是自动化有效的深度学习模型的设计,这些模型轻巧,仅需少量存储,并且仅产生低计算开销。该调查提供了针对边缘计算的深度学习模型设计自动化技术的全面覆盖。它提供了关键指标的概述和比较,这些指标通常用于量化模型在有效性,轻度和计算成本方面的水平。然后,该调查涵盖了深层设计自动化技术的三类最新技术:自动化神经体系结构搜索,自动化模型压缩以及联合自动化设计和压缩。最后,调查涵盖了未来研究的开放问题和方向。
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Astounding results from Transformer models on natural language tasks have intrigued the vision community to study their application to computer vision problems. Among their salient benefits, Transformers enable modeling long dependencies between input sequence elements and support parallel processing of sequence as compared to recurrent networks e.g., Long short-term memory (LSTM). Different from convolutional networks, Transformers require minimal inductive biases for their design and are naturally suited as set-functions. Furthermore, the straightforward design of Transformers allows processing multiple modalities (e.g., images, videos, text and speech) using similar processing blocks and demonstrates excellent scalability to very large capacity networks and huge datasets. These strengths have led to exciting progress on a number of vision tasks using Transformer networks. This survey aims to provide a comprehensive overview of the Transformer models in the computer vision discipline. We start with an introduction to fundamental concepts behind the success of Transformers i.e., self-attention, large-scale pre-training, and bidirectional feature encoding. We then cover extensive applications of transformers in vision including popular recognition tasks (e.g., image classification, object detection, action recognition, and segmentation), generative modeling, multi-modal tasks (e.g., visual-question answering, visual reasoning, and visual grounding), video processing (e.g., activity recognition, video forecasting), low-level vision (e.g., image super-resolution, image enhancement, and colorization) and 3D analysis (e.g., point cloud classification and segmentation). We compare the respective advantages and limitations of popular techniques both in terms of architectural design and their experimental value. Finally, we provide an analysis on open research directions and possible future works. We hope this effort will ignite further interest in the community to solve current challenges towards the application of transformer models in computer vision.
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Due to object detection's close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance easily stagnates by constructing complex ensembles which combine multiple low-level image features with high-level context from object detectors and scene classifiers. With the rapid development in deep learning, more powerful tools, which are able to learn semantic, high-level, deeper features, are introduced to address the problems existing in traditional architectures. These models behave differently in network architecture, training strategy and optimization function, etc. In this paper, we provide a review on deep learning based object detection frameworks. Our review begins with a brief introduction on the history of deep learning and its representative tool, namely Convolutional Neural Network (CNN). Then we focus on typical generic object detection architectures along with some modifications and useful tricks to improve detection performance further. As distinct specific detection tasks exhibit different characteristics, we also briefly survey several specific tasks, including salient object detection, face detection and pedestrian detection. Experimental analyses are also provided to compare various methods and draw some meaningful conclusions. Finally, several promising directions and tasks are provided to serve as guidelines for future work in both object detection and relevant neural network based learning systems.
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深度学习属于人工智能领域,机器执行通常需要某种人类智能的任务。类似于大脑的基本结构,深度学习算法包括一种人工神经网络,其类似于生物脑结构。利用他们的感官模仿人类的学习过程,深入学习网络被送入(感官)数据,如文本,图像,视频或声音。这些网络在不同的任务中优于最先进的方法,因此,整个领域在过去几年中看到了指数增长。这种增长在过去几年中每年超过10,000多种出版物。例如,只有在医疗领域中的所有出版物中覆盖的搜索引擎只能在Q3 2020中覆盖所有出版物的子集,用于搜索术语“深度学习”,其中大约90%来自过去三年。因此,对深度学习领域的完全概述已经不可能在不久的将来获得,并且在不久的将来可能会难以获得难以获得子场的概要。但是,有几个关于深度学习的综述文章,这些文章专注于特定的科学领域或应用程序,例如计算机愿景的深度学习进步或在物体检测等特定任务中进行。随着这些调查作为基础,这一贡献的目的是提供对不同科学学科的深度学习的第一个高级,分类的元调查。根据底层数据来源(图像,语言,医疗,混合)选择了类别(计算机愿景,语言处理,医疗信息和其他工程)。此外,我们还审查了每个子类别的常见架构,方法,专业,利弊,评估,挑战和未来方向。
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While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation, and the vision of the Internet of Things fuel the interest in resource-efficient approaches. These approaches aim for a carefully chosen trade-off between performance and resource consumption in terms of computation and energy. The development of such approaches is among the major challenges in current machine learning research and key to ensure a smooth transition of machine learning technology from a scientific environment with virtually unlimited computing resources into everyday's applications. In this article, we provide an overview of the current state of the art of machine learning techniques facilitating these real-world requirements. In particular, we focus on deep neural networks (DNNs), the predominant machine learning models of the past decade. We give a comprehensive overview of the vast literature that can be mainly split into three non-mutually exclusive categories: (i) quantized neural networks, (ii) network pruning, and (iii) structural efficiency. These techniques can be applied during training or as post-processing, and they are widely used to reduce the computational demands in terms of memory footprint, inference speed, and energy efficiency. We also briefly discuss different concepts of embedded hardware for DNNs and their compatibility with machine learning techniques as well as potential for energy and latency reduction. We substantiate our discussion with experiments on well-known benchmark datasets using compression techniques (quantization, pruning) for a set of resource-constrained embedded systems, such as CPUs, GPUs and FPGAs. The obtained results highlight the difficulty of finding good trade-offs between resource efficiency and predictive performance.
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随着深度学习(DL)的出现,超分辨率(SR)也已成为一个蓬勃发展的研究领域。然而,尽管结果有希望,但该领域仍然面临需要进一步研究的挑战,例如,允许灵活地采样,更有效的损失功能和更好的评估指标。我们根据最近的进步来回顾SR的域,并检查最新模型,例如扩散(DDPM)和基于变压器的SR模型。我们对SR中使用的当代策略进行了批判性讨论,并确定了有前途但未开发的研究方向。我们通过纳入该领域的最新发展,例如不确定性驱动的损失,小波网络,神经体系结构搜索,新颖的归一化方法和最新评估技术来补充先前的调查。我们还为整章中的模型和方法提供了几种可视化,以促进对该领域趋势的全球理解。最终,这篇综述旨在帮助研究人员推动DL应用于SR的界限。
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视觉变压器正在成为解决计算机视觉问题的强大工具。最近的技术还证明了超出图像域之外的变压器来解决许多与视频相关的任务的功效。其中,由于其广泛的应用,人类的行动识别是从研究界受到特别关注。本文提供了对动作识别的视觉变压器技术的首次全面调查。我们朝着这个方向分析并总结了现有文献和新兴文献,同时突出了适应变形金刚以进行动作识别的流行趋势。由于其专业应用,我们将这些方法统称为``动作变压器''。我们的文献综述根据其架构,方式和预期目标为动作变压器提供了适当的分类法。在动作变压器的背景下,我们探讨了编码时空数据,降低维度降低,框架贴片和时空立方体构造以及各种表示方法的技术。我们还研究了变压器层中时空注意的优化,以处理更长的序列,通常通过减少单个注意操作中的令牌数量。此外,我们还研究了不同的网络学习策略,例如自我监督和零局学习,以及它们对基于变压器的行动识别的相关损失。这项调查还总结了在具有动作变压器重要基准的评估度量评分方面取得的进步。最后,它提供了有关该研究方向的挑战,前景和未来途径的讨论。
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空间冗余广泛存在于视觉识别任务中,即图像或视频帧中的判别特征通常对应于像素的子集,而剩余区域与手头的任务无关。因此,在时间和空间消耗方面,处理具有相等计算量的所有像素的静态模型导致相当冗余。在本文中,我们将图像识别问题标准为顺序粗致细特征学习过程,模仿人类视觉系统。具体地,所提出的浏览和焦点网络(GFNET)首先以低分辨率比例提取输入图像的快速全局表示,然后策略性地参加一系列突出(小)区域以学习更精细的功能。顺序过程自然地促进了在测试时间的自适应推断,因为一旦模型对其预测充分信心,可以终止它,避免了进一步的冗余计算。值得注意的是,在我们模型中定位判别区域的问题被制定为增强学习任务,因此不需要除分类标签之外的其他手动注释。 GFNET是一般的,灵活,因为它与任何现成的骨干网型号(例如MobileCenets,Abservennet和TSM)兼容,可以方便地部署为特征提取器。对各种图像分类和视频识别任务的广泛实验以及各种骨干模型,证明了我们方法的显着效率。例如,它通过1.3倍降低了高效MobileNet-V3的平均等待时间,而不会牺牲精度。代码和预先训练的模型可在https://github.com/blackfeather-wang/gfnet-pytorch获得。
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注意力是一种令人震惊的状态,能够通过在一条信息上选择性地关注一个信息,同时忽略其他可察觉的信息,能够在人类中处理有限的处理瓶颈。几十年来,在哲学,心理学,神经科学和计算中研究了注意的概念和函数。目前,这家酒店已广泛探索深神经网络。现在可以使用许多不同的神经关注模型,并且在过去六年中是一个非常活跃的研究区域。从关注的理论观点来看,该调查对主要神经关注模型进行了批判性分析。在这里,我们提出了一种与预测深度学习的理论方面的分类学。我们的分类系统提供了一个组织结构,提出了新问题和结构对现有的注意机制的理解。特别地,17种来自心理学和神经科学的标准和神经科学经典研究的标准用于分析一组超过650篇论文的51个主要模型的定性比较和批判性分析。此外,我们突出了尚未探索的几个理论问题,包括讨论生物合理性,突出目前的研究趋势,并为未来提供见解。
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Image segmentation is a key topic in image processing and computer vision with applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among many others. Various algorithms for image segmentation have been developed in the literature. Recently, due to the success of deep learning models in a wide range of vision applications, there has been a substantial amount of works aimed at developing image segmentation approaches using deep learning models. In this survey, we provide a comprehensive review of the literature at the time of this writing, covering a broad spectrum of pioneering works for semantic and instance-level segmentation, including fully convolutional pixel-labeling networks, encoder-decoder architectures, multi-scale and pyramid based approaches, recurrent networks, visual attention models, and generative models in adversarial settings. We investigate the similarity, strengths and challenges of these deep learning models, examine the most widely used datasets, report performances, and discuss promising future research directions in this area.
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Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI) applications including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Accordingly, techniques that enable efficient processing of DNNs to improve energy efficiency and throughput without sacrificing application accuracy or increasing hardware cost are critical to the wide deployment of DNNs in AI systems.This article aims to provide a comprehensive tutorial and survey about the recent advances towards the goal of enabling efficient processing of DNNs. Specifically, it will provide an overview of DNNs, discuss various hardware platforms and architectures that support DNNs, and highlight key trends in reducing the computation cost of DNNs either solely via hardware design changes or via joint hardware design and DNN algorithm changes. It will also summarize various development resources that enable researchers and practitioners to quickly get started in this field, and highlight important benchmarking metrics and design considerations that should be used for evaluating the rapidly growing number of DNN hardware designs, optionally including algorithmic co-designs, being proposed in academia and industry.The reader will take away the following concepts from this article: understand the key design considerations for DNNs; be able to evaluate different DNN hardware implementations with benchmarks and comparison metrics; understand the trade-offs between various hardware architectures and platforms; be able to evaluate the utility of various DNN design techniques for efficient processing; and understand recent implementation trends and opportunities.
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基于无人机(UAV)基于无人机的视觉对象跟踪已实现了广泛的应用,并且由于其多功能性和有效性而引起了智能运输系统领域的越来越多的关注。作为深度学习革命性趋势的新兴力量,暹罗网络在基于无人机的对象跟踪中闪耀,其准确性,稳健性和速度有希望的平衡。由于开发了嵌入式处理器和深度神经网络的逐步优化,暹罗跟踪器获得了广泛的研究并实现了与无人机的初步组合。但是,由于无人机在板载计算资源和复杂的现实情况下,暹罗网络的空中跟踪仍然在许多方面都面临严重的障碍。为了进一步探索基于无人机的跟踪中暹罗网络的部署,这项工作对前沿暹罗跟踪器进行了全面的审查,以及使用典型的无人机板载处理器进行评估的详尽无人用分析。然后,进行板载测试以验证代表性暹罗跟踪器在现实世界无人机部署中的可行性和功效。此外,为了更好地促进跟踪社区的发展,这项工作分析了现有的暹罗跟踪器的局限性,并进行了以低弹片评估表示的其他实验。最后,深入讨论了基于无人机的智能运输系统的暹罗跟踪的前景。领先的暹罗跟踪器的统一框架,即代码库及其实验评估的结果,请访问https://github.com/vision4robotics/siamesetracking4uav。
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机器学习的进步为低端互联网节点(例如微控制器)带来了新的机会,将情报带入了情报。传统的机器学习部署具有较高的记忆力,并计算足迹阻碍了其在超资源约束的微控制器上的直接部署。本文强调了为MicroController类设备启用机载机器学习的独特要求。研究人员为资源有限的应用程序使用专门的模型开发工作流程,以确保计算和延迟预算在设备限制之内,同时仍保持所需的性能。我们表征了微控制器类设备的机器学习模型开发的广泛适用的闭环工作流程,并表明几类应用程序采用了它的特定实例。我们通过展示多种用例,将定性和数值见解介绍到模型开发的不同阶段。最后,我们确定了开放的研究挑战和未解决的问题,要求仔细考虑前进。
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神经网络(NNS)和决策树(DTS)都是机器学习的流行模型,但具有相互排斥的优势和局限性。为了带来两个世界中的最好,提出了各种方法来明确或隐式地集成NN和DTS。在这项调查中,这些方法是在我们称为神经树(NTS)的学校中组织的。这项调查旨在对NTS进行全面审查,并尝试确定它们如何增强模型的解释性。我们首先提出了NTS的彻底分类学,该分类法表达了NNS和DTS的逐步整合和共同进化。之后,我们根据NTS的解释性和绩效分析,并建议解决其余挑战的可能解决方案。最后,这项调查以讨论有条件计算和向该领域的有希望的方向进行讨论结束。该调查中审查的论文列表及其相应的代码可在以下网址获得:https://github.com/zju-vipa/awesome-neural-trees
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变压器是一种基于关注的编码器解码器架构,彻底改变了自然语言处理领域。灵感来自这一重大成就,最近在将变形式架构调整到计算机视觉(CV)领域的一些开创性作品,这已经证明了他们对各种简历任务的有效性。依靠竞争力的建模能力,与现代卷积神经网络相比在本文中,我们已经为三百不同的视觉变压器进行了全面的审查,用于三个基本的CV任务(分类,检测和分割),提出了根据其动机,结构和使用情况组织这些方法的分类。 。由于培训设置和面向任务的差异,我们还在不同的配置上进行了评估了这些方法,以便于易于和直观的比较而不是各种基准。此外,我们已经揭示了一系列必不可少的,但可能使变压器能够从众多架构中脱颖而出,例如松弛的高级语义嵌入,以弥合视觉和顺序变压器之间的差距。最后,提出了三个未来的未来研究方向进行进一步投资。
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计算机架构和系统已优化了很长时间,以便高效执行机器学习(ML)模型。现在,是时候重新考虑ML和系统之间的关系,并让ML转换计算机架构和系统的设计方式。这有一个双重含义:改善设计师的生产力,以及完成良性周期。在这篇论文中,我们对应用ML进行计算机架构和系统设计的工作进行了全面的审查。首先,我们考虑ML技术在架构/系统设计中的典型作用,即快速预测建模或设计方法,我们执行高级分类学。然后,我们总结了通过ML技术解决的计算机架构/系统设计中的常见问题,并且所用典型的ML技术来解决它们中的每一个。除了在狭义中强调计算机架构外,我们采用数据中心可被认为是仓库规模计算机的概念;粗略的计算机系统中提供粗略讨论,例如代码生成和编译器;我们还注意ML技术如何帮助和改造设计自动化。我们进一步提供了对机会和潜在方向的未来愿景,并设想应用ML的计算机架构和系统将在社区中蓬勃发展。
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Transformer models have shown great success handling long-range interactions, making them a promising tool for modeling video. However they lack inductive biases and scale quadratically with input length. These limitations are further exacerbated when dealing with the high dimensionality introduced with the temporal dimension. While there are surveys analyzing the advances of Transformers for vision, none focus on an in-depth analysis of video-specific designs. In this survey we analyze main contributions and trends of works leveraging Transformers to model video. Specifically, we delve into how videos are handled as input-level first. Then, we study the architectural changes made to deal with video more efficiently, reduce redundancy, re-introduce useful inductive biases, and capture long-term temporal dynamics. In addition we provide an overview of different training regimes and explore effective self-supervised learning strategies for video. Finally, we conduct a performance comparison on the most common benchmark for Video Transformers (i.e., action classification), finding them to outperform 3D ConvNets even with less computational complexity.
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