Visual recognition requires rich representations that span levels from low to high, scales from small to large, and resolutions from fine to coarse. Even with the depth of features in a convolutional network, a layer in isolation is not enough: compounding and aggregating these representations improves inference of what and where. Architectural efforts are exploring many dimensions for network backbones, designing deeper or wider architectures, but how to best aggregate layers and blocks across a network deserves further attention. Although skip connections have been incorporated to combine layers, these connections have been "shallow" themselves, and only fuse by simple, one-step operations. We augment standard architectures with deeper aggregation to better fuse information across layers. Our deep layer aggregation structures iteratively and hierarchically merge the feature hierarchy to make networks with better accuracy and fewer parameters. Experiments across architectures and tasks show that deep layer aggregation improves recognition and resolution compared to existing branching and merging schemes.

跨不同层的特征的聚合信息是密集预测模型的基本操作。尽管表现力有限，但功能级联占主导地位聚合运营的选择。在本文中，我们引入了细分特征聚合（AFA），以融合不同的网络层，具有更具表现力的非线性操作。 AFA利用空间和渠道注意，以计算层激活的加权平均值。灵感来自神经体积渲染，我们将AFA扩展到规模空间渲染（SSR），以执行多尺度预测的后期融合。 AFA适用于各种现有网络设计。我们的实验表明了对挑战性的语义细分基准，包括城市景观，BDD100K和Mapillary Vistas的一致而显着的改进，可忽略不计的计算和参数开销。特别是，AFA改善了深层聚集（DLA）模型在城市景观上的近6％Miou的性能。我们的实验分析表明，AFA学会逐步改进分割地图并改善边界细节，导致新的最先进结果对BSDS500和NYUDV2上的边界检测基准。在http://vis.xyz/pub/dla-afa上提供代码和视频资源。

Australian Centre for Robotic Vision {guosheng.lin;anton.milan;chunhua.shen;

语义分割是将类标签分配给图像中每个像素的问题，并且是自动车辆视觉堆栈的重要组成部分，可促进场景的理解和对象检测。但是，许多表现最高的语义分割模型非常复杂且笨拙，因此不适合在计算资源有限且低延迟操作的板载自动驾驶汽车平台上部署。在这项调查中，我们彻底研究了旨在通过更紧凑，更有效的模型来解决这种未对准的作品，该模型能够在低内存嵌入式系统上部署，同时满足实时推理的限制。我们讨论了该领域中最杰出的作品，根据其主要贡献将它们置于分类法中，最后我们评估了在一致的硬件和软件设置下，所讨论模型的推理速度，这些模型代表了具有高端的典型研究环境GPU和使用低内存嵌入式GPU硬件的现实部署方案。我们的实验结果表明，许多作品能够在资源受限的硬件上实时性能，同时说明延迟和准确性之间的一致权衡。

现代的高性能语义分割方法采用沉重的主链和扩张的卷积来提取相关特征。尽管使用上下文和语义信息提取功能对于分割任务至关重要，但它为实时应用程序带来了内存足迹和高计算成本。本文提出了一种新模型，以实现实时道路场景语义细分的准确性/速度之间的权衡。具体来说，我们提出了一个名为“比例吸引的条带引导特征金字塔网络”（s \ textsuperscript {2} -fpn）的轻巧模型。我们的网络由三个主要模块组成：注意金字塔融合（APF）模块，比例吸引条带注意模块（SSAM）和全局特征Upsample（GFU）模块。 APF采用了注意力机制来学习判别性多尺度特征，并有助于缩小不同级别之间的语义差距。 APF使用量表感知的关注来用垂直剥离操作编码全局上下文，并建模长期依赖性，这有助于将像素与类似的语义标签相关联。此外，APF还采用频道重新加权块（CRB）来强调频道功能。最后，S \ TextSuperScript {2} -fpn的解码器然后采用GFU，该GFU用于融合APF和编码器的功能。已经对两个具有挑战性的语义分割基准进行了广泛的实验，这表明我们的方法通过不同的模型设置实现了更好的准确性/速度权衡。提出的模型已在CityScapes Dataset上实现了76.2 \％miou/87.3fps，77.4 \％miou/67fps和77.8 \％miou/30.5fps，以及69.6 \％miou，71.0 miou，71.0 \％miou，和74.2 \％\％\％\％\％\％。 miou在Camvid数据集上。这项工作的代码将在\ url {https://github.com/mohamedac29/s2-fpn提供。

We introduce dense vision transformers, an architecture that leverages vision transformers in place of convolutional networks as a backbone for dense prediction tasks. We assemble tokens from various stages of the vision transformer into image-like representations at various resolutions and progressively combine them into full-resolution predictions using a convolutional decoder. The transformer backbone processes representations at a constant and relatively high resolution and has a global receptive field at every stage. These properties allow the dense vision transformer to provide finer-grained and more globally coherent predictions when compared to fully-convolutional networks. Our experiments show that this architecture yields substantial improvements on dense prediction tasks, especially when a large amount of training data is available. For monocular depth estimation, we observe an improvement of up to 28% in relative performance when compared to a state-of-theart fully-convolutional network. When applied to semantic segmentation, dense vision transformers set a new state of the art on ADE20K with 49.02% mIoU. We further show that the architecture can be fine-tuned on smaller datasets such as NYUv2, KITTI, and Pascal Context where it also sets the new state of the art. Our models are available at https://github.com/intel-isl/DPT.

Representing features at multiple scales is of great importance for numerous vision tasks. Recent advances in backbone convolutional neural networks (CNNs) continually demonstrate stronger multi-scale representation ability, leading to consistent performance gains on a wide range of applications. However, most existing methods represent the multi-scale features in a layerwise manner. In this paper, we propose a novel building block for CNNs, namely Res2Net, by constructing hierarchical residual-like connections within one single residual block. The Res2Net represents multi-scale features at a granular level and increases the range of receptive fields for each network layer. The proposed Res2Net block can be plugged into the state-of-the-art backbone CNN models, e.g., ResNet, ResNeXt, and DLA. We evaluate the Res2Net block on all these models and demonstrate consistent performance gains over baseline models on widely-used datasets, e.g., CIFAR-100 and ImageNet. Further ablation studies and experimental results on representative computer vision tasks, i.e., object detection, class activation mapping, and salient object detection, further verify the superiority of the Res2Net over the state-of-the-art baseline methods. The source code and trained models are available on https://mmcheng.net/res2net/.

语义分割是自主车辆了解周围场景的关键技术。当代模型的吸引力表现通常以牺牲重计算和冗长的推理时间为代价，这对于自行车来说是无法忍受的。在低分辨率图像上使用轻量级架构（编码器 - 解码器或双路）或推理，最近的方法实现了非常快的场景解析，即使在单个1080TI GPU上以100多件FPS运行。然而，这些实时方法与基于扩张骨架的模型之间的性能仍有显着差距。为了解决这个问题，我们提出了一家专门为实时语义细分设计的高效底座。所提出的深层双分辨率网络（DDRNET）由两个深部分支组成，之间进行多个双边融合。此外，我们设计了一个名为Deep聚合金字塔池（DAPPM）的新上下文信息提取器，以基于低分辨率特征映射放大有效的接收字段和熔丝多尺度上下文。我们的方法在城市景观和Camvid数据集上的准确性和速度之间实现了新的最先进的权衡。特别是，在单一的2080Ti GPU上，DDRNET-23-Slim在Camvid测试组上的Citycapes试验组102 FPS上的102 FPS，74.7％Miou。通过广泛使用的测试增强，我们的方法优于最先进的模型，需要计算得多。 CODES和培训的型号在线提供。

在本文中，我们专注于探索有效的方法，以更快，准确和域的不可知性语义分割。受到相邻视频帧之间运动对齐的光流的启发，我们提出了一个流对齐模块（FAM），以了解相邻级别的特征映射之间的\ textit {语义流}，并将高级特征广播到高分辨率特征有效地，有效地有效。 。此外，将我们的FAM与共同特征的金字塔结构集成在一起，甚至在轻量重量骨干网络（例如Resnet-18和DFNET）上也表现出优于其他实时方法的性能。然后，为了进一步加快推理过程，我们还提出了一个新型的封闭式双流对齐模块，以直接对齐高分辨率特征图和低分辨率特征图，在该图中我们将改进版本网络称为SFNET-LITE。广泛的实验是在几个具有挑战性的数据集上进行的，结果显示了SFNET和SFNET-LITE的有效性。特别是，建议的SFNET-LITE系列在使用RESNET-18主链和78.8 MIOU以120 fps运行的情况下，使用RTX-3090上的STDC主链在120 fps运行时，在60 fps运行时达到80.1 miou。此外，我们将四个具有挑战性的驾驶数据集（即CityScapes，Mapillary，IDD和BDD）统一到一个大数据集中，我们将其命名为Unified Drive细分（UDS）数据集。它包含不同的域和样式信息。我们基准了UDS上的几项代表性作品。 SFNET和SFNET-LITE仍然可以在UDS上取得最佳的速度和准确性权衡，这在如此新的挑战性环境中是强大的基准。所有代码和模型均可在https://github.com/lxtgh/sfsegnets上公开获得。

由于长距离依赖性建模的能力，变压器在各种自然语言处理和计算机视觉任务中表现出令人印象深刻的性能。最近的进展证明，将这种变压器与基于CNN的语义图像分割模型相结合非常有前途。然而，目前还没有很好地研究了纯变压器的方法如何实现图像分割。在这项工作中，我们探索了语义图像分割的新框架，它是基于编码器 - 解码器的完全变压器网络（FTN）。具体地，我们首先提出金字塔组变压器（PGT）作为逐步学习分层特征的编码器，同时降低标准视觉变压器（VIT）的计算复杂性。然后，我们将特征金字塔变换器（FPT）提出了来自PGT编码器的多电平进行语义图像分割的多级别的语义级别和空间级信息。令人惊讶的是，这种简单的基线可以在多个具有挑战性的语义细分和面部解析基准上实现更好的结果，包括帕斯卡背景，ADE20K，Cocostuff和Celebamask-HQ。源代码将在https://github.com/br -dl/paddlevit上发布。

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.

现有的基于变压器的图像骨干通常会在一个方向上传播特征信息，从较低到更高级别。这可能不是理想的选择，因为定位能力划定准确的物体边界，在较低的高分辨率特征图中最突出，而可以删除属于一个对象的图像信号的语义与另一个对象相对于另一个对象，通常是在较高级别中出现的处理。我们提出了分层间注意力（HILA），这是一种基于注意力的方法，可在不同级别的功能之间捕获自下而上的更新和自上而下的更新。 Hila通过将较高和较低级别的特征之间的局部连接添加到骨干编码器中，扩展了层次视觉变压器体系结构。在每次迭代中，我们通过具有更高级别的功能来竞争作业来更新属于它们的低级功能，从而构建层次结构，从而迭代解决对象零件关系。然后使用这些改进的低级功能来更新更高级别的功能。 HILA可以集成到大多数层次结构中，而无需对基本模型进行任何更改。我们将HILA添加到Segformer和Swin Transformer中，并以更少的参数和拖鞋的方式显示出明显的语义分割精度。项目网站和代码：https：//www.cs.toronto.edu/~garyleung/hila/

我们展示了一个下一代神经网络架构，马赛克，用于移动设备上的高效和准确的语义图像分割。MOSAIC是通过各种移动硬件平台使用常用的神经操作设计，以灵活地部署各种移动平台。利用简单的非对称编码器 - 解码器结构，该解码器结构由有效的多尺度上下文编码器和轻量级混合解码器组成，以从聚合信息中恢复空间细节，Mosaic在平衡准确度和计算成本的同时实现了新的最先进的性能。基于搜索的分类网络，马赛克部署在定制的特征提取骨架顶部，达到目前行业标准MLPerf型号和最先进的架构，达到5％的绝对精度增益。

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.

Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixelsto-pixels, exceed the state-of-the-art in semantic segmentation. Our key insight is to build "fully convolutional" networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning. We define and detail the space of fully convolutional networks, explain their application to spatially dense prediction tasks, and draw connections to prior models. We adapt contemporary classification networks (AlexNet [20], the VGG net [31], and GoogLeNet [32]) into fully convolutional networks and transfer their learned representations by fine-tuning [3] to the segmentation task. We then define a skip architecture that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer to produce accurate and detailed segmentations. Our fully convolutional network achieves stateof-the-art segmentation of PASCAL VOC (20% relative improvement to 62.2% mean IU on 2012), NYUDv2, and SIFT Flow, while inference takes less than one fifth of a second for a typical image.

视觉表示学习是解决各种视力问题的关键。依靠开创性的网格结构先验，卷积神经网络（CNN）已成为大多数深视觉模型的事实上的标准架构。例如，经典的语义分割方法通常采用带有编码器编码器体系结构的完全横向卷积网络（FCN）。编码器逐渐减少了空间分辨率，并通过更大的接受场来学习更多抽象的视觉概念。由于上下文建模对于分割至关重要，因此最新的努力一直集中在通过扩张（即极度）卷积或插入注意力模块来增加接受场。但是，基于FCN的体系结构保持不变。在本文中，我们旨在通过将视觉表示学习作为序列到序列预测任务来提供替代观点。具体而言，我们部署纯变压器以将图像编码为一系列贴片，而无需局部卷积和分辨率减少。通过在变压器的每一层中建立的全球环境，可以学习更强大的视觉表示形式，以更好地解决视力任务。特别是，我们的细分模型（称为分割变压器（SETR））在ADE20K上擅长（50.28％MIOU，这是提交当天测试排行榜中的第一个位置），Pascal环境（55.83％MIOU），并在CityScapes上达到竞争成果。此外，我们制定了一个分层局部全球（HLG）变压器的家族，其特征是窗户内的本地关注和跨窗户的全球性专注于层次结构和金字塔架构。广泛的实验表明，我们的方法在各种视觉识别任务（例如，图像分类，对象检测和实例分割和语义分割）上实现了吸引力的性能。

Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections-one between each layer and its subsequent layer-our network has L(L+1) 2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less computation to achieve high performance. Code and pre-trained models are available at https://github.com/liuzhuang13/DenseNet.

人们普遍认为，对于准确的语义细分，必须使用昂贵的操作（例如，非常卷积）结合使用昂贵的操作（例如非常卷积），从而导致缓慢的速度和大量的内存使用。在本文中，我们质疑这种信念，并证明既不需要高度的内部决议也不是必需的卷积。我们的直觉是，尽管分割是一个每像素的密集预测任务，但每个像素的语义通常都取决于附近的邻居和遥远的环境。因此，更强大的多尺度功能融合网络起着至关重要的作用。在此直觉之后，我们重新访问常规的多尺度特征空间（通常限制为P5），并将其扩展到更丰富的空间，最小的P9，其中最小的功能仅为输入大小的1/512，因此具有很大的功能接受场。为了处理如此丰富的功能空间，我们利用最近的BIFPN融合了多尺度功能。基于这些见解，我们开发了一个简化的分割模型，称为ESEG，该模型既没有内部分辨率高，也没有昂贵的严重卷积。也许令人惊讶的是，与多个数据集相比，我们的简单方法可以以比以前的艺术更快地实现更高的准确性。在实时设置中，ESEG-Lite-S在189 fps的CityScapes [12]上达到76.0％MIOU，表现优于更快的[9]（73.1％MIOU时为170 fps）。我们的ESEG-LITE-L以79 fps的速度运行，达到80.1％MIOU，在很大程度上缩小了实时和高性能分割模型之间的差距。

语义分割是计算机视觉中的关键任务之一，它是为图像中的每个像素分配类别标签。尽管最近取得了重大进展，但大多数现有方法仍然遇到两个具有挑战性的问题：1）图像中的物体和东西的大小可能非常多样化，要求将多规模特征纳入完全卷积网络（FCN）； 2）由于卷积网络的固有弱点，很难分类靠近物体/物体的边界的像素。为了解决第一个问题，我们提出了一个新的多受感受性现场模块（MRFM），明确考虑了多尺度功能。对于第二期，我们设计了一个边缘感知损失，可有效区分对象/物体的边界。通过这两种设计，我们的多种接收场网络在两个广泛使用的语义分割基准数据集上实现了新的最先进的结果。具体来说，我们在CityScapes数据集上实现了83.0的平均值，在Pascal VOC2012数据集中达到了88.4的平均值。

In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First, we highlight convolution with upsampled filters, or 'atrous convolution', as a powerful tool in dense prediction tasks. Atrous convolution allows us to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural Networks. It also allows us to effectively enlarge the field of view of filters to incorporate larger context without increasing the number of parameters or the amount of computation. Second, we propose atrous spatial pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales. Third, we improve the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models. The commonly deployed combination of max-pooling and downsampling in DCNNs achieves invariance but has a toll on localization accuracy. We overcome this by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF), which is shown both qualitatively and quantitatively to improve localization performance. Our proposed "DeepLab" system sets the new state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching 79.7% mIOU in the test set, and advances the results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and Cityscapes. All of our code is made publicly available online.