从世界上任何地方拍摄的单个地面RGB图像预测地理位置(地理位置)是一个非常具有挑战性的问题。挑战包括由于不同的环境场景而导致的图像多样性,相同位置的出现急剧变化,具体取决于一天中的时间,天气,季节和更重要的是,该预测是由单个图像可能只有一个可能只有一个图像做出的很少有地理线索。由于这些原因,大多数现有作品仅限于特定的城市,图像或全球地标。在这项工作中,我们专注于为行星尺度单位图地理定位开发有效的解决方案。为此,我们提出了转运器,这是一个统一的双分支变压器网络,在整个图像上关注细节,并在极端的外观变化下产生健壮的特征表示。转运器将RGB图像及其语义分割图作为输入,在每个变压器层之后的两个平行分支之间进行交互,并以多任务方式同时执行地理位置定位和场景识别。我们在四个基准数据集上评估转运器-IM2GPS,IM2GPS3K,YFCC4K,YFCC26K,并获得5.5%,14.1%,4.9%,9.9%的大陆级别准确度比最新的级别的精度提高。在现实世界测试图像上还验证了转运器,发现比以前的方法更有效。
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地理定位的概念是指确定地球上的某些“实体”的位置的过程,通常使用全球定位系统(GPS)坐标。感兴趣的实体可以是图像,图像序列,视频,卫星图像,甚至图像中可见的物体。由于GPS标记媒体的大规模数据集由于智能手机和互联网而迅速变得可用,而深入学习已经上升以提高机器学习模型的性能能力,因此由于其显着影响而出现了视觉和对象地理定位的领域广泛的应用,如增强现实,机器人,自驾驶车辆,道路维护和3D重建。本文提供了对涉及图像的地理定位的全面调查,其涉及从捕获图像(图像地理定位)或图像内的地理定位对象(对象地理定位)的地理定位的综合调查。我们将提供深入的研究,包括流行算法的摘要,对所提出的数据集的描述以及性能结果的分析来说明每个字段的当前状态。
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The recently developed vision transformer (ViT) has achieved promising results on image classification compared to convolutional neural networks. Inspired by this, in this paper, we study how to learn multi-scale feature representations in transformer models for image classification. To this end, we propose a dual-branch transformer to combine image patches (i.e., tokens in a transformer) of different sizes to produce stronger image features. Our approach processes small-patch and large-patch tokens with two separate branches of different computational complexity and these tokens are then fused purely by attention multiple times to complement each other. Furthermore, to reduce computation, we develop a simple yet effective token fusion module based on cross attention, which uses a single token for each branch as a query to exchange information with other branches. Our proposed cross-attention only requires linear time for both computational and memory complexity instead of quadratic time otherwise. Extensive experiments demonstrate that our approach performs better than or on par with several concurrent works on vision transformer, in addition to efficient CNN models. For example, on the ImageNet1K dataset, with some architectural changes, our approach outperforms the recent DeiT by a large margin of 2% with a small to moderate increase in FLOPs and model parameters. Our source codes and models are available at https://github.com/IBM/CrossViT.
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哥内克人Sentinel Imagery的纯粹卷的可用性为使用深度学习的大尺度创造了新的土地利用陆地覆盖(Lulc)映射的机会。虽然在这种大型数据集上培训是一个非琐碎的任务。在这项工作中,我们试验Lulc Image分类和基准不同最先进模型的Bigearthnet数据集,包括卷积神经网络,多层感知,视觉变压器,高效导通和宽残余网络(WRN)架构。我们的目标是利用分类准确性,培训时间和推理率。我们提出了一种基于用于网络深度,宽度和输入数据分辨率的WRNS复合缩放的高效导通的框架,以有效地训练和测试不同的模型设置。我们设计一种新颖的缩放WRN架构,增强了有效的通道注意力机制。我们提出的轻量级模型具有较小的培训参数,实现所有19个LULC类的平均F分类准确度达到4.5%,并且验证了我们使用的resnet50最先进的模型速度快两倍作为基线。我们提供超过50种培训的型号,以及我们在多个GPU节点上分布式培训的代码。
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Visual place recognition (VPR) is usually considered as a specific image retrieval problem. Limited by existing training frameworks, most deep learning-based works cannot extract sufficiently stable global features from RGB images and rely on a time-consuming re-ranking step to exploit spatial structural information for better performance. In this paper, we propose StructVPR, a novel training architecture for VPR, to enhance structural knowledge in RGB global features and thus improve feature stability in a constantly changing environment. Specifically, StructVPR uses segmentation images as a more definitive source of structural knowledge input into a CNN network and applies knowledge distillation to avoid online segmentation and inference of seg-branch in testing. Considering that not all samples contain high-quality and helpful knowledge, and some even hurt the performance of distillation, we partition samples and weigh each sample's distillation loss to enhance the expected knowledge precisely. Finally, StructVPR achieves impressive performance on several benchmarks using only global retrieval and even outperforms many two-stage approaches by a large margin. After adding additional re-ranking, ours achieves state-of-the-art performance while maintaining a low computational cost.
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视觉地位识别是自主驾驶导航和移动机器人定位等应用的具有挑战性的任务。分散注意力在复杂的场景中呈现的元素经常导致视觉场所的感知偏差。为了解决这个问题,必须将信息与任务相关区域中的信息集成到图像表示中至关重要。在本文中,我们介绍了一种基于视觉变压器的新型整体地点识别模型,TransVPR。它受益于变形金刚的自我关注操作的理想性能,这可以自然地聚合任务相关的特征。从多个级别的变压器的关注,重点关注不同的感兴趣区域,以产生全球图像表示。另外,由熔融注意掩模过滤的变压器层的输出令牌被认为是密钥贴片描述符,用于执行空间匹配以重新排名通过全局图像特征检索的候选。整个模型允许具有单个目标和图像级监控的端到端培训。 TransVPR在几个现实世界基准上实现最先进的性能,同时保持低计算时间和存储要求。
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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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Image segmentation is often ambiguous at the level of individual image patches and requires contextual information to reach label consensus. In this paper we introduce Segmenter, a transformer model for semantic segmentation. In contrast to convolution-based methods, our approach allows to model global context already at the first layer and throughout the network. We build on the recent Vision Transformer (ViT) and extend it to semantic segmentation. To do so, we rely on the output embeddings corresponding to image patches and obtain class labels from these embeddings with a point-wise linear decoder or a mask transformer decoder. We leverage models pre-trained for image classification and show that we can fine-tune them on moderate sized datasets available for semantic segmentation. The linear decoder allows to obtain excellent results already, but the performance can be further improved by a mask transformer generating class masks. We conduct an extensive ablation study to show the impact of the different parameters, in particular the performance is better for large models and small patch sizes. Segmenter attains excellent results for semantic segmentation. It outperforms the state of the art on both ADE20K and Pascal Context datasets and is competitive on Cityscapes.
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With the rapid advances of image editing techniques in recent years, image manipulation detection has attracted considerable attention since the increasing security risks posed by tampered images. To address these challenges, a novel multi-scale multi-grained deep network (MSMG-Net) is proposed to automatically identify manipulated regions. In our MSMG-Net, a parallel multi-scale feature extraction structure is used to extract multi-scale features. Then the multi-grained feature learning is utilized to perceive object-level semantics relation of multi-scale features by introducing the shunted self-attention. To fuse multi-scale multi-grained features, global and local feature fusion block are designed for manipulated region segmentation by a bottom-up approach and multi-level feature aggregation block is designed for edge artifacts detection by a top-down approach. Thus, MSMG-Net can effectively perceive the object-level semantics and encode the edge artifact. Experimental results on five benchmark datasets justify the superior performance of the proposed method, outperforming state-of-the-art manipulation detection and localization methods. Extensive ablation experiments and feature visualization demonstrate the multi-scale multi-grained learning can present effective visual representations of manipulated regions. In addition, MSMG-Net shows better robustness when various post-processing methods further manipulate images.
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弱监督的语义分割(WSSS)是具有挑战性的,特别是当使用图像级标签来监督像素级预测时。为了弥合它们的差距,通常生成一个类激活图(CAM)以提供像素级伪标签。卷积神经网络中的凸轮患有部分激活,即,仅激活最多的识别区域。另一方面,基于变压器的方法在探索具有长范围依赖性建模的全球背景下,非常有效,可能会减轻“部分激活”问题。在本文中,我们提出了基于第一变压器的WSSS方法,并介绍了梯度加权元素明智的变压器注意图(GetAn)。 GetaN显示所有特征映射元素的精确激活,跨越变压器层显示对象的不同部分。此外,我们提出了一种激活感知标签完成模块来生成高质量的伪标签。最后,我们将我们的方法纳入了使用双向向上传播的WSS的结束框架。 Pascal VOC和Coco的广泛实验表明,我们的结果通过显着的保证金击败了最先进的端到端方法,并且优于大多数多级方法.M大多数多级方法。
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细粒度的图像识别是具有挑战性的,因为鉴别性线索通常是碎片化的,无论是来自单个图像还是多个图像。尽管有重要的改进,但大多数现有方法仍然专注于从单个图像中的最辨别部分,忽略其他地区的信息细节,缺乏从其他相关图像的线索考虑。在本文中,我们从新的角度分析了微粒图像识别的困难,并提出了一种具有峰值抑制模块和知识引导模块的变压器架构,其尊重单个图像中辨别特征的多样化和鉴别线索的聚合在多个图像中。具体地,峰值抑制模块首先利用线性投影来将输入图像转换为顺序令牌。然后,它基于变压器编码器产生的注意响应来阻止令牌。该模块因特征学习过程中的最辨别部分而受到惩罚,因此,提高了忽视区域的信息利用。知识引导模块将从峰值抑制模块生成的基于图像的表示与被学习的知识嵌入集进行比较,以获得知识响应系数。之后,使用响应系数作为分类分数,将知识学习形式形式化为分类问题。在训练期间更新知识嵌入和基于图像的表示,以便知识嵌入包括不同图像的鉴别线索。最后,我们将所获得的知识嵌入纳入基于形象的表示,作为全面的表示,导致性能显着提高。对六个流行数据集的广泛评估证明了所提出的方法的优势。
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近年来,人群计数研究取得了重大进展。然而,随着人群中存在具有挑战性的规模变化和复杂的场景,传统的卷积网络和最近具有固定大小的变压器架构都不能良好地处理任务。为了解决这个问题,本文提出了一个场景 - 自适应关注网络,称为Saanet。首先,我们设计了可变形的变压器骨干内的可变形关注,从而了解具有可变形采样位置和动态注意力的自适应特征表示。然后,我们提出了多级特征融合和计数专注特征增强模块,以加强全局图像上下文下的特征表示。学习的陈述可以参加前景,并适应不同的人群。我们对四个具有挑战性的人群计数基准进行广泛的实验,表明我们的方法实现了最先进的性能。特别是,我们的方法目前在NWPU-Crowd基准的公共排行榜上排名第一。我们希望我们的方法可能是一个强大的基线,以支持人群计数的未来研究。源代码将被释放到社区。
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大型预训练的变压器是现代语义分割基准的顶部,但具有高计算成本和冗长的培训。为了提高这种约束,我们从综合知识蒸馏的角度来研究有效的语义分割,并考虑弥合多源知识提取和特定于变压器特定的斑块嵌入之间的差距。我们提出了基于变压器的知识蒸馏(TransKD)框架,该框架通过蒸馏出大型教师变压器的特征地图和补丁嵌入来学习紧凑的学生变形金刚,绕过长期的预训练过程并将FLOPS降低> 85.0%。具体而言,我们提出了两个基本和两个优化模块:(1)交叉选择性融合(CSF)可以通过通道注意和层次变压器内的特征图蒸馏之间的知识转移; (2)嵌入对齐(PEA)在斑块过程中执行尺寸转换,以促进贴片嵌入蒸馏; (3)全局本地上下文混合器(GL-MIXER)提取了代表性嵌入的全局和局部信息; (4)嵌入助手(EA)是一种嵌入方法,可以无缝地桥接老师和学生模型,并具有老师的渠道数量。关于CityScapes,ACDC和NYUV2数据集的实验表明,TransKD的表现优于最先进的蒸馏框架,并竞争了耗时的预训练方法。代码可在https://github.com/ruipingl/transkd上找到。
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Camouflaged objects are seamlessly blended in with their surroundings, which brings a challenging detection task in computer vision. Optimizing a convolutional neural network (CNN) for camouflaged object detection (COD) tends to activate local discriminative regions while ignoring complete object extent, causing the partial activation issue which inevitably leads to missing or redundant regions of objects. In this paper, we argue that partial activation is caused by the intrinsic characteristics of CNN, where the convolution operations produce local receptive fields and experience difficulty to capture long-range feature dependency among image regions. In order to obtain feature maps that could activate full object extent, keeping the segmental results from being overwhelmed by noisy features, a novel framework termed Cross-Model Detail Querying network (DQnet) is proposed. It reasons the relations between long-range-aware representations and multi-scale local details to make the enhanced representation fully highlight the object regions and eliminate noise on non-object regions. Specifically, a vanilla ViT pretrained with self-supervised learning (SSL) is employed to model long-range dependencies among image regions. A ResNet is employed to enable learning fine-grained spatial local details in multiple scales. Then, to effectively retrieve object-related details, a Relation-Based Querying (RBQ) module is proposed to explore window-based interactions between the global representations and the multi-scale local details. Extensive experiments are conducted on the widely used COD datasets and show that our DQnet outperforms the current state-of-the-arts.
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编码器 - 解码器模型已广泛用于RGBD语义分割,并且大多数通过双流网络设计。通常,共同推理RGBD的颜色和几何信息是有益的对语义分割。然而,大多数现有方法都无法全面地利用编码器和解码器中的多模式信息。在本文中,我们提出了一种用于RGBD语义细分的新型关注的双重监督解码器。在编码器中,我们设计一个简单但有效的关注的多模式融合模块,以提取和保险丝深度多级成对的互补信息。要了解更强大的深度表示和丰富的多模态信息,我们介绍了一个双分支解码器,以有效利用不同任务的相关性和互补线。在Nyudv2和Sun-RGBD数据集上的广泛实验表明,我们的方法达到了最先进的方法的卓越性能。
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旨在识别来自子类别的对象的细粒度视觉分类(FGVC)是一个非常具有挑战性的任务,因为固有的微妙级别差异。大多数现有工程主要通过重用骨干网络来提取检测到的歧视区域的特征来解决这个问题。然而,该策略不可避免地使管道复杂化并推动所提出的区域,其中大多数物体的大多数部分未能定位真正重要的部分。最近,视觉变压器(VIT)在传统的分类任务中表现出其强大的表现。变压器的自我关注机制将每个补丁令牌链接到分类令牌。在这项工作中,我们首先评估vit框架在细粒度识别环境中的有效性。然后,由于注意力的强度,可以直观地被认为是令牌重要性的指标,我们进一步提出了一种新颖的部分选择模块,可以应用于我们整合变压器的所有原始注意力的变压器架构进入注意地图,用于指导网络以有效,准确地选择鉴别的图像斑块并计算它们的关系。应用对比损失来扩大混淆类的特征表示之间的距离。我们将基于增强的变压器的模型Transfg命名,并通过在我们实现最先进的绩效的五个流行的细粒度基准测试中进行实验来展示它的价值。提出了更好地理解模型的定性结果。
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这项工作介绍了用于户外机器人技术的视觉跨视图定位。给定一个地面颜色图像和包含本地周围环境的卫星贴片,任务是确定地面摄像头在卫星贴片中的位置。相关工作解决了用于射程传感器(LIDAR,RADAR)的此任务,但对于视觉,仅作为初始跨视图图像检索步骤之后的次要回归步骤。由于还可以通过任何粗糙的本地化(例如,从GPS/GNSS,时间过滤)检索局部卫星贴片,因此我们删除图像检索目标并仅关注度量定位。我们设计了一种具有密集的卫星描述符的新型网络体系结构,在瓶颈处与相似性匹配(而不是图像检索中的输出)以及一个密集的空间分布作为输出,以捕获多模式的定位歧义。我们将使用全局图像描述符的最新回归基线进行比较。关于最近提出的活力和牛津机器人数据集的定量和定性实验结果验证了我们的设计。产生的概率与定位精度相关,甚至可以在未知的方向时大致估计地面摄像头的标题。总体而言,与最先进的面积相比,我们的方法将中值度量定位误差降低了51%,37%和28%,而在同一区域,整个区域和整个时间之间分别概括。
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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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Action recognition models have achieved impressive results by incorporating scene-level annotations, such as objects, their relations, 3D structure, and more. However, obtaining annotations of scene structure for videos requires a significant amount of effort to gather and annotate, making these methods expensive to train. In contrast, synthetic datasets generated by graphics engines provide powerful alternatives for generating scene-level annotations across multiple tasks. In this work, we propose an approach to leverage synthetic scene data for improving video understanding. We present a multi-task prompt learning approach for video transformers, where a shared video transformer backbone is enhanced by a small set of specialized parameters for each task. Specifically, we add a set of ``task prompts'', each corresponding to a different task, and let each prompt predict task-related annotations. This design allows the model to capture information shared among synthetic scene tasks as well as information shared between synthetic scene tasks and a real video downstream task throughout the entire network. We refer to this approach as ``Promptonomy'', since the prompts model a task-related structure. We propose the PromptonomyViT model (PViT), a video transformer that incorporates various types of scene-level information from synthetic data using the ``Promptonomy'' approach. PViT shows strong performance improvements on multiple video understanding tasks and datasets.
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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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