Consequently, image denoising algorithms are mostly developed and evaluated on synthetic data that is usually generated with a widespread Structures matter in single image super resolution (SISR). CVPR 2020 Open Access Repository. Jun 20, 2022 · The ability to learn richer network representations generally boosts the performance of deep learning models. In this paper, after briefly reviewing recent advances of visible and infrared image fusion, we Jun 9, 2021 · Gait recognition, applied to identify individual walking patterns in a long-distance, is one of the most promising video-based biometric technologies. It neglects the structure of an object, results in poor object delineation and small spurious regions in the segmentation result. At present, most gait recognition methods take the whole human body as a unit to establish the spatio-temporal representations. However, the global representation often suffers from the information loss of structure details on local regions of incomplete point cloud. Published in: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Jun 19, 2020 · The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. Jun 19, 2020 · Recent works have widely explored the contextual dependencies to achieve more accurate segmentation results. In this paper, we propose an interpretable neural network for computational spectral imaging. Seattle, WA, USA. Within the remote sensing domain, a diverse set of acquisition modalities exist, each with their own unique strengths and weaknesses. We combine this with a novel tracklet-based dynamic programming algorithm, which takes advantage of re-detections of both the first-frame template and previous-frame predictions, to model the full history of both the object to be tracked Action recognition in still images is closely related to various other computer vision tasks such as pose estimation, object recognition, image retrieval, video action recognition and frame tagging in videos. However, we have observed that different parts of human body possess evidently various visual appearances and Pan Zhang, Bo Zhang, Dong Chen, Lu Yuan, Fang Wen; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. We also introduce back-projection, a simple and effective semi-supervised training method that leverages unlabeled video data. Qilin Sun, Ethan Tseng, Qiang Fu, Wolfgang Heidrich, Felix Heide; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. Mon Jun 17th through Fri Jun 21st, 2024. First, they have low visibility (i. The videos in PANDA were captured by a gigapixel camera and cover real-world scenes with both wide field-of-view (~1 square kilometer area) and high-resolution details (~gigapixel-level/frame). Despite this, most methods treat the image as a whole, which makes the results they produce for content-rich scenes less realistic. Unlike some recent methods that directly regress the coordinates of the object boundary points from an image, deep snake uses a neural network to iteratively deform an initial contour to match the object boundary, which implements the classic idea of snake algorithms with a learning-based Scene flow estimation has been receiving increasing attention for 3D environment perception. However, most approaches rarely distinguish different types of contextual dependencies, which may pollute the scene understanding. Recently, fully convolutional instance segmentation methods have drawn much attention as they are often simpler and more efficient than two-stage approaches like Mask R-CNN. Deep Unfolding Network for Image Super-Resolution. However, CAMs can hardly serve as the object mask due to the gap between full and weak supervisions. To date, almost all such approaches fall behind the two-stage Mask R-CNN method in mask precision when models have similar computation complexity, leaving great Face anti-spoofing is critical to the security of face recognition systems. In this work we propose a generic The availability of large-scale datasets has helped unleash the true potential of deep convolutional neural networks (CNNs). The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024. Jun 19, 2020 · As smartphones become people's primary cameras to take photos, the quality of their cameras and the associated computational photography modules has become a de facto standard in evaluating and ranking smartphones in the consumer market. Although the problem can be alleviated by the heteroscedastic Gaussian noise model, the noise sources caused by digital camera electronics are still largely overlooked, despite their significant effect on raw measurement, especially under CVPR 2020. The scenes may contain 4k head counts with over 100× scale Bolun Zheng, Shanxin Yuan, Gregory Slabaugh, Ales Leonardis; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. More recently, the idea of mining-based strategies is adopted to emphasize the misclassified samples, achieving promising results. This active Read all the papers in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) | IEEE Conference | IEEE Xplore CVPR 2020. This transformation explicitly models channel relationships with explainable control variables. In this paper, we propose a self-supervised equivariant attention mechanism (SEAM) to discover additional We present a new method for efficient high-quality image segmentation of objects and scenes. First, we introduce a novel data-driven prior that can adaptively exploit both the local and non-local correlations among the spectral image. Unlike previous work, we densely connect each point with every other in a local neighborhood, aiming to specify feature of each point based on the local region characteristics for better representing the region. Note that we had a large number of workshops with significant overlap in scope. We conduct so far the most comprehensive study of perceptual quality assessment of smartphone photography. In this paper, we present a simple neural network module for leveraging the knowledge from multiple modalities in Read all the papers in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) | IEEE Conference | IEEE Xplore The current state-of-the-art method for high-resolution image synthesis is StyleGAN [21], which has been shown to work reliably on a variety of datasets. Mitigating Bias in Face Recognition Using Skewness-Aware Reinforcement Learning Abstract: Racial equality is an important theme of international human rights law, but it has been largely obscured when the overall face recognition accuracy is pursued blindly. Based on a set of In late fusion, each modality is processed in a separate unimodal Convolutional Neural Network (CNN) stream and the scores of each modality are fused at the end. In this work, we directly supervise the feature aggregation to distinguish the intra-class and interclass context clearly. Most existing object detection algorithms attend to certain object areas once and then predict the object locations. Unlike action recognition in videos - a relatively very well established area of research where Visible and infrared image fusion is an important area in image processing due to its numerous applications. However, for the single-image denoising problem, capturing a real dataset is an unacceptably expensive and cumbersome procedure. Jun 19, 2020 · This paper introduces a novel contour-based approach named deep snake for real-time instance segmentation. Our key technical This paper presents PointWeb, a new approach to extract contextual features from local neighborhood in a point cloud. However, most existing methods train a dehazing model on synthetic hazy images, which are less able to generalize well to real hazy images due to domain shift. In particular, large-scale control of agricultural parcels is an issue of major political and economic importance. 01/19 – CVPR 2021 Workshops have been announced here. ISBN: 978-1-7281-9360-1. Iterative Closest Point (ICP) solves the rigid point cloud registration problem iteratively in two steps: (1) make hard assignments of spatially closest point correspondences, and then (2) find the least-squares rigid transformation. In this paradigm, all upsampling layers and the refinement stage, which are indispensable in all existing point-based methods, are abandoned. , Swin Transformers We present Siam R-CNN, a Siamese re-detection architecture which unleashes the full power of two-stage object detection approaches for visual object tracking. In this work, we propose PointPainting: a sequential fusion Yash Bhalgat, Jinwon Lee, Markus Nagel, Tijmen Blankevoort, Nojun Kwak; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. We propose a novel monocular scene flow method that yields competitive accuracy and real-time performance. The redundancy in feature maps is an important characteristic of those successful CNNs, but has rarely been investigated in neural architecture design. Our Visual tempo characterizes the dynamics and the temporal scale of an action. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e. This paper presents an architecture, algorithms, and a prototype implementation addressing this vision. 12/4 – Due to the large number of proposals, the announcement of accepted proposals will be delayed until Dec 11 th, 2020. These variables determine the neuron behaviors of competition or cooperation, and they are jointly optimized with the convolutional weight towards more accurate Point cloud completion aims to infer the complete geometries for missing regions of 3D objects from incomplete ones. In We present a fast feature-metric point cloud registration framework, which enforces the optimisation of registration by minimising a feature-metric projection e CVPR 2020. Jun 14, 2020 · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) June 14 2020 to June 19 2020. Apart from that, existing popular Multi-scale approaches are runtime intensive and memory inefficient. 3481-3489. By observing the cartoon painting behavior and consulting artists, we propose to separately identify three white-box representations from images: the surface representation that contains smooth surface of cartoon images, the structure representation that refers to the sparse color-blocks and flatten global content in the celluloid style CVPR 2020 Open Access Repository. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore. at the Seattle Convention Center. We present Momentum Contrast (MoCo) for unsupervised visual representation learning. In particular, it aligns the training image pairs into similar lighting condition with predictive CVPR 2020. This enables building a large and consistent dictionary on-the-fly that facilitates contrastive unsupervised learning. Camera and lidar are important sensor modalities for robotics in general and self-driving cars in particular. Specifically, we develop a The research community has increasing interest in autonomous driving research, despite the resource intensity of obtaining representative real world data. 9243-9252 Abstract Despite the recent advance of Generative Adversarial Networks (GANs) in high-fidelity image synthesis, there lacks enough understanding of how GANs are able to map a latent code Deploying convolutional neural networks (CNNs) on embedded devices is difficult due to the limited memory and computation resources. This paper proposes a novel Ghost module to generate more feature maps from cheap operations. In this work, we propose a generally applicable transformation unit for visual recognition with deep convolutional neural networks. In this context, we proposed a fast Deep Multi-patch Hierarchical Network to restore Non-homogeneous hazed images by aggregating features CVPR 2020. This problem is focused on recognizing a person's action or behavior using a single frame. Read all the papers in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) | IEEE Conference | IEEE Xplore We study on image super-resolution (SR), which aims to recover realistic textures from a low-resolution (LR) image. Due to its simplicity, late fusion is still the predominant approach in many state-of-the-art multimodal applications. These CVPR 2020 papers are the Open Access versions, provided by the. In this work, we propose a unified Context-Aware Attention Satellite image time series, bolstered by their growing availability, are at the forefront of an extensive effort towards automated Earth monitoring by international institutions. To address this issue, we propose a domain adaptation paradigm, which consists of an image translation module and two image dehazing As an emerging topic in face recognition, designing margin-based loss functions can increase the feature margin between different classes for enhanced discriminability. g. Monocular scene flow estimation - obtaining 3D structure and 3D motion from two temporally consecutive images - is a highly ill-posed problem, and practical solutions are lacking to date. From this vantage, we present the PointRend (Point-based Rendering) neural CVPR 2020. This material is presented to ensure timely dissemination of scholarly and technical work. Our data-driven prior is Jun 12, 2023 · 2024 Conference. This paper proposes a novel graph attention convolution (GAC), whose kernels can be dynamically carved into specific shapes to adapt to the The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. To achieve this, we first design a Spatial pooling has been proven highly effective to capture long-range contextual information for pixel-wise prediction tasks, such as scene parsing. e. Second, noise becomes significant and disrupts the image content, due to low signal-to-noise ratio. Recent studies benefiting from generative adversarial network (GAN) have promoted the development of SISR by recovering photo-realistic images. From a perspective on contrastive learning as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder. Inspired by feature selection methods in machine learning, a simple stepwise network expansion approach is employed that expands a single axis in each step, such that good accuracy to complexity trade This paper presents an approach for image cartoonization. Table of Contents. It remains challenging because prior works seldom explore semantic correspondences between modalities and semantic correlations in a single modality at the same time. The hard assignments of closest point correspondences based on spatial distances are sensitive to the initial rigid transformation and noisy/outlier points, which Image-to-image translation has made great strides in recent years, with current techniques being able to handle unpaired training images and to account for the multi-modality of the translation problem. To @InProceedings{Jiang_2020_CVPR, author = {Jiang, Chiyu "Max" and Sud, Avneesh and Makadia, Ameesh and Huang, Jingwei and Niessner, Matthias and Funkhouser, Thomas}, title = {Local Implicit Grid Representations for 3D Scenes}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. We expose and analyze several of its characteristic artifacts, and propose changes in both model architecture and training methods to address them. These CVPR 2020 workshop papers are the Open Access versions, provided by the Computer Vision Foundation. In this paper, we introduce a Detection-based Unsupervised Standard convolution is inherently limited for semantic segmentation of point cloud due to its isotropy about features. Surprisingly, lidar-only methods outperform fusion methods on the main benchmark datasets, suggesting a gap in the literature. Latent Fingerprint Image Enhancement Based on Progressive Generative Adversarial Network pp. It is the hierarchical Transformers (e. However, during the entire training process, the prior methods either do not explicitly @InProceedings{Guo_2020_CVPR, author = {Guo, Qiushan and Wang, Xinjiang and Wu, Yichao and Yu, Zhipeng and Liang, Ding and Hu, Xiaolin and Luo, Ping}, title = {Online Knowledge Distillation via Collaborative Learning}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year This paper presents X3D, a family of efficient video networks that progressively expand a tiny 2D image classification architecture along multiple network axes, in space, time, width and depth. CVPR 2020. Computational spectral imaging has been striving to capture the spectral information of the dynamic world in the last few decades. MoCo provides competitive results under the common These CVPR 2020 workshop papers are the Open Access versions, provided by the Computer Vision Foundation. We CVPR 2020. Most existing low-light image enhancement methods, however, learn from noise-negligible datasets. The distinguishing feature of StyleGAN [21] is its un-conventional generator architecture. Jiabo Huang, Shaogang Gong, Xiatian Zhu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. A novel module, namely Adaptive Feature Adjustment (AFA) module Object detection is an essential step towards holistic scene understanding. , semantic segmentation We propose D3VO as a novel framework for monocular visual odometry that exploits deep networks on three levels -- deep depth, pose and uncertainty estimation. 3636-3645 Abstract Image demoireing is a multi-faceted image restoration task involving both texture and color restoration. Kai Zhang, Luc Van Gool, Radu Timofte; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers. However, there are always undesired structural distortions in the recovered images. Depth supervised learning has been proven as one of the most effective methods for face anti-spoofing. The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. firstback. We will be contacting workshop organizers to consider merging proposals. 8849-8858 Abstract By simultaneously learning visual features and data grouping, deep clustering has shown impressive ability to deal with unsupervised learning for structure analysis of high-dimensional Read all the papers in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) | IEEE Conference | IEEE Xplore Recently, CNN based end-to-end deep learning methods achieve superiority in Image Dehazing but they tend to fail drastically in Non-homogeneous dehazing. , pose and identity when trained on human faces) and stochastic variation in the generated images (e. , freckles, hair), and it enables intuitive, scale . In this paper, we present a lightweight point-based 3D single stage object detector 3DSSD to achieve decent balance of accuracy and efficiency. Learning-based single image super-resolution (SISR) methods are continuously showing superior As a typical cross-modal problem, image-text bi-directional retrieval relies heavily on the joint embedding learning and similarity measure for each image-text pair. We introduce the Smartphone Photography Attribute Yujun Shen, Jinjin Gu, Xiaoou Tang, Bolei Zhou; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. Yet, most of the current literature and open datasets only deal with electro-optical (optical) data for different detection and segmentation tasks at high spatial resolutions. They rely on users having good photographic skills in taking images with low noise Single RGB image hyperspectral reconstruction has seen a boost in performance and research attention with the emergence of CNNs and more availability of RGB/hyperspectral datasets. e CVPR 2020 Open Access Repository. optical data is often the preferred choice for geospatial applications, but requires Low-light images typically suffer from two problems. Read all the papers in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) | IEEE Conference | IEEE Xplore Dec 3, 2019 · Analyzing and Improving the Image Quality of StyleGAN. By Printed and digitally displayed photos have the ability to hide imperceptible digital data that can be accessed through internet-connected imaging systems. Jan 10, 2022 · The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model. We present PANDA, the first gigaPixel-level humAN-centric viDeo dAtaset, for large-scale, long-term, and multi-object visual analysis. Most of advanced solutions exploit class activation map (CAM). Recent progress has been made by taking high-resolution images as references (Ref), so that relevant textures can be transferred to LR images. Many methods have set state-of-the-art performance on restoring images degraded by bad weather such as rain, haze, fog, and snow, however they are designed specifically to handle one type of degradation. We first propose a novel self-supervised monocular depth estimation network trained on stereo videos without any external supervision. The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) is the premier annual computer vision event comprising the main conference and several co-located On face recognition, person re-identification, as well as several fine-grained image retrieval datasets, the achieved performance is on par with the state of the art. In this paper, we propose a method that can handle multiple bad weather degradations: rain, fog, snow and adherent raindrops using a single network. The sensors provide complementary information offering an opportunity for tight sensor-fusion. A vanilla ViT, on the other hand, faces difficulties when applied to general computer vision tasks such as object detection and semantic segmentation. Abstract. This work proposes a CNN-based strategy for learning RGB to hyperspectral cube mapping by learning a set of basis functions and weights in a combined manner and using them both to reconstruct the hyperspectral Image dehazing using learning-based methods has achieved state-of-the-art performance in recent years. Existing self-driving datasets are limited in the scale and variation of the environments they capture, even though generalization within and between operating regions is crucial to the over-all viability of the technology. In this paper, we propose a structure-preserving super resolution method to alleviate the above issue while CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. In this regard, hybrid convolutional-recurrent neural architectures have shown promising results for CVPR 2020. To improve representation-learning in convolutional neural networks, we present a multi-branch architecture, which applies channel-wise attention across different network branches to leverage the complementary strengths of both feature-map attention and multi-path representation. By analogizing classical computer graphics methods for efficient rendering with over- and undersampling challenges faced in pixel labeling tasks, we develop a unique perspective of image segmentation as a rendering problem. Modeling such visual tempos of different actions facilitates their recognition. While much progress has been made in recent years with efforts on developing image fusion algorithms, there is a lack of code library and benchmark which can gauge the state-of-the-art. These CVPR 2020 papers are the Open Access versions, provided by the Computer Vision Foundation. 696-697 Abstract Unlike ReLU, newer activation functions (like Swish, H-swish, Mish) that are frequently employed in popular efficient architectures can also result Instance segmentation is one of the fundamental vision tasks. Previous works often capture the visual tempo through sampling raw videos at multiple rates and constructing an input-level frame pyramid, which usually requires a costly multi-branch network to handle. 1386-1396 Abstract High-dynamic range (HDR) imaging is an essential imaging modality for a wide range of applications in uncontrolled environments, including autonomous driving Image-level weakly supervised semantic segmentation is a challenging problem that has been deeply studied in recent years. 10021-10030 Prevalence of voxel-based 3D single-stage detectors contrast with underexplored point-based methods. 3217-3226. , small pixel values). Previous methods usually predict the complete point cloud based on the global shape representation extracted from the incomplete input. However, existing SR approaches neglect to use attention mechanisms to transfer high-resolution (HR) textures from Ref images, which Read all the papers in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) | IEEE Conference | IEEE Xplore IEEE Catalog Number: ISBN: CFP22003-POD 978-1-6654-6947-0 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022) New Orleans, Louisiana, USA Read all the papers in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) | IEEE Conference | IEEE Xplore In this work, we demonstrate that 3D poses in video can be effectively estimated with a fully convolutional model based on dilated temporal convolutions over 2D keypoints. 5143-5153 Abstract We present a general framework for exemplar-based image translation, which synthesizes a photo-realistic image from the input in a distinct domain (e. Instead, human eyes move around, locating informative parts to understand the object location. In this paper, beyond conventional spatial pooling that usually has a regular shape of NxN, we rethink the formulation of spatial pooling by introducing a new pooling strategy, called strip pooling, which considers a long but narrow kernel, i. Vehicle Re-Identification is to find images of the same vehicle from various views in the cross-camera scenario. Our work focuses on fixing its char-acteristic artifacts and improving the result quality further. Extensive experiments demonstrate that when applying self-calibrated convolutions into different backbones, our networks can significantly improve the baseline models in a variety of vision tasks, including image recognition, object detection, instance segmentation, and keypoint detection, with no need to change the network architectures. Despite the great success, most previous works still formulate the problem as a single-frame multi-task one by simply augmenting the loss with depth, while neglecting the detailed fine-grained information and the Sicheng Gao, Xuhui Liu, Bohan Zeng, Sheng Xu, Yanjing Li, Xiaoyan Luo, Jianzhuang Liu, Xiantong Zhen, Baochang Zhang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. In an effort to CVPR 2020. We start with predicted 2D keypoints for unlabeled video, then estimate 3D poses and finally back Lacking rich and realistic data, learned single image denoising algorithms generalize poorly in real raw images that not resemble the data used for training. Another way to think about this is physical photographs that have unique QR codes invisibly embedded within them. In this paper, we propose a parsing-based view-aware embedding network (PVEN) to achieve the view-aware feature Abstract: Leveraging physical knowledge described by partial differential equations (PDEs) is an appealing way to improve unsupervised video forecasting models. Since physics is too restrictive for describing the full visual content of generic video sequences, we introduce PhyDNet, a two-branch deep architecture, which explicitly disentangles PDE dynamics from unknown complementary information. The main challenges of this task are the large intra-instance distance caused by different views and the subtle inter-instance discrepancy caused by similar vehicles. However, scientists have revealed that human do not look at the scene in fixed steadiness.
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