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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

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Coarse-to-Fine Domain Adaptive Semantic Segmentation with Photometric Alignment and Category-Center Regularization

Coarse-to-Fine Domain Adaptive Semantic Segmentation with Ph...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Ma, Haoyu Lin, Xiangru Wu, Zifeng Yu, Yizhou Univ Hong Kong Hong Kong Peoples R China Deepwise AI Lab Beijing Peoples R China

ISBN: (纸本)9781665445092

Unsupervised domain adaptation (UDA) in semantic segmentation is a fundamental yet promising task relieving the need for laborious annotation works. However, the domain shifts/discrepancies problem in this task compromise the final segmentation performance. Based on our observation, the main causes of the domain shifts are differences in imaging conditions, called image-level domain shifts, and differences in object category configurations called category-level domain shifts. In this paper, we propose a novel UDA pipeline that unifies image-level alignment and category-level feature distribution regularization in a coarse-to-fine manner. Specifically, on the coarse side, we propose a photometric alignment module that aligns an image in the source domain with a reference image from the target domain using a set of image-level operators;on the fine side, we propose a category-oriented triplet loss that imposes a soft constraint to regularize category centers in the source domain and a self-supervised consistency regularization method in the target domain. Experimental results show that our proposed pipeline improves the generalization capability of the final segmentation model and significantly outperforms all previous state-of-the-arts.

关键词： Image segmentation Adaptation models computer vision Annotations Computational modeling Semantics Pipelines

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High Quality Segmentation for Ultra High-resolution Images

High Quality Segmentation for Ultra High-resolution Images

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Shen, Tiancheng Zhang, Yuechen Qi, Lu Kuen, Jason Xie, Xingyu Wu, Jianlong Lin, Zhe Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China Adobe Res Beijing Peoples R China Peking Univ Beijing Peoples R China Shandong Univ Jinan Peoples R China SmartMore Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

To segment 4K or 6K ultra high-resolution images needs extra computation consideration in image segmentation. Common strategies, such as down-sampling, patch cropping, and cascade model, cannot address well the balance issue between accuracy and computation cost. Motivated by the fact that humans distinguish among objects continuously from coarse to precise levels, we propose the Continuous Refinement Model (CRM) for the ultra high-resolution segmentation refinement task. CRM continuously aligns the feature map with the refinement target and aggregates features to reconstruct these image details. Besides, our CRM shows its significant generalization ability to fill the resolution gap between low-resolution training images and ultra high-resolution testing ones. We present quantitative performance evaluation and visualization to show that our proposed method is fast and effective on image segmentation refinement. Code is available at https://***/dvlab-research/Entity/tree/main/CRM.

关键词： Training Image segmentation computer vision Visualization Computational modeling Aggregates Customer relationship management

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SoftGroup for 3D Instance Segmentation on Point Clouds

SoftGroup for 3D Instance Segmentation on Point Clouds

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Thang Vu Kim, Kookhoi Luu, Tung M. Thanh Nguyen Yoo, Chang D. Korea Adv Inst Sci & Technol KAIST Daejeon South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Existing state-of-the-art 3D instance segmentation methods perform semantic segmentation followed by grouping. The hard predictions are made when performing semantic segmentation such that each point is associated with a single class. However, the errors stemming from hard decision propagate into grouping that results in (1) low overlaps between the predicted instance with the ground truth and (2) substantial false positives. To address the aforementioned problems, this paper proposes a 3D instance segmentation method referred to as SoftGroup by performing bottom-up soft grouping followed by top-down refinement. SoftGroup allows each point to be associated with multiple classes to mitigate the problems stemming from semantic prediction errors and suppresses false positive instances by learning to categorize them as background. Experimental results on different datasets and multiple evaluation metrics demonstrate the efficacy of SoftGroup. Its performance surpasses the strongest prior method by a significant margin of +6.2% on the ScanNet v2 hidden test set and +6.8% on S3DIS Area 5 in terms of AP(50) . Soft-Group is also fast, running at 345ms per scan with a single Titan X on ScanNet v2 dataset. The source code and trained models for both datasets are available at https://***/thangvubk/***.

关键词： Point cloud compression Measurement computer vision Three-dimensional displays Codes Semantics pattern recognition

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Weakly Supervised Dense Video Captioning 30

Weakly Supervised Dense Video Captioning

引用

30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Shen, Zhiqiang Li, Jianguo Su, Zhou Li, Minjun Chen, Yurong Jiang, Yu-Gang Xue, Xiangyang Fudan Univ Sch Comp Sci Shanghai Key Lab Intelligent Informat Proc Shanghai Peoples R China Intel Labs China Beijing Peoples R China

ISBN: (纸本)9781538604571

This paper focuses on a novel and challenging vision task, dense video captioning, which aims to automatically describe a video clip with multiple informative and diverse caption sentences. The proposed method is trained without explicit annotation of fine-grained sentence to video regionsequence correspondence, but is only based on weak videolevel sentence annotations. It differs from existing video captioning systems in three technical aspects. First, we propose lexical fully convolutional neural networks (LexicalFCN) with weakly supervised multi-instance multi-label learning to weakly link video regions with lexical labels. Second, we introduce a novel submodular maximization scheme to generate multiple informative and diverse regionsequences based on the Lexical-FCN outputs. A winnertakes- all scheme is adopted to weakly associate sentences to region-sequences in the training phase. Third, a sequenceto- sequence learning based language model is trained with the weakly supervised information obtained through the association process. We show that the proposed method can not only produce informative and diverse dense captions, but also outperform state-of-the-art single video captioning methods by a large margin.

关键词： Visualization Training Motion segmentation Vocabulary computer vision Neural networks captions Motion segmentation images computer vision Neural network Visualization Vocabulary Functional training Videotapes video clips Training modelling languages

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Semantic Image Matting

Semantic Image Matting

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Sun, Yanan Tang, Chi-Keung Tai, Yu-Wing HKUST Hong Kong Peoples R China Kuaishou Technol Beijing Peoples R China

ISBN: (纸本)9781665445092

Natural image matting separates the foreground from background in fractional occupancy which can be caused by highly transparent objects, complex foreground (e.g., net or tree), and/or objects containing very fine details (e.g., hairs). Although conventional matting formulation can be applied to all of the above cases, no previous work has attempted to reason the underlying causes of matting due to various foreground semantics. We show how to obtain better alpha mattes by incorporating into our framework semantic classification of matting regions. Specifically, we consider and learn 20 classes of matting patterns, and propose to extend the conventional trimap to semantic trimap. The proposed semantic trimap can be obtained automatically through patch structure analysis within trimap regions. Meanwhile, we learn a multi-class discriminator to regularize the alpha prediction at semantic level, and content-sensitive weights to balance different regularization losses. Experiments on multiple benchmarks show that our method outperforms other methods and has achieved the most competitive state-of-theart performance. Finally, we contribute a large-scale Semantic Image Matting Dataset with careful consideration of data balancing across different semantic classes. Code and dataset are available at https://***/nowsyn/ SIM.

关键词： Hair computer vision Codes Semantics Benchmark testing Data mining Optimization

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RoutedFusion: Learning Real-time Depth Map Fusion

RoutedFusion: Learning Real-time Depth Map Fusion

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Weder, Silvan Schonberger, Johannes Pollefeys, Marc Oswald, Martin R. Swiss Fed Inst Technol Zurich Switzerland Microsoft Albuquerque NM USA

ISBN: (纸本)9781728171685

The efficient fusion of depth maps is a key part of most state-of-the-art 3D reconstruction methods. Besides requiring high accuracy, these depth fusion methods need to be scalable and real-time capable. To this end, we present a novel real-time capable machine learning-based method for depth map fusion. Similar to the seminal depth map fusion approach by Curless and Levoy, we only update a local group of voxels to ensure real-time capability. Instead of a simple linear fusion of depth information, we propose a neural network that predicts non-linear updates to better account for typical fusion errors. Our network is composed of a 2D depth routing network and a 3D depth fusion network which efficiently handle sensor-specific noise and outliers. This is especially useful for surface edges and thin objects for which the original approach suffers from thickening artifacts. Our method outperforms the traditional fusion approach and related learned approaches on both synthetic and real data. We demonstrate the performance of our method in reconstructing fine geometric details from noise and outlier contaminated data on various scenes.

关键词： computer vision

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Relative Pose from a Calibrated and an Uncalibrated Smartphone Image

Relative Pose from a Calibrated and an Uncalibrated Smartpho...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Ding, Yaqing Barath, Daniel Yang, Jian Kukelova, Zuzana Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China Swiss Fed Inst Technol Dept Comp Sci Comp Vis & Geometry Grp Zurich Switzerland Czech Tech Univ Fac Elect Engn Visual Recognit Grp Prague Czech Republic Lund Univ Ctr Math Sci Lund Sweden

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we propose a new minimal and a non-minimal solver for estimating the relative camera pose together with the unknown focal length of the second camera. This configuration has a number of practical benefits, e.g., when processing large-scale datasets. Moreover, it is resistant to the typical degenerate cases of the traditional six-point algorithm. The minimal solver requires four point correspondences and exploits the gravity direction that the built-in IMU of recent smart devices recover. We also propose a linear solver that enables estimating the pose from a larger-than-minimal sample extremely efficiently which then can be improved by, e.g., bundle adjustment. The methods are tested on 35654 image pairs from publicly available real-world and new datasets. When combined with a recent robust estimator, they lead to results superior to the traditional solvers in terms of rotation, translation and focal length accuracy, while being notably faster.

关键词： Resistance Bundle adjustment computer vision Codes Tracking Cameras Sensors

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Self-supervised Spatial Reasoning on Multi-View Line Drawings

Self-supervised Spatial Reasoning on Multi-View Line Drawing...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Xiang, Siyuan Yang, Anbang Xue, Yanfei Yang, Yaoqing Feng, Chen NYU Tandon Sch Engn New York NY 10003 USA Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Spatial reasoning on multi-view line drawings by state-of-the-art supervised deep networks is recently shown with puzzling low performances on the SPARE3D dataset [14]. Based on the fact that self-supervised learning is helpful when a large number of data are available, we propose two self-supervised learning approaches to improve the baseline performance for view consistency reasoning and camera pose reasoning tasks on the SPARE3D dataset. For the first task, we use a self-supervised binary classification network to contrast the line drawing differences between various views of any two similar 3D objects, enabling the trained networks to effectively learn detail-sensitive yet view-invariant line drawing representations of 3D objects. For the second type of task, we propose a self-supervised multi-class classification framework to train a model to select the correct corresponding view from which a line drawing is rendered. Our method is even helpful for the downstream tasks with unseen camera poses. Experiments show that our method could significantly increase the baseline performance in SPARE3D, while some popular self-supervised learning methods cannot.

关键词： Location awareness computer vision Three-dimensional displays Navigation Self-supervised learning Cameras Cognition

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HEAT: Holistic Edge Attention Transformer for Structured Reconstruction

HEAT: Holistic Edge Attention Transformer for Structured Rec...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Chen, Jiacheng Qian, Yiming Furukawa, Yasutaka Simon Fraser Univ Burnaby BC Canada Univ Manitoba Winnipeg MB Canada

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This paper presents a novel attention-based neural network for structured reconstruction, which takes a 2D raster image as an input and reconstructs a planar graph depicting an underlying geometric structure. The approach detects corners and classifies edge candidates between corners in an end-to-end manner. Our contribution is a holistic edge classification architecture, which 1) initializes the feature of an edge candidate by a trigonometric positional encoding of its end-points;2) fides image feature to each edge candidate by deformable attention;3) employs two weight-sharing Transformer decoders to learn holistic structural patterns over the graph edge candidates;and 4) is trained with a masked learning strategy. The corner detector is a variant of the edge classification architecture, adapted to operate on pixels as corner candidates. We conduct experiments on two structured reconstruction tasks: outdoor building architecture and indoor floorplan planar graph reconstruction. Extensive qualitative and quantitative evaluations demonstrate the superiority of our approach over the state of the art. Code and pre-trained models are available at https://heat-structured-reconstruction. github io/

关键词： Heating systems Codes Three-dimensional displays Image edge detection Architecture computer architecture Transformer cores

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Boosting Adversarial Transferability by Block Shuffle and Rotation

Boosting Adversarial Transferability by Block Shuffle and Ro...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wang, Kunyu He, Xuanran Wang, Wenxuan Wang, Xiaosen Chinese Univ Hong Kong Hong Kong Peoples R China Nanyang Technol Univ Singapore Singapore Huawei Singular Secur Lab Beijing Peoples R China

ISBN: (纸本)9798350353006

Adversarial examples mislead deep neural networks with imperceptible perturbations and have brought significant threats to deep learning. An important aspect is their transferability, which refers to their ability to deceive other models, thus enabling attacks in the black-box setting. Though various methods have been proposed to boost transferability, the performance still falls short compared with white-box attacks. In this work, we observe that existing input transformation based attacks, one of the mainstream transfer-based attacks, result in different attention heatmaps on various models, which might limit the transferability. We also find that breaking the intrinsic relation of the image can disrupt the attention heatmap of the original image. Based on this finding, we propose a novel input transformation based attack called block shuffle and rotation (BSR). Specifically, BSR splits the input image into several blocks, then randomly shuffles and rotates these blocks to construct a set of new images for gradient calculation. Empirical evaluations on the ImageNet dataset demonstrate that BSR could achieve significantly better transferability than the existing input transformation based methods under single-model and ensemble-model settings. Combining BSR with the current input transformation method can further improve the transferability, which significantly outperforms the state-of-the-art methods. Code is available at https://***/Trustworthy-AI-Group/BSR.

关键词： adversarial attack computer vision

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