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

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SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data

SetVAE: Learning Hierarchical Composition for Generative Mod...

引用

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

作者： Kim, Jinwoo Yoo, Jaehoon Lee, Juho Hong, Seunghoon Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9781665445092

Generative modeling of set-structured data, such as point clouds, requires reasoning over local and global structures at various scales. However, adopting multi-scale frameworks for ordinary sequential data to a set-structured data is nontrivial as it should be invariant to the permutation of its elements. In this paper, we propose SetVAE, a hierarchical variational autoencoder for sets. Motivated by recent progress in set encoding, we build SetVAE upon attentive modules that first partition the set and project the partition back to the original cardinality. Exploiting this module, our hierarchical VAE learns latent variables at multiple scales, capturing coarse-to-fine dependency of the set elements while achieving permutation invariance. We evaluate our model on point cloud generation task and achieve competitive performance to the prior arts with substantially smaller model capacity. We qualitatively demonstrate that our model generalizes to unseen set sizes and learns interesting subset relations without supervision.

关键词： computer vision Art Data models Cognition Encoding pattern recognition Task analysis

来源：评论

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Learning to Segment Rigid Motions from Two Frames

Learning to Segment Rigid Motions from Two Frames

引用

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

作者： Yang, Gengshan Ramanan, Deva Carnegie Mellon Univ Pittsburgh PA 15213 USA Argo AI Pittsburgh PA USA

ISBN: (纸本)9781665445092

Appearance-based detectors achieve remarkable performance on common scenes, benefiting from high-capacity models and massive annotated data, but tend to fail for scenarios that lack training data. Geometric motion segmentation algorithms, however, generalize to novel scenes, but have yet to achieve comparable performance to appearance-based ones, due to noisy motion estimations and degenerate motion configurations. To combine the best of both worlds, we propose a modular network, whose architecture is motivated by a geometric analysis of what independent object motions can be recovered from an egomotion field. It takes two consecutive frames as input and predicts segmentation masks for the background and multiple rigidly moving objects, which are then parameterized by 3D rigid transformations. Our method achieves state-of-the-art performance for rigid motion segmentation on KITTI and Sintel. The inferred rigid motions lead to a significant improvement for depth and scene flow estimation.

关键词： computer vision Three-dimensional displays Motion segmentation Motion estimation Estimation Training data computer architecture

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Differentiable Diffusion for Dense Depth Estimation from Multi-view Images

Differentiable Diffusion for Dense Depth Estimation from Mul...

引用

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

作者： Khan, Numair Kim, Min H. Tompkin, James Brown Univ Providence RI 02912 USA Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9781665445092

We present a method to estimate dense depth by optimizing a sparse set of points such that their diffusion into a depth map minimizes a multi-view repmjection error from RGB supervision. We optimize point positions, depths, and weights with respect to the loss by differential splatting that models points as Gaussians with analytic transmittance. Further;we develop an efficient optimization routine that can simultaneously optimize the 50k+ points required for complex scene reconstruction. We validate our routine using ground truth data and show high reconstruction quality. Then, we apply this to light field and wider baseline images via self supervision, and show improvements in both average and outlier error for depth maps diffused from inaccurate sparse points. Finally, we compare qualitative and quantitative results to image processing and deep learning methods.

关键词： Deep learning computer vision Analytical models Estimation Rendering (computer graphics) Propagation losses Light fields

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From Activation to Initialization: Scaling Insights for Optimizing Neural Fields

From Activation to Initialization: Scaling Insights for Opti...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Hemanth Saratchandran Sameera Ramasinghe Simon Lucey Australian Institute of Machine Learning University of Adelaide Australia Amazon Australia

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In the realm of computer vision, Neural Fields have gained prominence as a contemporary tool harnessing neural networks for signal representation. Despite the remarkable progress in adapting these networks to solve a variety of problems, the field still lacks a comprehensive theoretical framework. This article aims to address this gap by delving into the intricate interplay between initialization and activation, providing a foundational basis for the robust optimization of Neural Fields. Our theoretical insights reveal a deep-seated connection among network initialization, architectural choices, and the optimization process, emphasizing the need for a holistic approach when designing cutting-edge Neural Fields.

关键词： computer vision Scalability Neural networks pattern recognition Signal representation Optimization Convergence

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MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers

MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Tran...

引用

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

作者： Wang, Huiyu Zhu, Yukun Adam, Hartwig Yuille, Alan Chen, Liang-Chieh Johns Hopkins Univ Baltimore MD 21218 USA Google Res Mountain View CA USA Google Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

We present MaX-DeepLab, the first end-to-end model for panoptic segmentation. Our approach simplifies the current pipeline that depends heavily on surrogate sub-tasks and hand-designed components, such as box detection, non-maximum suppression, thing-stuff merging, etc. Although these sub-tasks are tackled by area experts, they fail to comprehensively solve the target task. By contrast, our MaX-DeepLab directly predicts class-labeled masks with a mask transformer, and is trained with a panoptic quality inspired loss via bipartite matching. Our mask transformer employs a dual-path architecture that introduces a global memory path in addition to a CNN path, allowing direct communication with any CNN layers. As a result, MaX-DeepLab shows a significant 7.1% PQ gain in the box-free regime on the challenging COCO dataset, closing the gap between box-based and box-free methods for the first time. A small variant of MaX-DeepLab improves 3.0% PQ over DETR with similar parameters and M-Adds. Furthermore, MaX-DeepLab, without test time augmentation, achieves new state-of-the-art 51.3% PQ on COCO test-dev set.

关键词： computer vision Merging Pipelines computer architecture Transformers pattern recognition Task analysis

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Source-Free Domain Adaptation for Semantic Segmentation

Source-Free Domain Adaptation for Semantic Segmentation

引用

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

作者： Liu, Yuang Zhang, Wei Wang, Jun East China Normal Univ Shanghai Peoples R China

ISBN: (纸本)9781665445092

Unsupervised Domain Adaptation (UDA) can tackle the challenge that convolutional neural network (CNN)-based approaches for semantic segmentation heavily rely on the pixel-level annotated data, which is labor-intensive. However, existing UDA approaches in this regard inevitably require the full access to source datasets to reduce the gap between the source and target domains during model adaptation, which are impractical in the real scenarios where the source datasets are private, and thus cannot be released along with the well-trained source models. To cope with this issue, we propose a source-free domain adaptation framework for semantic segmentation, namely SFDA, in which only a well-trained source model and an unlabeled target domain dataset are available for adaptation. SFDA not only enables to recover and preserve the source domain knowledge from the source model via knowledge transfer during model adaptation, but also distills valuable information from the target domain for self-supervised learning. The pixel- and patch-level optimization objectives tailored for semantic segmentation are seamlessly integrated in the framework. The extensive experimental results on numerous benchmark datasets highlight the effectiveness of our framework against the existing UDA approaches relying on source data.

关键词： Adaptation models Image segmentation computer vision Semantics Benchmark testing pattern recognition Convolutional neural networks

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Learning Position and Target Consistency for Memory-based Video Object Segmentation

Learning Position and Target Consistency for Memory-based Vi...

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

作者： Hu, Li Zhang, Peng Zhang, Bang Pan, Pan Xu, Yinghui Jin, Rong Alibaba Grp Machine Intelligence Technol Lab Hangzhou Peoples R China

ISBN: (纸本)9781665445092

This paper studies the problem of semi-supervised video object segmentation(VOS). Multiple works have shown that memory-based approaches can be effective for video object segmentation. They are mostly based on pixel-level matching, both spatially and temporally. The main shortcoming of memory-based approaches is that they do not take into account the sequential order among frames and do not exploit object-level knowledge from the target. To address this limitation, we propose to Learn position and target Consistency framework for Memory-based video object segmentation, termed as LCM. It applies the memory mechanism to retrieve pixels globally, and meanwhile learns position consistency for more reliable segmentation. The learned location response promotes a better discrimination between target and distractors. Besides, LCM introduces an object-level relationship from the target to maintain target consistency, making LCM more robust to error drifting. Experiments show that our LCM achieves state-of-the-art performance on both DAVIS and Youtube-VOS benchmark. And we rank the 1st in the DAVIS 2020 challenge semi-supervised VOS task.

关键词： computer vision Object segmentation Benchmark testing pattern recognition Reliability Task analysis

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Animating Pictures with Eulerian Motion Fields

Animating Pictures with Eulerian Motion Fields

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

作者： Holynski, Aleksander Curless, Brian Seitz, Steven M. Szeliski, Richard Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9781665445092

In this paper, we demonstrate a fully automatic method for converting a still image into a realistic animated looping video. We target scenes with continuous fluid motion, such as flowing water and billowing smoke. Our method relies on the observation that this type of natural motion can be convincingly reproduced from a static Eulerian motion description, i.e. a single, temporally constant flow field that defines the immediate motion of a particle at a given 2D location. We use an image-to-image translation network to encode motion priors of natural scenes collected from online videos, so that for a new photo, we can synthesize a corresponding motion field. The image is then animated using the generated motion through a deep warping technique: pixels are encoded as deep features, those features are warped via Eulerian motion, and the resulting warped feature maps are decoded as images. In order to produce continuous, seamlessly looping video textures, we propose a novel video looping technique that flows features both forward and backward in time and then blends the results. We demonstrate the effectiveness and robustness of our method by applying it to a large collection of examples including beaches, waterfalls, and flowing rivers.

关键词： computer vision Fluids Animation Robustness Rivers pattern recognition

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PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks

PU-GCN: Point Cloud Upsampling using Graph Convolutional Net...

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

作者： Qian, Guocheng Abualshour, Abdulellah Li, Guohao Thabet, Ali Ghanem, Bernard King Abdullah Univ Sci & Technol KAUST Abu Dhabi U Arab Emirates

ISBN: (纸本)9781665445092

The effectiveness of learning-based point cloud upsampling pipelines heavily relies on the upsampling modules and feature extractors used therein. For the point upsampling module, we propose a novel model called NodeShuffle, which uses a Graph Convolutional Network (GCN) to better encode local point information from point neighborhoods. NodeShuffle is versatile and can be incorporated into any point cloud upsampling pipeline. Extensive experiments show how NodeShuffle consistently improves state-of-theart upsampling methods. For feature extraction, we also propose a new multi-scale point feature extractor, called Inception DenseGCN. By aggregating features at multiple scales, this feature extractor enables further performance gain in the final upsampled point clouds. We combine Inception DenseGCN with NodeShuffle into a new point upsampling pipeline called PU-GCN. PU-GCN sets new state-of-art performance with much fewer parameters and more efficient inference. Our code is publicly available at https://***/guochengqian/PU-GCN.

关键词： Convolutional codes computer vision Pipelines Performance gain Feature extraction pattern recognition

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Video Object Segmentation Using Global and Instance Embedding Learning

Video Object Segmentation Using Global and Instance Embeddin...

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

作者： Ge, Wenbin Lu, Xiankai Shen, Jianbing Beijing Inst Technol Beijing Peoples R China Shandong Univ Sch Software Jinan Peoples R China Incept Inst Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9781665445092

In this paper, we propose a feature embedding based video object segmentation (VOS) method which is simple, fast and effective. The current VOS task involves two main challenges: object instance differentiation and cross-frame instance alignment. Most state-of-the-art matching based VOS methods simplify this task into a binary segmentation task and tackle each instance independently. In contrast, we decompose the VOS task into two subtasks: global embedding learning that segments foreground objects of each frame in a pixel-to-pixel manner, and instance feature embedding learning that separates instances. The outputs of these two subtasks are fused to obtain the final instance masks quickly and accurately. Through using the relation among different instances per-frame as well as temporal relation across different frames, the proposed network learns to differentiate multiple instances and associate them properly in one feed-forward manner. Extensive experimental results on the challenging DAVIS[34] and YoutubeVOS [57] datasets show that our method achieves better performances than most counterparts in each case.

关键词： computer vision Annotations Object segmentation pattern recognition Task analysis

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