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检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"

共 22906 条记录，以下是4721-4730 订阅

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Recurrent Scene Parsing with Perspective Understanding in the Loop 31

Recurrent Scene Parsing with Perspective Understanding in th...

引用

31st ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Kong, Shu Fowlkes, Charless Univ Calif Irvine Dept Comp Sci Irvine CA 92697 USA

ISBN: (纸本)9781538664209

Objects may appear at arbitrary scales in perspective images of a scene, posing a challenge for recognition systems that process images at a fixed resolution. We propose a depth-aware gating module that adaptively selects the pooling field size in a convolutional network architecture according to the object scale (inversely proportional to the depth) so that small details are preserved for distant objects while larger receptive fields are used for those nearby. The depth gating signal is provided by stereo disparity or estimated directly from monocular input. We integrate this depth-aware gating into a recurrent convolutional neural network to perform semantic segmentation. Our recurrent module iteratively refines the segmentation results, leveraging the depth and semantic predictions from the previous iterations. Through extensive experiments on four popular large-scale datasets, we demonstrate this approach achieves competitive semantic segmentation performance with a model which is substantially more compact. We carry out extensive analysis of this architecture including variants that operate on monocular RGB but use depth as side-information during training, unsupervised gating as a generic attentional mechanism, and multi-resolution gating. We find that gated pooling for joint semantic segmentation and depth yields state-of-the-art results for quantitative monocular depth estimation.

关键词： Semantics Image segmentation computer architecture Training Task analysis Computational modeling Convolution

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Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective

Can Neural Nets Learn the Same Model Twice? Investigating Re...

引用

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

作者： Somepalli, Gowthami Fowl, Liam Bansal, Arpit Yeh-Chiang, Ping Dar, Yehuda Baraniuk, Richard Goldblum, Micah Goldstein, Tom Univ Maryland College Pk MD 20742 USA Rice Univ Houston TX 77251 USA NYU New York NY 10003 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We discuss methods for visualizing neural network decision boundaries and decision regions. We use these visualizations to investigate issues related to reproducibility and generalization in neural network training. We observe that changes in model architecture (and its associate inductive bias) cause visible changes in decision boundaries, while multiple runs with the same architecture yield results with strong similarities, especially in the case of wide architectures. We also use decision boundary methods to visualize double descent phenomena. We see that decision boundary reproducibility depends strongly on model width. Near the threshold of interpolation, neural network decision boundaries become fragmented into many small decision regions, and these regions are non-reproducible. Meanwhile, very narrows and very wide networks have high levels of reproducibility in their decision boundaries with relatively few decision regions. We discuss how our observations relate to the theory of double descent phenomena in convex models. Code is available at https://***/ somepago/dbViz.

关键词： Training Interpolation computer vision Computational modeling Neural networks Machine learning computer architecture

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Rethinking Visual Geo-localization for Large-Scale Applications

Rethinking Visual Geo-localization for Large-Scale Applicati...

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

作者： Berton, Gabriele Masone, Carlo Caputo, Barbara Politecn Torino Turin Italy CINI Turin Italy

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Visual Geo-localization (VG) is the task of estimating the position where a given photo was taken by comparing it with a large database of images of known locations. To investigate how existing techniques would perform on a real-world city-wide VG application, we build San Francisco eXtra Large, a new dataset covering a whole city and providing a wide range of challenging cases, with a size 30x bigger than the previous largest dataset for visual geo-localization. We find that current methods fail to scale to such large datasets, therefore we design a new highly scalable training technique, called CosPlace, which casts the training as a classification problem avoiding the expensive mining needed by the commonly used contrastive learning. We achieve state-of-the-art performance on a wide range of datasets and find that CosPlace is robust to heavy domain changes. Moreover, we show that, compared to the previous state-of-the-art, CosPlace requires roughly 80% less GPU memory at train time, and it achieves better results with 8x smaller descriptors, paving the way for city-wide real-world visual geo-localization. Dataset, code and trained models are available for research purposes at https://***/gmberton/CosPlace.

关键词： Training Visualization computer vision Codes Urban areas Memory management Graphics processing units

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Learning to Predict Activity Progress by Self-Supervised Video Alignment

Learning to Predict Activity Progress by Self-Supervised Vid...

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

作者： Donahue, Gerard Elhamifar, Ehsan Northwestern Univ Boston MA 02115 USA

ISBN: (纸本)9798350353006

In this paper, we tackle the problem of self-supervised video alignment and activity progress prediction using in-the-wild videos. Our proposed self-supervised representation learning method carefully addresses different action orderings, redundant actions, and background frames to generate improved video representations compared to previous methods. Our model generalizes temporal cycleconsistency learning to allow for more flexibility in determining cycle-consistent neighbors. More specifically, to handle repeated actions, we propose a multi-neighbor cycle consistency and a multi-cycle-back regression loss by finding multiple soft nearest neighbors using a Gaussian Mixture Model. To handle background and redundant frames, we introduce a context-dependent drop function in our framework, discouraging the alignment of droppable frames. On the other hand, to learn from videos of multiple activities jointly, we propose a multi-head crosstask network, allowing us to embed a video and estimate progress without knowing its activity label. Experiments on multiple datasets show that our method outperforms the state-of-the-art for video alignment and progress prediction. (1)

关键词： computer vision in the wild procedural learning progress progress prediction representation learning self-supervised self-supervised representation learning unconstrained unconstrained videos Video Alignment video understanding

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Semantic Component Decomposition for Face Attribute Manipulation 32

Semantic Component Decomposition for Face Attribute Manipula...

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32nd ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Chen, Ying-Cong Shen, Xiaohui Lin, Zhe Lu, Xin Pao, I-Ming Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China Tencent Youtu Lab Shanghai Peoples R China Adobe Res San Jose CA USA ByteDance AI Lab Beijing Peoples R China

ISBN: (纸本)9781728132938

Deep neural network-based methods were proposed for face attribute manipulation. There still exist, however, two major issues, i.e., insufficient visual quality (or resolution) of the results and lack of user control. They limit the applicability of existing methods since users may have different editing preference on facial attributes. In this paper, we address these issues by proposing a semantic component model. The model decomposes a facial attribute into multiple semantic components, each corresponds to a specific face region. This not only allows for user control of edit strength on different parts based on their preference, but also makes it effective to remove unwanted edit effect. Further, each semantic component is composed of two fundamental elements, which determine the edit effect and region respectively. This property provides fine interactive control. As shown in experiments, our model not only produces high quality results, but also allows effective user interaction.

关键词： And Body Pose Face Gesture Image and Video Synthesis vision Applications and Systems

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GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields

GIRAFFE: Representing Scenes as Compositional Generative Neu...

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

作者： Niemeyer, Michael Geiger, Andreas Max Planck Inst Intelligent Syst Tubingen Germany Univ Tubingen Tubingen Germany

ISBN: (纸本)9781665445092

Deep generative models allow for photorealistic image synthesis at high resolutions. But for many applications, this is not enough: content creation also needs to be controllable. While several recent works investigate how to disentangle underlying factors of variation in the data, most of them operate in 2D and hence ignore that our world is three-dimensional. Further, only few works consider the compositional nature of scenes. Our key hypothesis is that incorporating a compositional 3D scene representation into the generative model leads to more controllable image synthesis. Representing scenes as compositional generative neural feature fields allows us to disentangle one or multiple objects from the background as well as individual objects' shapes and appearances while learning from unstructured and unposed image collections without any additional supervision. Combining this scene representation with a neural rendering pipeline yields a fast and realistic image synthesis model. As evidenced by our experiments, our model is able to disentangle individual objects and allows for translating and rotating them in the scene as well as changing the camera pose.

关键词： Solid modeling computer vision Three-dimensional displays Image resolution Image synthesis Shape Pipelines

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MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution

MASA-SR: Matching Acceleration and Spatial Adaptation for Re...

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

作者： Lu, Liying Li, Wenbo Tao, Xin Lu, Jiangbo Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China Kuaishou Beijing Peoples R China SmartMore Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Reference-based image super-resolution (RefSR) has shown promising success in recovering high frequency details by utilizing an external reference image (Ref). In this task, texture details are transferred from the Ref image to the low-resolution (LR) image according to their point- or patch-wise correspondence. Therefore, high-quality correspondence matching is critical. It is also desired to be computationally efficient. Besides, existing RefSR methods tend to ignore the potential large disparity in distributions between the LR and Ref images, which hurts the effectiveness of the information utilization. In this paper, we propose the MASA network for RefSR, where two novel modules are designed to address these problems. The proposed Match & Extraction Module significantly reduces the computational cost by a coarse-to-fine correspondence matching scheme. The Spatial Adaptation Module learns the difference of distribution between the LR and Ref images, and remaps the distribution of Ref features to that of LR features in a spatially adaptive way. This scheme makes the network robust to handle different reference images. Extensive quantitative and qualitative experiments validate the effectiveness of our proposed model.

关键词： computer vision Adaptation models Computational modeling Superresolution Feature extraction Robustness Computational efficiency

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A Conservative Approach for Unbiased Learning on Unknown Biases

A Conservative Approach for Unbiased Learning on Unknown Bia...

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

作者： Jeon, Myeongho Kim, Daekyung Lee, Woochul Kang, Myungjoo Lee, Joonseok Seoul Natl Univ Seoul South Korea Monitor Corp Seoul South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Although convolutional neural networks (CNNs) achieve state-of-the-art in image classification, recent works address their unreliable predictions due to their excessive dependence on biased training data. Existing unbiased modeling postulates that the bias in the dataset is obvious to know, but it is actually unsuited for image datasets including countless sensory attributes. To mitigate this issue, we present a new scenario that does not necessitate a predefined bias. Under the observation that CNNs do have multi-variant and unbiased representations in the model, we propose a conservative framework that employs this internal information for unbiased learning. Specifically, this mechanism is implemented via hierarchical features captured along the multiple layers and orthogonal regularization. Extensive evaluations on public benchmarks demonstrate our method is effective for unbiased learning.

关键词： Representation learning Deep learning Machine vision Training data Distributed databases Data models Robustness

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GIF2Video: Color Dequantization and Temporal Interpolation of GIF images 32

GIF2Video: Color Dequantization and Temporal Interpolation o...

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32nd ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wang, Yang Huang, Haibin Wang, Chuan He, Tong Wang, Jue Minh Hoai SUNY Stony Brook Stony Brook NY USA Megvii Res USA Hangzhou Peoples R China UCLA Los Angeles CA USA

ISBN: (纸本)9781728132938

Graphics Interchange Format (GIF) is a highly portable graphics format that is ubiquitous on the Internet. Despite their small sizes, GIF images often contain undesirable visual artifacts such as flat color regions, false contours, color shift, and dotted patterns. In this paper, we propose GIF2Video, the first learning-based method for enhancing the visual quality of GIFs in the wild. We focus on the challenging task of GIF restoration by recovering information lost in the three steps of GIF creation: frame sampling, color quantization, and color dithering. We first propose a novel CNN architecture for color dequantization. It is built upon a compositional architecture for multi-step color correction, with a comprehensive loss function designed to handle large quantization errors. We then adapt the SuperSlomo network for temporal interpolation of GIF frames. We introduce two large datasets, namely GIF-Faces and GIF-Moments, for both training and evaluation. Experimental results show that our method can significantly improve the visual quality of GIFs, and outperforms direct baseline and state-of-the-art approaches.

关键词： Image and Video Synthesis vision Applications and Systems

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Matching Feature Sets for Few-Shot Image Classification

Matching Feature Sets for Few-Shot Image Classification

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

作者： Afrasiyabi, Arman Larochelle, Hugo Lalonde, Jean-Francois Gagne, Christian Univ Laval Quebec City PQ Canada Google Brain Mountain View CA USA Canada CIFAR AI Chair Toronto ON Canada Mila Montreal PQ Canada

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In image classification, it is common practice to train deep networks to extract a single feature vector per input image. Few-shot classification methods also mostly follow this trend. In this work, we depart from this established direction and instead propose to extract sets of feature vectors for each image. We argue that a set-based representation intrinsically builds a richer representation of images from the base classes, which can subsequently better transfer to the few-shot classes. To do so, we propose to adapt existing feature extractors to instead produce sets of feature vectors from images. Our approach, dubbed SetFeat, embeds shallow self-attention mechanisms inside existing encoder architectures. The attention modules are lightweight, and as such our method results in encoders that have approximately the same number of parameters as their original versions. During training and inference, a set-to-set matching metric is used to perform image classification. The effectiveness of our proposed architecture and metrics is demonstrated via thorough experiments on standard few-shot datasetsD namely miniImageNet, tieredImageNet, and CUBDin both the 1- and 5-shot scenarios. In all cases but one, our method outperforms the state-of-the-art.

关键词： Measurement Training computer vision computer architecture Feature extraction Market research Standards

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