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检索条件"任意字段=2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020"

共 11281 条记录，以下是31-40 订阅

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Single-View View Synthesis with Multiplane Images

Single-View View Synthesis with Multiplane Images

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Tucker, Richard Snavely, Noah Google Res Mountain View CA 94043 USA

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171685

A recent strand of work in view synthesis uses deep learning to generate multiplane images-a camera-centric, layered 3D representation-given two or more input images at known viewpoints. We apply this representation to single-view view synthesis, a problem which is more challenging but has potentially much wider application. Our method learns to predict a multiplane image directly from a single image input, and we introduce scale-invariant view synthesis for supervision, enabling us to train on online video. We show this approach is applicable to several different datasets, that it additionally generates reasonable depth maps, and that it learns to fill in content behind the edges of foreground objects in background layers.

关键词： Cameras Training Three-dimensional displays Rendering (computer graphics) Task analysis Training data Interpolation

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Seeing the World in a Bag of Chips

Seeing the World in a Bag of Chips

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Park, Jeong Joon Holynski, Aleksander Seitz, Steven M. Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9781728171685

We address the dual problems of novel view synthesis and environment reconstruction from hand-held RGBD sensors. Our contributions include 1) modeling highly specular objects, 2) modeling inter-reflections and Fresnel effects, and 3) enabling surface light field reconstruction with the same input needed to reconstruct shape alone. In cases where scene surface has a strong mirror-like material component, we generate highly detailed environment images, revealing room composition, objects, people, buildings, and trees visible through windows. Our approach yields state of the art view synthesis techniques, operates on low dynamic range imagery, and is robust to geometric and calibration errors.

关键词： computer vision

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Counterfactual vision and Language Learning

Counterfactual Vision and Language Learning

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Abbasnejad, Ehsan Teney, Damien Parvaneh, Amin Shi, Javen van den Hengel, Anton Australian Inst Machine Learning Adelaide SA Australia Univ Adelaide Adelaide SA Australia

ISBN: (纸本)9781728171685

The ongoing success of visual question answering methods has been somewhat surprising given that, at its most general, the problem requires understanding the entire variety of both visual and language stimuli. It is particularly remarkable that this success has been achieved on the basis of comparatively small datasets, given the scale of the problem. One explanation is that this has been accomplished partly by exploiting bias in the datasets rather than developing deeper multi-modal reasoning. This fundamentally limits the generalization of the method, and thus its practical applicability. We propose a method that addresses this problem by introducing counterfactuals in the training. In doing so we leverage structural causal models for counterfactual evaluation to formulate alternatives, for instance, questions that could be asked of the same image set. We show that simulating plausible alternative training data through this process results in better generalization.

关键词： Visual languages

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Active vision for Early recognition of Human Actions

Active Vision for Early Recognition of Human Actions

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Wang, Boyu Huang, Lihan Minh Hoai SUNY Stony Brook Stony Brook NY 11794 USA VinAI Res Hanoi Vietnam

ISBN: (纸本)9781728171685

We propose a method for early recognition of human actions, one that can take advantages of multiple cameras while satisfying the constraints due to limited communication bandwidth and processing power. Our method considers multiple cameras, and at each time step, it will decide the best camera to use so that a confident recognition decision can be reached as soon as possible. We formulate the camera selection problem as a sequential decision process, and learn a view selection policy based on reinforcement learning. We also develop a novel recurrent neural network architecture to account for the unobserved video frames and the irregular intervals between the observed frames. Experiments on three datasets demonstrate the effectiveness of our approach for early recognition of human actions.

关键词： Reinforcement learning

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Revisiting Pose-Normalization for Fine-Grained Few-Shot recognition

Revisiting Pose-Normalization for Fine-Grained Few-Shot Reco...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Tang, Luming Wertheimer, Davis Hariharan, Bharath Cornell Univ Ithaca NY 14850 USA

ISBN: (纸本)9781728171685

Pew shot, fine-grained classification requires a model to learn subtle, fine-grained distinctions between different classes (e.g., birds) based on a few images alone. This requires a remarkable degree of invariance to pose, articulation and background. A solution is to use pose normalized representations: first localize semantic parts in each image, and then describe images by characterizing, the appearance of each part While such representations are out offavor far fully supervised classification, we show that they are extremely effective for few-shot fine-grained classification. With a minimal increase in model capacity, pose normalization improves accuracy between 10 and 20 percentage points for shallow and deep architectures, generalizes better to new domains, and is effective for multiple few-shot algorithms and network backbones. Code is available at https://***/Tsingularity/PoseNorm_Fewshot

关键词： Semantics

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Benchmarking the Robustness of Semantic Segmentation Models

Benchmarking the Robustness of Semantic Segmentation Models

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Kamann, Christoph Rother, Carsten Heidelberg Univ HCI IWR Visual Learning Lab Heidelberg Germany

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171685

When designing a semantic segmentation module for a practical application, such as autonomous driving, it is crucial to understand the robustness of the module with respect to a wide range of image corruptions. While there are recent robustness studies for full-image classification, we are the first to present an exhaustive study for semantic segmentation, based on the state-of-the-art model DeepLabv3+. To increase the realism of our study, we utilize almost 400,000 images generated from Cityscapes, PASCAL VOC 2012, and ADE20K. Based on the benchmark study, we gain several new insights. Firstly, contrary to full-image classification, model robustness increases with model performance, in most cases. Secondly, some architecture properties affect robustness significantly, such as a Dense Prediction Cell, which was designed to maximize performance on clean data only.

关键词： Robustness Semantics computer architecture Image segmentation Feature extraction Benchmark testing Cameras

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Gated Channel Transformation for Visual recognition

Gated Channel Transformation for Visual Recognition

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Yang, Zongxin Zhu, Linchao Wu, Yu Yang, Yi Baidu Res Sunnyvale CA USA Univ Thchnol Sydney ReLER Ultimo NSW Australia

ISBN: (纸本)9781728171685

In this work, we propose a generally applicable transformation unit for visual recognition with deep convolutional neural networks. This transformation explicitly models channel relationships with explainable control variables. These variables determine the neuron behaviors of competition or cooperation, and they are jointly optimized with the convolutional weight towards more accurate recognition. In Squeeze-and-Excitation (SE) Networks, the channel relationships are implicitly learned by fully connected layers, and the SE block is integrated at the block-level. We instead introduce a channel normalization layer to reduce the number of parameters and computational complexity. This lightweight layer incorporates a simple l(2) normalization, enabling our transformation unit applicable to operator-level without much increase of additional parameters. Extensive experiments demonstrate the effectiveness of our unit with clear margins on many vision tasks, i.e., image classification on ImageNet, object detection and instance segmentation on COCO, video classification on Kinetics.

关键词： Object detection

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OOPS! Predicting Unintentional Action in Video

OOPS! Predicting Unintentional Action in Video

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Epstein, Dave Chen, Boyuan Vondrick, Carl Columbia Univ New York NY 10027 USA

ISBN: (纸本)9781728171685

From just a short glance at a video, we can often tell whether a person's action is intentional or not. Can we train a model to recognize this? We introduce a dataset of in-the-wild videos of unintentional action, as well as a suite of tasks for recognizing, localizing, and anticipating its onset. We train a supervised neural network as a baseline and analyze its performance compared to human consistency on the tasks. We also investigate self-supervised representations that leverage natural signals in our dataset, and show the effectiveness of an approach that uses the intrinsic speed of video to perform competitively with highly-supervised pre-training. However, a significant gap between machine and human performance remains.

关键词： computer vision

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How Useful is Self-Supervised Pretraining for Visual Tasks?

How Useful is Self-Supervised Pretraining for Visual Tasks?

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Newell, Alejandro Deng, Jia Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9781728171685

Recent advances have spurred incredible progress in self-supervised pretraining for vision. We investigate what factors may play a role in the utility of these pretraining methods for practitioners. To do this, we evaluate various self-supervised algorithms across a comprehensive array of synthetic datasets and downstream tasks. We prepare a suite of synthetic data that enables an endless supply of annotated images as well as MI control over dataset difficulty. Our experiments offer insights into how the utility of self-supervision changes as the number of available labels grows as well as how the utility changes as a function of the downstream task and the properties of the training data. We also find that linear evaluation does not correlate with finetuning performance. Code and data is available at ***/princeton-vl/selfstudy.

关键词： vision

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Zero-Assignment Constraint for Graph Matching with Outliers

Zero-Assignment Constraint for Graph Matching with Outliers

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Wang, Fudong Xue, Nan Yu, Jin-Gang Xia, Gui-Song Wuhan Univ Wuhan Peoples R China South China Univ Technol Guangzhou Peoples R China

ISBN: (纸本)9781728171685

Graph matching (GM), as a longstanding problem in computer vision and pattern recognition, still suffers from numerous cluttered outliers in practical applications. To address this issue, we present the zero-assignment constraint (ZAC) for approaching the graph matching problem in the presence of outliers. The underlying idea is to suppress the matchings of outliers by assigning zero-valued vectors to the potential outliers in the obtained optimal correspondence matrix. We provide elaborate theoretical analysis to the problem, i.e., GM with ZAC, and figure out that the GM problem with and without outliers are intrinsically different, which enables us to put forward a sufficient condition to construct valid and reasonable objective function. Consequently, we design an efficient outlier-robust algorithm to significantly reduce the incorrect or redundant matchings caused by numerous outliers. Extensive experiments demonstrate that our method can achieve the stateof-the-art performance in terms of accuracy and efficiency, especially in the presence of numerous outliers.

关键词： computer vision

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