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检索条件"任意字段=2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021"

共 11423 条记录，以下是41-50 订阅

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Self-supervised Augmentation Consistency for Adapting Semantic Segmentation

Self-supervised Augmentation Consistency for Adapting Semant...

引用

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

作者： Araslanov, Nikita Roth, Stefan Tech Univ Darmstadt Dept Comp Sci Darmstadt Germany Hessian AI Darmstadt Germany

ISBN: (纸本)9781665445092

We propose an approach to domain adaptation for semantic segmentation that is both practical and highly accurate. In contrast to previous work, we abandon the use of computationally involved adversarial objectives, network ensembles and style transfer. Instead, we employ standard data augmentation techniques photometric noise, flipping and scaling and ensure consistency of the semantic predictions across these image transformations. We develop this principle in a lightweight self-supervised framework trained on co-evolving pseudo labels without the need for cumbersome extra training rounds. Simple in training from a practitioner's standpoint, our approach is remarkably effective. We achieve significant improvements of the state-of-the-art segmentation accuracy after adaptation, consistent both across different choices of the backbone architecture and adaptation scenarios.

关键词： Training Image segmentation computer vision Semantics computer architecture pattern recognition Complexity theory

来源：评论

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OCONet: Image Extrapolation by Object Completion

OCONet: Image Extrapolation by Object Completion

引用

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

作者： Bowen, Richard Strong Chang, Huiwen Herrmann, Charles Teterwak, Piotr Liu, Ce Zabih, Ramin Cornell Tech New York NY 10044 USA Google Res Mountain View CA USA Boston Univ Boston MA 02215 USA

ISBN: (纸本)9781665445092

Image extrapolation extends an input image beyond the originally-captured field of view. Existing methods struggle to extrapolate images with salient objects in the foreground or are limited to very specific objects such as humans, but tend to work well on indoor/outdoor scenes. We introduce OCONet (Object COmpletion Networks) to extrapolate foreground objects, with an object completion network conditioned on its class. OCONet uses an encoder-decoder architecture trained with adversarial loss to predict the object's texture as well as its extent, represented as a predicted signed-distance field. An independent step extends the background, and the object is composited on top using the predicted mask. Both qualitative and quantitative results show that we improve on state-of-the-art image extrapolation results for challenging examples.

关键词： Extrapolation computer vision Semantics computer architecture pattern recognition

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InverseForm: A Loss Function for Structured Boundary-Aware Segmentation

InverseForm: A Loss Function for Structured Boundary-Aware S...

引用

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

作者： Borse, Shubhankar Wang, Ying Zhang, Yizhe Porikli, Fatih Qualcomm AI Res San Diego CA 92121 USA

ISBN: (纸本)9781665445092

We present a novel boundary-aware loss term for semantic segmentation using an inverse-transformation network, which efficiently learns the degree of parametric transformations between estimated and target boundaries. This plug-in loss term complements the cross-entropy loss in capturing boundary transformations and allows consistent and significant performance improvement on segmentation backbone models without increasing their size and computational complexity. We analyze the quantitative and qualitative effects of our loss function on three indoor and outdoor segmentation benchmarks, including Cityscapes, NYU-Depth-v2, and PASCAL, integrating it into the training phase of several backbone networks in both single-task and multi-task settings. Our extensive experiments show that the proposed method consistently outperforms baselines, and even sets the new state-of-the-art on two datasets.

关键词： Training computer vision Computational modeling Semantics computer architecture Benchmark testing pattern recognition

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Checkerboard Context Model for Efficient Learned Image Compression

Checkerboard Context Model for Efficient Learned Image Compr...

引用

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

作者： He, Dailan Zheng, Yaoyan Sun, Baocheng Wang, Yan Qin, Hongwei SenseTime Res Hong Kong Peoples R China

ISBN: (纸本)9781665445092

For learned image compression, the autoregressive context model is proved effective in improving the rate-distortion (RD) performance. Because it helps remove spatial redundancies among latent representations. However, the decoding process must be done in a strict scan order, which breaks the parallelization. We propose a parallelizable checkerboard context model (CCM) to solve the problem. Our two-pass checkerboard context calculation eliminates such limitations on spatial locations by re-organizing the decoding order. Speeding up the decoding process more than 40 times in our experiments, it achieves significantly improved computational efficiency with almost the same rate-distortion performance. To the best of our knowledge, this is the first exploration on parallelization-friendly spatial context model for learned image compression.

关键词： computer vision Image coding Computational modeling Redundancy Rate-distortion Decoding pattern recognition

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Few-Shot Classification with Feature Map Reconstruction Networks

Few-Shot Classification with Feature Map Reconstruction Netw...

引用

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

作者： Wertheimer, Davis Tang, Luming Hariharan, Bharath Cornell Univ Ithaca NY 14853 USA

ISBN: (纸本)9781665445092

In this paper we reformulate few-shot classification as a reconstruction problem in latent space. The ability of the network to reconstruct a query feature map from support features of a given class predicts membership of the query in that class. We introduce a novel mechanism for few-shot classification by regressing directly from support features to query features in closed form, without introducing any new modules or large-scale learnable parameters. The resulting Feature Map Reconstruction Networks are both more performant and computationally efficient than previous approaches. We demonstrate consistent and substantial accuracy gains on four fine-grained benchmarks with varying neural architectures. Our model is also competitive on the non-fine-grained mini-ImageNet and tiered-ImageNet benchmarks with minimal bells and whistles.

关键词： computer vision Computational modeling computer architecture Benchmark testing pattern recognition Computational efficiency

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Distilling Knowledge via Knowledge Review

Distilling Knowledge via Knowledge Review

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

作者： Chen, Pengguang Liu, Shu Zhao, Hengshuang Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China SmartMore Hong Kong Peoples R China Univ Oxford Oxford England

ISBN: (纸本)9781665445092

Knowledge distillation transfers knowledge from the teacher network to the student one, with the goal of greatly improving the performance of the student network. Previous methods mostly focus on proposing feature transformation and loss functions between the same level's features to improve the effectiveness. We differently study the factor of connection path cross levels between teacher and student networks, and reveal its great importance. For the first time in knowledge distillation, cross-stage connection paths are proposed. Our new review mechanism is effective and structurally simple. Our finally designed nested and compact framework requires negligible computation overhead, and outperforms other methods on a variety of tasks. We apply our method to classification, object detection, and instance segmentation tasks. All of them witness significant student network performance improvement.

关键词： Knowledge engineering computer vision Object detection pattern recognition Task analysis

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A Large-Scale Study on Unsupervised Spatiotemporal Representation Learning

A Large-Scale Study on Unsupervised Spatiotemporal Represent...

引用

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

作者： Feichtenhofer, Christoph Fan, Haoqi Xiong, Bo Girshick, Ross He, Kaiming Facebook AI Res FAIR Paris France

ISBN: (纸本)9781665445092

We present a large-scale study on unsupervised spatiotemporal representation learning from videos. With a unified perspective on four recent image-based frameworks, we study a simple objective that can easily generalize all these methods to space-time. Our objective encourages temporally-persistent features in the same video, and in spite of its simplicity, it works surprisingly well across: (i) different unsupervised frameworks, (ii) pre-training datasets, (iii) downstream datasets, and (iv) backbone architectures. We draw a series of intriguing observations from this study, e.g., we discover that encouraging long-spanned persistency can be effective even if the timespan is 60 seconds. In addition to state-of-the-art results in multiple benchmarks, we report a few promising cases in which unsupervised pre-training can outperform its supervised counterpart.

关键词： Training computer vision Codes computer architecture Benchmark testing Spatiotemporal phenomena pattern recognition

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Learning Goals from Failure

Learning Goals from Failure

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

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

ISBN: (纸本)9781665445092

We introduce a framework that predicts the goals behind observable human action in video. Motivated by evidence in developmental psychology, we leverage video of unintentional action to learn video representations of goals without direct supervision. Our approach models videos as contextual trajectories that represent both low-level motion and high-level action features. Experiments and visualizations show our trained model is able to predict the underlying goals in video of unintentional action. We also propose a method to "automatically correct" unintentional action by leveraging gradient signals of our model to adjust latent trajectories. Although the model is trained with minimal supervision, it is competitive with or outperforms baselines trained on large (supervised) datasets of successfully executed goals, showing that observing unintentional action is crucial to learning about goals in video.

关键词： Visualization computer vision Psychology Predictive models Encoding Trajectory pattern recognition

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Can We Characterize Tasks Without Labels or Features?

Can We Characterize Tasks Without Labels or Features?

引用

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

作者： Wallace, Bram Wu, Ziyang Hariharan, Bharath Cornell Univ Ithaca NY 14853 USA

ISBN: (纸本)9781665445092

The problem of expert model selection deals with choosing the appropriate pretrained network ("expert") to transfer to a target task. Methods, however, generally depend on two separate assumptions: the presence of labeled images and access to powerful "probe" networks that yield useful features. In this work, we demonstrate the current reliance on both of these aspects and develop algorithms to operate when either of these assumptions fail. In the unlabeled case, we show that pseudolabels from the probe network provide discriminative enough gradients to perform nearly-equal task selection even when the probe network is trained on imagery unrelated to the tasks. To compute the embedding with no probe network at all, we introduce the Task Tangent Kernel (TTK) which uses a kernelized distance across multiple random networks to achieve performance over double that of other methods with randomly initialized models.

关键词： computer vision Codes Shape Computational modeling Natural languages pattern recognition Task analysis

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Deep Multi-Task Learning for Joint Localization, Perception, and Prediction

Deep Multi-Task Learning for Joint Localization, Perception,...

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

作者： Phillips, John Martinez, Julieta Barsan, Ioan Andrei Casas, Sergio Sadat, Abbas Urtasun, Raquel Uber Adv Technol Grp Pittsburgh PA 15201 USA Univ Waterloo Waterloo ON Canada Univ Toronto Toronto ON Canada

ISBN: (纸本)9781665445092

Over the last few years, we have witnessed tremendous progress on many subtasks of autonomous driving including perception, motion forecasting, and motion planning. However, these systems often assume that the car is accurately localized against a high-definition map. In this paper we question this assumption, and investigate the issues that arise in state-of-the-art autonomy stacks under localization error. Based on our observations, we design a system that jointly performs perception, prediction, and localization. Our architecture is able to reuse computation between the three tasks, and is thus able to correct localization errors efficiently. We show experiments on a large-scale autonomy dataset, demonstrating the efficiency and accuracy of our proposed approach.

关键词： Location awareness computer vision Graphics processing units Object detection computer architecture Planning pattern recognition

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