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检索条件"任意字段=2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022"

共 11141 条记录，以下是81-90 订阅

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Total Variation Optimization Layers for computer vision

Total Variation Optimization Layers for Computer Vision

引用

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

作者： Yeh, Raymond A. Hu, Yuan-Ting Ren, Zhongzheng Schwing, Alexander G. Toyota Technol Inst Chicago IL 60637 USA Univ Illinois Champaign IL USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Optimization within a layer of a deep-net has emerged as a new direction for deep-net layer design. However, there are two main challenges when applying these layers to computer vision tasks: (a) which optimization problem within a layer is useful?;(b) how to ensure that computation within a layer remains efficient? To study question (a), in this work, we propose total variation (TV) minimization as a layer for computer vision. Motivated by the success of total variation in image processing, we hypothesize that TV as a layer provides useful inductive bias for deep-nets too. We study this hypothesis on five computer vision tasks: image classification, weakly supervised object localization, edge-preserving smoothing, edge detection, and image denoising, improving over existing baselines. To achieve these results we had to address question (b): we developed a GPU-based projectedNewton method which is 37x faster than existing solutions.

关键词： Location awareness computer vision TV Smoothing methods Image edge detection Minimization pattern recognition

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Style-ERD: Responsive and Coherent Online Motion Style Transfer

Style-ERD: Responsive and Coherent Online Motion Style Trans...

引用

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

作者： Tao, Tianxin Zhan, Xiaohang Chen, Zhongquan van de Panne, Michiel Univ British Columbia Vancouver BC Canada Univ Calif Davis Davis CA 95616 USA

ISBN: (纸本)9781665469463

Motion style transfer is a common method for enriching character animation. Motion style transfer algorithms are often designed for offline settings where motions are processed in segments. However, for online animation applications, such as real-time avatar animation from motion capture, motions need to be processed as a stream with minimal latency. In this work, we realize a flexible, high-quality motion style transfer method for this setting. We propose a novel style transfer model, Style-ERD, to stylize motions in an online manner with an Encoder-Recurrent-Decoder structure, along with a novel discriminator that combines feature attention and temporal attention. Our method stylizes motions into multiple target styles with a unified model. Although our method targets online settings, it outperforms previous offline methods in motion realism and style expressiveness and provides significant gains in runtime efficiency.

关键词： computer vision Runtime Perturbation methods Motion segmentation Memory modules Animation Real-time systems

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End-to-end Generative Pretraining for Multimodal Video Captioning

End-to-end Generative Pretraining for Multimodal Video Capti...

引用

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

作者： Seo, Paul Hongsuck Nagrani, Arsha Arnab, Anurag Schmid, Cordelia Google Res Mountain View CA 94043 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent video and language pretraining frameworks lack the ability to generate sentences. We present Multimodal Video Generative Pretraining (MV-GPT), a new pretraining framework for learning from unlabelled videos which can be effectively used for generative tasks such as multimodal video captioning. Unlike recent video-language pretraining frameworks, our framework trains both a multimodal video encoder and a sentence decoder jointly. To overcome the lack of captions in unlabelled videos, we leverage the future utterance as an additional text source and propose a bidirectional generation objective-we generate future utterances given the present mulitmodal context, and also the present utterance given future observations. With this objective, we train an encoder-decoder model end-to-end to generate a caption from raw pixels and transcribed speech directly. Our model achieves state-of-the-art performance for multimodal video captioning on four standard benchmarks, as well as for other video understanding tasks such as VideoQA, video retrieval and action classification.

关键词： Representation learning computer vision Computational modeling Bidirectional control Benchmark testing Decoding pattern recognition

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Programmatic Concept Learning for Human Motion Description and Synthesis

Programmatic Concept Learning for Human Motion Description a...

引用

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

作者： Kulal, Sumith Mao, Jiayuan Aiken, Alex Wu, Jiajun Stanford Univ Stanford CA 94305 USA MIT Cambridge MA 02139 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We introduce Programmatic Motion Concepts, a hierarchical motion representation for human actions that captures both low-level motion and high-level description as motion concepts. This representation enables human motion description, interactive editing, and controlled synthesis of novel video sequences within a single framework. We present an architecture that learns this concept representation from paired video and action sequences in a semi-supervised manner. The compactness of our representation also allows us to present a low-resource training recipe for data-efficient learning. By outperforming established baselines, especially in the small data regime, we demonstrate the efficiency and effectiveness of our framework for multiple applications.

关键词： Training computer vision Video sequences computer architecture pattern recognition

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Automatic Color Image Stitching Using Quaternion Rank-1 Alignment

Automatic Color Image Stitching Using Quaternion Rank-1 Alig...

引用

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

作者： Li, Jiaxue Zhou, Yicong Univ Macau Dept Comp & Informat Sci Taipa Macao Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Color image stitching is a challenging task in real-world applications. This paper first proposes a quaternion rank-1 alignment (QR1A) model for high-precision color image alignment. To solve the optimization problem of QR1A, we develop a nested iterative algorithm under the framework of complex-valued alternating direction method of multipliers. To quantitatively evaluate image stitching performance, we propose a perceptual seam quality (PSQ) measure to calculate misalignments of local regions along the seamline. Using QR1A and PSQ, we further propose an automatic color image stitching (ACIS-QR1A) framework. In this framework, the automatic strategy and iterative learning strategy are developed to simultaneously learn the optimal seamline and local alignment. Extensive experiments on challenging datasets demonstrate that the proposed ACIS-QR1A is able to obtain high-quality stitched images under several difficult scenarios including large parallax, low textures, moving objects, large occlusions or/and their combinations.

关键词： Photography computer vision Quaternions Color Convex functions pattern recognition Iterative methods

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Long-tail recognition via Compositional Knowledge Transfer

Long-tail Recognition via Compositional Knowledge Transfer

引用

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

作者： Parisot, Sarah Esperanca, Pedro M. McDonagh, Steven Madarasz, Tamas J. Yang, Yongxin Li, Zhenguo Huawei Noahs Ark Lab Hong Kong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this work, we introduce a novel strategy for long-tail recognition that addresses the tail classes' few-shot problem via training free knowledge transfer. Our objective is to transfer knowledge acquired from information-rich common classes to semantically similar, and yet data-hungry, rare classes in order to obtain stronger tail class representations. We leverage the fact that class prototypes and learned cosine classifiers provide two different, complementary representations of class cluster centres in feature space, and use an attention mechanism to select and recompose learned classifier features from common classes to obtain higher quality rare class representations. Our knowledge transfer process is training free, reducing overfitting risks, and can afford continual extension of classifiers to new classes. Experiments show that our approach can achieve significant performance boosts on rare classes while maintaining robust common class performance, outperforming directly comparable state-of-the-art models.

关键词： Training Analytical models computer vision Prototypes Tail Performance gain Benchmark testing

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Multiview Transformers for Video recognition

Multiview Transformers for Video Recognition

引用

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

作者： Yan, Shen Xiong, Xuehan Arnab, Anurag Lu, Zhichao Zhang, Mi Sun, Chen Schmid, Cordelia Google Res Cambridge MA USA Michigan State Univ E Lansing MI 48824 USA Brown Univ Providence RI 02912 USA Google Mountain View CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Video understanding requires reasoning at multiple spatiotemporal resolutions - from short fine-grained motions to events taking place over longer durations. Although transformer architectures have recently advanced the state-of-the-art, they have not explicitly modelled different spatiotemporal resolutions. To this end, we present Multiview Transformers for Video recognition (MTV). Our model consists of separate encoders to represent different views of the input video with lateral connections to flue information across views. We present thorough ablation studies of our model and show that MTV consistently performs better than single-view counterparts in terms of accuracy and computational cost across a range of model sizes. Furthermore, we achieve state-of-the-art results on six standard datasets, and improve even further with large-scale pretraining. Code and checkpoints are available at: https://***/google-research/scenic.

关键词： computer vision Fuses Computational modeling computer architecture Transformers Cognition Spatiotemporal phenomena

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Generating High Fidelity Data from Low-density Regions using Diffusion Models

Generating High Fidelity Data from Low-density Regions using...

引用

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

作者： Sehwag, Vikash Hazirbas, Caner Gordo, Albert Ozgenel, Firat Ferrer, Cristian Canton Princeton Univ Princeton NJ 08544 USA Meta AI Menlo Pk CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Our work focuses on addressing sample deficiency from low-density regions of data manifold in common image datasets. We leverage diffusion process based generative models to synthesize novel images from low-density regions. We observe that uniform sampling from diffusion models predominantly samples from high-density regions of the data manifold. Therefore, we modify the sampling process to guide it towards low-density regions while simultaneously maintaining the fidelity of synthetic data. We rigorously demonstrate that our process successfully generates novel high fidelity samples from low-density regions. We further examine generated samples and show that the model does not memorize low-density data and indeed learns to generate novel samples from low-density regions.

关键词： Manifolds computer vision Computational modeling Diffusion processes Data models pattern recognition

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Masked Autoencoders Are Scalable vision Learners

Masked Autoencoders Are Scalable Vision Learners

引用

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

作者： He, Kaiming Chen, Xinlei Xie, Saining Li, Yanghao Dollar, Piotr Girshick, Ross Facebook AI Res FAIR New York NY 10003 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This paper shows that masked autoencoders (MAE) are scalable self-supervised learners for computer vision. Our MAE approach is simple: we mask random patches of the input image and reconstruct the missing pixels. It is based on two core designs. First, we develop an asymmetric encoder-decoder architecture, with an encoder that operates only on the visible subset of patches (without mask tokens), along with a lightweight decoder that reconstructs the original image from the latent representation and mask tokens. Second, we find that masking a high proportion of the input image, e.g., 75%, yields a nontrivial and meaningful self-supervisory task. Coupling these two designs enables us to train large models efficiently and effectively: we accelerate training (by 3x or more) and improve accuracy. Our scalable approach allows for learning high-capacity models that generalize well: e.g., a vanilla ViT-Huge model achieves the best accuracy (87.8%) among methods that use only ImageNet-IK data. Transfer performance in downstream tasks outperforms supervised pretraining and shows promising scaling behavior.

关键词： Training Couplings computer vision Computational modeling computer architecture Data models pattern recognition

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A case for using rotation invariant features in state of the art feature matchers

A case for using rotation invariant features in state of the...

引用

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

作者： Bokman, Georg Kahl, Fredrik Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9781665487399

The aim of this paper is to demonstrate that a state of the art feature matcher (LoFTR) can be made more robust to rotations by simply replacing the backbone CNN with a steerable CNN which is equivariant to translations and image rotations. It is experimentally shown that this boost is obtained without reducing performance on ordinary illumination and viewpoint matching sequences.

关键词： computer vision conferences Lighting pattern matching

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