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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024"

共 11897 条记录，以下是1791-1800 订阅

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MViTv2: Improved Multiscale vision Transformers for Classification and Detection

MViTv2: Improved Multiscale Vision Transformers for Classifi...

引用

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

作者： Li, Yanghao Wu, Chao-Yuan Fan, Haoqi Mangalam, Karttikeya Xiong, Bo Malik, Jitendra Feichtenhofer, Christoph Facebook AI Res Seattle WA 98101 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we study Multiscale vision Transformers (MViTv2) as a unified architecture for image and video classification, as well as object detection. We present an improved version of MViT that incorporates decomposed relative positional embeddings and residual pooling connections. We instantiate this architecture in five sizes and evaluate it for ImageNet classification, COCO detection and Kinetics video recognition where it outperforms prior work. We further compare MViTv2s' pooling attention to window attention mechanisms where it outperforms the latter in accuracy/compute. Without bells-and-whistles, MViTv2 has state-of-the-art performance in 3 domains: 88.8% accuracy on ImageNet classification, 58.7 AP(box) on COCO object detection as well as 86.1% on Kinetics-400 video classification. Code and models are available at https: //***/facebookresearch/mvit.

关键词： Representation learning Visualization Image segmentation Image recognition computer architecture Object detection Transformers

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SASIC: Stereo Image Compression with Latent Shifts and Stereo Attention

SASIC: Stereo Image Compression with Latent Shifts and Stere...

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

作者： Woedlinger, Matthias Kotera, Jan Xu, Jan Sablatnig, Robert TU Wien Vienna Austria Deep Render London England

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose a learned method for stereo image compression that leverages the similarity of the left and right images in a stereo pair due to overlapping fields of view. The left image is compressed by a learned compression method based on an autoencoder with a hyperprior entropy model. The right image uses this information from the previously encoded left image in both the encoding and decoding stages. In particular, for the right image, we encode only the residual of its latent representation to the optimally shifted latent of the left image. On top of that, we also employ a stereo attention module to connect left and right images during decoding. The performance of the proposed method is evaluated on two benchmark stereo image datasets (Cityscapes and InStereo2K) and outperforms previous stereo image compression methods while being significantly smaller in model size.

关键词： computer vision Image coding Codes Computational modeling Bit rate Performance gain Benchmark testing

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Semi-Supervised Object Detection via Multi-instance Alignment with Global Class Prototypes

Semi-Supervised Object Detection via Multi-instance Alignmen...

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

作者： Li, Aoxue Yuan, Peng Li, Zhenguo Huawei Noahs Ark Lab Shenzhen Guangdong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Semi-Supervised object detection (SSOD) aims to improve the generalization ability of object detectors with large-scale unlabeled images. Current pseudo-labeling-based SSOD methods individually learn from labeled data and unlabeled data, without considering the relation between them. To make full use of labeled data, we propose a Multi-instance Alignment model which enhances the prediction consistency based on Global Class Prototypes (MA-GCP). Specifically, we impose the consistency between pseudo ground-truths and their high-IoU candidates by minimizing the cross-entropy loss of their class distributions computed based on global class prototypes. These global class prototypes are estimated with the whole labeled dataset via the exponential moving average algorithm. To evaluate the proposed MA-GCP model, we integrate it into the state-of-the-art SSOD framework and experiments on two benchmark datasets demonstrate the effectiveness of our MA-GCP approach.

关键词： Training computer vision Computational modeling Prototypes Detectors Object detection Predictive models

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Masked Autoencoders Are Scalable vision Learners

Masked Autoencoders Are Scalable Vision Learners

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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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V-Doc : Visual questions answers with Documents

V-Doc : Visual questions answers with Documents

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

作者： Ding, Yihao Huang, Zhe Wang, Runlin Zhang, Yanhang Chen, Xianru Ma, Yuzhong Chung, Hyunsuk Han, Soyeon Caren Univ Sydney Sydney NSW Australia FortifyEdge Cambridge MA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose V-Doc, a question-answering tool using document images and PDF, mainly for researchers and general non-deep learning experts looking to generate, process, and understand the document visual question answering tasks. The V-Doc supports generating and using both extractive and abstractive question-answer pairs using documents images. The extractive QA selects a subset of tokens or phrases from the document contents to predict the answers, while the abstractive QA recognises the language in the content and generates the answer based on the trained model. Both aspects are crucial to understanding the documents, especially in an image format. We include a detailed scenario of question generation for the abstractive QA task. V-Doc supports a wide range of datasets and models, and is highly extensible through a declarative, framework-agnostic platform.(1)

关键词： Deep learning Visualization computer vision Computational modeling Predictive models Portable document format Question answering (information retrieval)

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UnweaveNet: Unweaving Activity Stories

UnweaveNet: Unweaving Activity Stories

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

作者： Price, Will Vondrick, Carl Damen, Dima Univ Bristol Bristol Avon England Columbia Univ New York NY 10027 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Our lives can be seen as a complex weaving of activities;we switch from one activity to another, to maximise our achievements or in reaction to demands placed upon us. Observing a video of unscripted daily activities, we parse the video into its constituent activity threads through a process we call unweaving. To accomplish this, we introduce a video representation explicitly capturing activity threads called a thread bank, along with a neural controller capable of detecting goal changes and resuming of past activities, together forming UnweaveNet. We train and evaluate UnweaveNet on sequences from the unscripted egocentric dataset EPIC-KITCHENS. We propose and showcase the efficacy of pretraining UnweaveNet in a self-supervised manner.

关键词： Representation learning Instruction sets Surveillance Memory management Switches Weaving pattern recognition

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Estimating Example Difficulty using Variance of Gradients

Estimating Example Difficulty using Variance of Gradients

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

作者： Agarwal, Chirag D'souza, Daniel Hooker, Sara Adobe MDSR Lab San Jose CA 95110 USA ML Collect Ann Arbor MI USA Google Res Mountain View CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In machine learning, a question of great interest is understanding what examples are challenging for a model to classify. Identifying atypical examples ensures the safe deployment of models, isolates samples that require further human inspection and provides interpretability into model behavior. In this work, we propose Variance of Gradients (VoG) as a valuable and efficient metric to rank data by difficulty and to surface a tractable subset of the most challenging examples for human-in-the-loop auditing. We show that data points with high VoG scores are far more difficult for the model to learn and over-index on corrupted or memorized examples. Further, restricting the evaluation to the test set instances with the lowest VoG improves the model's generalization performance. Finally, we show that VoG is a valuable and efficient ranking for out-of-distribution detection.

关键词： Measurement Ethics computer vision Computational modeling Machine learning Inspection Human in the loop

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SkinningNet: Two-Stream Graph Convolutional Neural Network for Skinning Prediction of Synthetic Characters

SkinningNet: Two-Stream Graph Convolutional Neural Network f...

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

作者： Mosella-Montoro, Albert Ruiz-Hidalgo, Javier Univ Politecn Cataluna Barcelona Spain

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This work presents SkinningNet, an end-to-end Two Stream Graph Neural Network architecture that computes skinning weights from an input mesh and its associated skeleton, without making any assumptions on shape class and structure of the provided mesh. Whereas previous methods pre-compute handcrafted features that relate the mesh and the skeleton or assume a fixed topology of the skeleton, the proposed method extracts this information in an end-to-end learnable fashion by jointly learning the best relationship between mesh vertices and skeleton joints. The proposed method exploits the benefits of the novel Multi-Aggregator Graph Convolution that combines the results of different aggregators during the summarizing step of the Message-Passing scheme, helping the operation to generalize for unseen topologies. Experimental results demonstrate the effectiveness of the contributions of our novel architecture, with SkinningNet outperforming current state-of-the-art alternatives.

关键词： Shape Network topology Machine vision computer architecture Feature extraction Skeleton Graph neural networks

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PCA-Based Knowledge Distillation Towards Lightweight and Content-Style Balanced Photorealistic Style Transfer Models

PCA-Based Knowledge Distillation Towards Lightweight and Con...

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

作者： Chiu, Tai-Yin Gurari, Danna Univ Texas Austin Austin TX 78712 USA Univ Colorado Boulder CO 80309 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Photorealistic style transfer entails transferring the style of a reference image to another image so the result seems like a plausible photo. Our work is inspired by the observation that existing models are slow due to their large sizes. We introduce PCA-based knowledge distillation to distill lightweight models and show it is motivated by theory. To our knowledge, this is the first knowledge distillation method for photorealistic style transfer. Our experiments demonstrate its versatility for use with different backbone architectures, VGG and MobileNet, across six image resolutions. Compared to existing models, our top-performing model runs at speeds 5-20x faster using at most 1% of the parameters. Additionally, our distilled models achieve a better balance between stylization strength and content preservation than existing models. To support reproducing our method and models, we share the code at https://***/chiutaiyin/PCA-Knowledge-Distillation.

关键词： Deep learning computer vision Image resolution Codes Computational modeling computer architecture pattern recognition

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Manifold Learning Benefits GANs

Manifold Learning Benefits GANs

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

作者： Ni, Yao Koniusz, Piotr Hartley, Richard Nock, Richard Australian Natl Univ Canberra ACT Australia CSIRO Data61 Sydney NSW Australia Google Res Canberra ACT Australia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper(1), we improve Generative Adversarial Networks by incorporating a manifold learning step into the discriminator. We consider locality-constrained linear and subspace-based manifolds(2), and locality-constrained nonlinear manifolds. In our design, the manifold learning and coding steps are intertwined with layers of the discriminator, with the goal of attracting intermediate feature representations onto manifolds. We adaptively balance the discrepancy between feature representations and their manifold view, which is a trade-off between denoising on the manifold and refining the manifold. We find that locality-constrained non-linear manifolds outperform linear manifolds due to their non-uniform density and smoothness. We also substantially outperform state-of-the-art baselines.

关键词： Manifolds computer vision Image coding Statistical analysis Refining Noise reduction Machine learning

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