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检索条件"任意字段=2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016"

共 21008 条记录，以下是1491-1500 订阅

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SASIC: Stereo Image Compression with Latent Shifts and Stereo Attention

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

引用

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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Few-shot Learning with Noisy Labels

Few-shot Learning with Noisy Labels

引用

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

作者： Liang, Kevin J. Rangrej, Samrudhdhi B. Petrovic, Vladan Hassner, Tal Facebook AI Res Menlo Pk CA 94025 USA McGill Univ Montreal PQ Canada

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Few-shot learning (FSL) methods typically assume clean support sets with accurately labeled samples when training on novel classes. This assumption can often be unrealistic: support sets, no matter how small, can still include mislabeled samples. Robustness to label noise is therefore essential for FSL methods to be practical, but this problem surprisingly remains largely unexplored. To address mislabeled samples in FSL settings, we make several technical contributions. (1) We offer simple, yet effective, feature aggregation methods, improving the prototypes used by ProtoNet, a popular FSL technique. (2) We describe a novel Transformer model for Noisy Few-Shot Learning (TraNFS). TraNFS leverages a transformer's attention mechanism to weigh mislabeled versus correct samples. (3) Finally, we extensively test these methods on noisy versions of Mini-ImageNet and TieredImageNet. Our results show that TraNFS is on-par with leading FSL methods on clean support sets, yet outperforms them, by far, in the presence of label noise.

关键词： Training computer vision Computational modeling Prototypes Transformers Robustness pattern recognition

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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...

引用

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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Style-ERD: Responsive and Coherent Online Motion Style Transfer

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

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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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CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation

CMT-DeepLab: Clustering Mask Transformers for Panoptic Segme...

引用

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

作者： Yu, Qihang Wang, Huiyu Kim, Dahun Qiao, Siyuan Collins, Maxwell Zhu, Yukun Adam, Hartwig Yuille, Alan Chen, Liang-Chieh Johns Hopkins Univ Baltimore MD 21218 USA Korea Adv Inst Sci & Technol Daejeon South Korea Google Res Mountain View CA USA Google Mountain View CA 94043 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose Clustering Mask Transformer (CMT-DeepLab), a transformer-based framework for panoptic segmentation designed around clustering. It rethinks the existing transformer architectures used in segmentation and detection;CMT-DeepLab considers the object queries as cluster centers, which fill the role of grouping the pixels when applied to segmentation. The clustering is computed with an alternating procedure, by first assigning pixels to the clusters by their feature affinity, and then updating the cluster centers and pixel features. Together, these operations comprise the Clustering Mask Transformer (CMT) layer, which produces cross-attention that is denser and more consistent with the final segmentation task. CMT-DeepLab improves the performance over prior art significantly by 4.4% PQ, achieving a new state-of-the-art of 55.7% PQ on the COCO test-dev set.

关键词： computer vision Art computer architecture Transformers pattern recognition Task analysis

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Do Explanations Explain? Model Knows Best

Do Explanations Explain? Model Knows Best

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

作者： Khakzar, Ashkan Khorsandi, Pedram Nobahari, Rozhin Navab, Nassir Tech Univ Munich Munich Germany Quebec Artificial Intelligence Inst Mila Montreal PQ Canada

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

It is a mystery which input features contribute to a neural network's output. Various explanation (feature attribution) methods are proposed in the literature to shed light on the problem. One peculiar observation is that these explanations (attributions) point to different features as being important. The phenomenon raises the question, which explanation to trust? We propose a framework for evaluating the explanations using the neural network model itself. The framework leverages the network to generate input features that impose a particular behavior on the output. Using the generated features, we devise controlled experimental setups to evaluate whether an explanation method conforms to an axiom. Thus we propose an empirical framework for axiomatic evaluation of explanation methods. We evaluate well-known and promising explanation solutions using the proposed framework. The framework provides a toolset to reveal properties and drawbacks within existing and future explanation solutions.

关键词： computer vision Perturbation methods Neural networks pattern recognition Behavioral sciences

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MViTv2: Improved Multiscale vision Transformers for Classification and Detection

MViTv2: Improved Multiscale Vision Transformers for Classifi...

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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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Incremental Learning in Semantic Segmentation from Image Labels

Incremental Learning in Semantic Segmentation from Image Lab...

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

作者： Cermelli, Fabio Fontanel, Dario Tavera, Antonio Ciccone, Marco Caputo, Barbara Politecn Torino Turin Italy Italian Inst Technol Genoa Italy

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Although existing semantic segmentation approaches achieve impressive results, they still struggle to update their models incrementally as new categories are uncovered. Furthermore, pixel-by-pixel annotations are expensive and time-consuming. This paper proposes a novel framework for Weakly Incremental Learning for Semantic Segmentation, that aims at learning to segment new classes from cheap and largely available image-level labels. As opposed to existing approaches, that need to generate pseudolabels offline, we use a localizes, trained with image-level labels and regularized by the segmentation model, to obtain pseudo-supervision online and update the model incrementally. We cope with the inherent noise in the process by using soft-labels generated by the localizes. We demonstrate the effectiveness of our approach on the Pascal VOC and COCO datasets, outperforming offline weakly-supervised methods and obtaining results comparable with incremental learning methods with full supervision.(1)

关键词： Learning systems Location awareness Image segmentation computer vision Protocols Annotations Semantics

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Contrastive Learning for Unsupervised Video Highlight Detection

Contrastive Learning for Unsupervised Video Highlight Detect...

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

作者： Badamdorj, Taivanbat Rochan, Mrigank Wang, Yang Cheng, Li Univ Alberta Edmonton AB Canada Huawei Noahs Ark Lab Winnipeg MB Canada Univ Manitoba Winnipeg MB Canada

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Video highlight detection can greatly simplify video browsing, potentially paving the way for a wide range of applications. Existing efforts are mostly fully-supervised, requiring humans to manually identify and label the interesting moments (called highlights) in a video. Recent weakly supervised methods forgo the use of highlight annotations, but typically require extensive efforts in collecting external data such as web-crawled videos for model learning. This observation has inspired us to consider unsupervised highlight detection where neither frame-level nor video-level annotations are available in training. We propose a simple contrastive learning framework for unsupervised highlight detection. Our framework encodes a video into a vector representation by learning to pick video clips that help to distinguish it from other videos via a contrastive objective using dropout noise. This inherently allows our framework to identify video clips corresponding to highlight of the video. Extensive empirical evaluations on three highlight detection benchmarks demonstrate the superior performance of our approach.

关键词： Training computer vision Annotations Benchmark testing Data models pattern recognition

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Open Challenges in Deep Stereo: the Booster Dataset

Open Challenges in Deep Stereo: the Booster Dataset

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

作者： Ramirez, Pierluigi Zama Tosi, Fabio Poggi, Matteo Salti, Samuele Mattoccia, Stefano Di Stefano, Luigi Univ Bologna Dept Comp Sci & Engn DISI CVLAB Bologna Italy

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present a novel high-resolution and challenging stereo dataset framing indoor scenes annotated with dense and accurate ground-truth disparities. Peculiar to our dataset is the presence of several specular and transparent surfaces, i.e. the main causes of failures for state-of-the-art stereo networks. Our acquisition pipeline leverages a novel deep space-time stereo framework which allows for easy and accurate labeling with sub-pixel precision. We release a total of 419 samples collected in 64 different scenes and annotated with dense ground-truth disparities. Each sample include a high-resolution pair (12 Mpx) as well as an unbalanced pair (Left: 12 Mpx, Right: 1.1 Mpx). Additionally, we provide manually annotated material segmentation masks and 15K unlabeled samples. We evaluate state-of-the-art deep networks based on our dataset, highlighting their limitations in addressing the open challenges in stereo and drawing hints for future research.

关键词： computer vision Three-dimensional displays Pipelines Sensors pattern recognition Labeling

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