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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

共 30988 条记录，以下是4791-4800 订阅

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Semi-supervised Semantic Segmentation with Directional Context-aware Consistency

Semi-supervised Semantic Segmentation with Directional Conte...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Lai, Xin Tian, Zhuotao Jiang, Li Liu, Shu Zhao, Hengshuang Wang, Liwei 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

Semantic segmentation has made tremendous progress in recent years. However, satisfying performance highly depends on a large number of pixel-level annotations. Therefore, in this paper, we focus on the semi-supervised segmentation problem where only a small set of labeled data is provided with a much larger collection of totally unlabeled images. Nevertheless, due to the limited annotations, models may overly rely on the contexts available in the training data, which causes poor generalization to the scenes unseen before. A preferred high-level representation should capture the contextual information while not losing self-awareness. Therefore, we propose to maintain the context-aware consistency between features of the same identity but with different contexts, making the representations robust to the varying environments. Moreover, we present the Directional Contrastive Loss (DC Loss) to accomplish the consistency in a pixel-to-pixel manner, only requiring the feature with lower quality to be aligned towards its counterpart. In addition, to avoid the false-negative samples and filter the uncertain positive samples, we put forward two sampling strategies. Extensive experiments show that our simple yet effective method surpasses current state-of-the-art methods by a large margin and also generalizes well with extra image-level annotations.

关键词： Image segmentation computer vision Annotations Semantics Training data Data models pattern recognition

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HumanGPS: Geodesic PreServing Feature for Dense Human Correspondences

HumanGPS: Geodesic PreServing Feature for Dense Human Corres...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Tan, Feitong Tang, Danhang Dou, Mingsong Guo, Kaiwen Pandey, Rohit Keskin, Cem Du, Ruofei Sun, Deqing Bouaziz, Sofien Fanello, Sean Tan, Ping Zhang, Yinda Google Mountain View CA 94043 USA Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781665445092

In this paper, we address the problem of building dense correspondences between human images under arbitrary camera viewpoints and body poses. Prior art either assumes small motion between frames or relies on local descriptors, which cannot handle large motion or visually ambiguous body parts, e.g., left vs. right hand. In contrast, we propose a deep learning framework that maps each pixel to a feature space, where the feature distances reflect the geodesic distances among pixels as if they were projected onto the surface of a 3D human scan. To this end, we introduce novel loss functions to push features apart according to their geodesic distances on the surface. Without any semantic annotation, the proposed embeddings automatically learn to differentiate visually similar parts and align different subjects into an unified feature space. Extensive experiments show that the learned embeddings can produce accurate correspondences between images with remarkable generalization capabilities on both intra and inter subjects.(1)

关键词： Deep learning Image segmentation computer vision Three-dimensional displays Shape Semantics Buildings

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Learning Continuous Image Representation with Local Implicit Image Function

Learning Continuous Image Representation with Local Implicit...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Chen, Yinbo Liu, Sifei Wang, Xiaolong Univ Calif San Diego La Jolla CA 92093 USA NVIDIA Santa Clara CA USA

ISBN: (纸本)9781665445092

How to represent an image? While the visual world is presented in a continuous manner, machines store and see the images in a discrete way with 2D arrays of pixels. In this paper, we seek to learn a continuous representation for images. Inspired by the recent progress in 3D reconstruction with implicit neural representation, we propose Local Implicit Image Function (LIIF), which takes an image coordinate and the 2D deep features around the coordinate as inputs, predicts the RGB value at a given coordinate as an output. Since the coordinates are continuous, LIIF can be presented in arbitrary resolution. To generate the continuous representation for images, we train an encoder with LIIF representation via a self-supervised task with super-resolution. The learned continuous representation can be presented in arbitrary resolution even extrapolate to x30 higher resolution, where the training tasks are not provided. We further show that LIIF representation builds a bridge between discrete and continuous representation in 2D, it naturally supports the learning tasks with size-varied image ground-truths and significantly outperforms the method with resizing the ground-truths.

关键词： Training Bridges Visualization computer vision Three-dimensional displays Superresolution Image representation

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LayoutTransformer: Scene Layout Generation with Conceptual and Spatial Diversity

LayoutTransformer: Scene Layout Generation with Conceptual a...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Yang, Cheng-Fu Fan, Wan-Cyuan Yang, Fu-En Wang, Yu-Chiang Frank Natl Taiwan Univ Grad Inst Commun Engn Taipei Taiwan ASUS Intelligent Cloud Serv Taipei Taiwan

ISBN: (纸本)9781665445092

When translating text inputs into layouts or images, existing works typically require explicit descriptions of each object in a scene, including their spatial information or the associated relationships. To better exploit the text input, so that implicit objects or relationships can be properly inferred during layout generation, we propose a LayoutTransformer Network (LT-Net) in this paper. Given a scene-graph input, our LT-Net uniquely encodes the semantic features for exploiting their co-occurrences and implicit relationships. This allows one to manipulate conceptually diverse yet plausible layout outputs. Moreover, the decoder of our LT-Net translates the encoded contextual features into bounding boxes with self-supervised relation consistency preserved. By fitting their distributions to Gaussian mixture models, spatially-diverse layouts can be additionally produced by LT-Net. We conduct extensive experiments on the datasets of MS-COCO and Visual Genome, and confirm the effectiveness and plausibility of our LT-Net over recent layout generation models. Codes will be released at LaynaTransformer.

关键词： Visualization computer vision Spatial diversity Layout Semantics Fitting Genomics

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DriveGAN: Towards a Controllable High-Quality Neural Simulation

DriveGAN: Towards a Controllable High-Quality Neural Simulat...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Kim, Seung Wook Philion, Jonah Torralba, Antonio Fidler, Sanja NVIDIA Santa Clara CA 95051 USA Univ Toronto Toronto ON Canada Vector Inst Toronto ON Canada MIT Cambridge MA 02139 USA

ISBN: (纸本)9781665445092

Realistic simulators are critical for training and verifying robotics systems. While most of the contemporary simulators are hand-crafted, a scaleable way to build simulators is to use machine learning to learn how the environment behaves in response to an action, directly from data. In this work, we aim to learn to simulate a dynamic environment directly in pixel-space, by watching unannotated sequences offrames and their associated actions. We introduce a novel high-quality neural simulator referred to as DriveGAN that achieves controllability by disentangling different components without supervision. In addition to steering controls, it also includes controls for sampling features of a scene, such as the weather as well as the location of non player objects. Since DriveGAN is a filly differentiable simulator, it further allows for re-simulation of a given video sequence, offering an agent to drive through a recorded scene again, possibly taking different actions. We train DriveGAN on multiple datasets, including 160 hours of real-world driving data. We showcase that our approach greatly surpasses the performance of previous data-driven simulators, and allows for new key features not explored before.

关键词： Training computer vision Computational modeling Video sequences Machine learning Generative adversarial networks Controllability

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Structured Gradient-Based Interpretations via Norm-Regularized Adversarial Training

Structured Gradient-Based Interpretations via Norm-Regulariz...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Shizhan Gong Qi Dou Farzan Farnia The Chinese University of Hong Kong

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Gradient-based saliency maps have been widely used to explain the decisions of deep neural network classifiers. However, standard gradient-based interpretation maps, including the simple gradient and integrated gradient algorithms, often lack desired structures such as sparsity and connectedness in their application to real-world computer vision models. A frequently used approach to inducing sparsity structures into gradient-based saliency maps is to alter the simple gradient scheme using sparsification or norm-based regularization. A drawback with such post-processing methods is their frequently-observed significant loss in fidelity to the original simple gradient map. In this work, we propose to apply adversarial training as an inprocessing scheme to train neural networks with structured simple gradient maps. We show a duality relation between the regularized norms of the adversarial perturbations and gradient-based maps, based on which we design adversarial training loss functions promoting sparsity and group-sparsity properties in simple gradient maps. We present several numerical results to show the influence of our proposed norm-based adversarial training methods on the standard gradient-based maps of standard neural network architectures on benchmark image datasets 1 1 The paper's code is available at: https://***/peterant330/AdvGrad.

关键词： Training computer vision Perturbation methods Benchmark testing Stability analysis Robustness pattern recognition

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MobileHumanPose: Toward real-time 3D human pose estimation in mobile devices

MobileHumanPose: Toward real-time 3D human pose estimation i...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Choi, Sangbum Choi, Seokeon Kim, Changick Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9781665448994

Currently, 3D pose estimation methods are not compatible with a variety of low computational power devices because of efficiency and accuracy. In this paper, we revisit a pose estimation architecture from a viewpoint of both efficiency and accuracy. We propose a mobile-friendly model, MobileHumanPose, for real-time 3D human pose estimation from a single RGB image. This model consists of the modified MobileNetV2 backbone, a parametric activation function, and the skip concatenation inspired by U-Net. Especially, the skip concatenation structure improves accuracy by propagating richer features with negligible computational power. Our model achieves not only comparable performance to the state-of-the-art models but also has a seven times smaller model size compared to the ResNet-50 based model. In addition, our extra small model reduces inference time by 12.2ms on Galaxy S20 CPU, which is suitable for real-time 3D human pose estimation in mobile applications. The source code is available at: https://***/SangbumChoi/MobileHumanPose.

关键词： Performance evaluation Solid modeling Three-dimensional displays Computational modeling Pose estimation computer architecture Real-time systems

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Dense vision Transformer Compression with Few Samples

Dense Vision Transformer Compression with Few Samples

引用

conference on computer vision and pattern recognition (CVPR)

作者： Hanxiao Zhang Yifan Zhou Guo-Hua Wang National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Few-shot model compression aims to compress a large model into a more compact one with only a tiny training set (even without labels). Block-level pruning has recently emerged as a leading technique in achieving high accuracy and low latency in few-shot CNN compression. But, few-shot compression for vision Transformers (ViT) remains largely unexplored, which presents a new challenge. In particular, the issue of sparse compression exists in traditional CNN few-shot methods, which can only produce very few compressed models of different model sizes. This paper proposes a novel framework for few-shot ViT compression named DC-ViT. Instead of dropping the entire block, DC-ViT selectively eliminates the attention module while retaining and reusing portions of the MLP module. DC-ViT enables dense compression, which outputs numerous compressed models that densely populate the range of model complexity. DC-ViT outperforms state-of-the-art few-shot compression methods by a significant margin of 10 percentage points, along with lower latency in the compression of ViT and its variants.

关键词： Training computer vision Accuracy Computational modeling Transformers pattern recognition Complexity theory

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SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning over Traffic Events

SUTD-TrafficQA: A Question Answering Benchmark and an Effici...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Xu, Li Huang, He Liu, Jun Singapore Univ Technol & Design Informat Syst Technol & Design Singapore Singapore

ISBN: (纸本)9781665445092

Traffic event cognition and reasoning in videos is an important task that has a wide range of applications in intelligent transportation, assisted driving, and autonomous vehicles. In this paper, we create a novel dataset, SUTD-TrafficQA (Traffic Question Answering), which takes the form of video QA based on the collected 10,080 in-the-wild videos and annotated 62,535 QA pairs, for benchmarking the cognitive capability of causal inference and event understanding models in complex traffic scenarios. Specifically, we propose 6 challenging reasoning tasks corresponding to various traffic scenarios, so as to evaluate the reasoning capability over different kinds of complex yet practical traffic events. Moreover, we propose Eclipse, a novel Efficient glimpse network via dynamic inference, in order to achieve computation-efficient and reliable video reasoning. The experiments show that our method achieves superior performance while reducing the computation cost significantly. The project page: https://github. com/SUTDCV/ SUTD- TrafficQA.

关键词： Transportation Benchmark testing Knowledge discovery Cognition Computational efficiency pattern recognition Reliability

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Neural Lumigraph Rendering

Neural Lumigraph Rendering

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Kellnhofer, Petr Jebe, Lars C. Jones, Andrew Spicer, Ryan Pulli, Kari Wetzstein, Gordon Raxium Fremont CA 94539 USA Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781665445092

Novel view synthesis is a challenging and ill-posed inverse rendering problem. Neural rendering techniques have recently achieved photorealistic image quality for this task. State-of-the-art (SOTA) neural volume rendering approaches, however, are slow to train and require minutes of inference (i.e., rendering) time for high image resolutions. We adopt high-capacity neural scene representations with periodic activations for jointly optimizing an implicit surface and a radiance field of a scene supervised exclusively with posed 2D images. Our neural rendering pipeline accelerates SOTA neural volume rendering by about two orders of magnitude and our implicit surface representation is unique in allowing us to export a mesh with view-dependent texture information. Thus, like other implicit surface representations, ours is compatible with traditional graphics pipelines, enabling real-time rendering rates, while achieving unprecedented image quality compared to other surface methods. We assess the quality of our approach using existing datasets as well as high-quality 3D face data captured with a custom multi-camera rig.

关键词： Graphics Image quality Three-dimensional displays Image resolution Face recognition Pipelines Rendering (computer graphics)

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