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

共 30983 条记录，以下是4791-4800 订阅

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DriveGAN: Towards a Controllable High-Quality Neural Simulation

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

引用

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

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

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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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Prototype-Guided Saliency Feature Learning for Person Search

Prototype-Guided Saliency Feature Learning for Person Search

引用

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

作者： Kim, Hanjae Joung, Sunghun Kim, Ig-Jae Sohn, Kwanghoon Yonsei Univ Seoul South Korea Korea Inst Sci & Technol KIST Seoul South Korea

ISBN: (纸本)9781665445092

Existing person search methods integrate person detection and re-identification (re-ID) module into a unified system. Though promising results have been achieved, the misalignment problem, which commonly occurs in person search, limits the discriminative feature representation for re-ID. To overcome this limitation, we introduce a novel framework to learn the discriminative representation by utilizing prototype in OIM loss. Unlike conventional methods using prototype as a representation of person identity, we utilize it as guidance to allow the attention network to consistently highlight multiple instances across different poses. Moreover, we propose a new prototype update scheme with adaptive momentum to increase the discriminative ability across different instances. Extensive ablation experiments demonstrate that our method can significantly enhance the feature discriminative power, outperforming the state-of-the-art results on two person search benchmarks including CUHK-SYSU and PRW.

关键词： computer vision Adaptation models Annotations Computational modeling Prototypes Benchmark testing Search problems

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UnsupervisedR&R: Unsupervised Point Cloud Registration via Differentiable Rendering

UnsupervisedR&R: Unsupervised Point Cloud Registration via D...

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

作者： El Banani, Mohamed Gao, Luya Johnson, Justin Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781665445092

Aligning partial views of a scene into a single whole is essential to understanding one's environment and is a key component of numerous robotics tasks such as SLAM and SfM. Recent approaches have proposed end-to-end systems that can outperform traditional methods by leveraging pose supervision. However, with the rising prevalence of cameras with depth sensors, we can expect a new stream of raw RGB-D data without the annotations needed for supervision. We propose UnsupervisedR&R: an end-to-end unsupervised approach to learning point cloud registration from raw RGB-D video. The key idea is to leverage differentiable alignment and rendering to enforce photometric and geometric consistency between frames. We evaluate our approach on indoor scene datasets and find that we outperform existing traditional approaches with classical and learned descriptors while being competitive with supervised geometric point cloud registration approaches.

关键词： Training Simultaneous localization and mapping Robot vision systems Feature extraction Rendering (computer graphics) Cameras Sensors

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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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LPSNet: A lightweight solution for fast panoptic segmentation

LPSNet: A lightweight solution for fast panoptic segmentatio...

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

作者： Hong, Weixiang Guo, Qingpei Zhang, Wei Chen, Jingdong Chu, Wei Ant Financial Serv Grp Hangzhou Peoples R China

ISBN: (纸本)9781665445092

Panoptic segmentation is a challenging task aiming to simultaneously segment objects (things) at instance level and background contents (stuff) at semantic level. Existing methods mostly utilize a two-stage detection network to attain instance segmentation results, and a fully convolutional network to produce a semantic segmentation prediction. Post-processing or additional modules are required to handle the conflicts between the outputs from these two nets, which makes such methods suffer from low efficiency, heavy memory consumption and complicated implementation. To simplify the pipeline and decrease computation/memory cost, we propose an one-stage approach called Lightweight Panoptic Segmentation Network (LPSNet), which does not involve a proposal, anchor or mask head. Instead, we predict a bounding box and semantic category at each pixel upon the feature map produced by an augmented feature pyramid, and design a parameter-free head to merge the per-pixel bounding box and semantic prediction into panoptic segmentation output. Our LPSNet is not only efficient in computation and memory, but also accurate in panoptic segmentation. Comprehensive experiments on COCO, Cityscapes and Mapillary Vistas datasets demonstrate the promising effectiveness and efficiency of the proposed LP-SNet.

关键词： computer vision Costs Semantics Memory management Pipelines Object detection Real-time systems

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