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检索条件"任意字段=2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003"

共 6678 条记录，以下是491-500 订阅

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Coupling vision and Proprioception for Navigation of Legged Robots

Coupling Vision and Proprioception for Navigation of Legged ...

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

作者： Fu, Zipeng Kumar, Ashish Agarwal, Ananye Qi, Haozhi Malik, Jitendra Pathak, Deepak Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665487399

We exploit the complementary strengths of vision and proprioception to develop a point-goal navigation system for legged robots, called VP-Nav. Legged systems are capable of traversing more complex terrain than wheeled robots, but to fully utilize this capability, we need a high-level path planner in the navigation system to be aware of the walking capabilities of the low-level locomotion policy in varying environments. We achieve this by using proprioceptive feedback to ensure the safety of the planned path by sensing unexpected obstacles like glass walls, terrain properties like slipperiness or softness of the ground and robot properties like extra payload that are likely missed by vision. The navigation system uses onboard cameras to generate an occupancy map and a corresponding cost map to reach the goal. A fast marching planner then generates a target path. A velocity command generator takes this as input to generate the desired velocity for the walking policy. A safety advisor module adds sensed unexpected obstacles to the occupancy map and environment-determined speed limits to the velocity command generator. We show superior performance compared to wheeled robot baselines, and ablation studies which have disjoint highlevel planning and low-level control. We also show the real-world deployment of VP-Nav on a quadruped robot with onboard sensors and computation. Videos at https://***

关键词： Legged locomotion Navigation Robot vision systems Robot sensing systems Generators Sensors Safety

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Goal-driven Self-Attentive Recurrent Networks for Trajectory Prediction

Goal-driven Self-Attentive Recurrent Networks for Trajectory...

引用

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

作者： Chiara, Luigi Filippo Coscia, Pasquale Das, Sourav Calderara, Simone Cucchiara, Rita Ballan, Lamberto Univ Padua Padua Italy Univ Modena & Reggio Emilia Modena Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Human trajectory forecasting is a key component of autonomous vehicles, social-aware robots and advanced video-surveillance applications. This challenging task typically requires knowledge about past motion, the environment and likely destination areas. In this context, multimodality is a fundamental aspect and its effective modeling can be beneficial to any architecture. Inferring accurate trajectories is nevertheless challenging, due to the inherently uncertain nature of the future. To overcome these difficulties, recent models use different inputs and propose to model human intentions using complex fusion mechanisms. In this respect, we propose a lightweight attention-based recurrent backbone that acts solely on past observed positions. Although this backbone already provides promising results, we demonstrate that its prediction accuracy can be improved considerably when combined with a scene-aware goal-estimation module. To this end, we employ a common goal module, based on a U-Net architecture, which additionally extracts semantic information to predict scene-compliant destinations. We conduct extensive experiments on publicly-available datasets (i.e. SDD, inD, ETH/UCY) and show that our approach performs on par with state-of-the-art techniques while reducing model complexity.

关键词： Measurement Semantics Stochastic processes computer architecture Trajectory Data mining Task analysis

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Remote Heart Rate Estimation by Signal Quality Attention Network

Remote Heart Rate Estimation by Signal Quality Attention Net...

引用

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

作者： Gao, Haoyuan Wu, Xiaopei Geng, Jidong Lv, Yang Anhui Univ Sch Comp Sci & Technol Hefei Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Heart rate estimation is very important for heart health monitoring. As a non-invasive optical technology, remote photoplethysmography (rPPG) has the advantages of non-contact, portability and low-price. However, motion and noise artifacts bring additional uncertainty to the results of heart rate estimation. Based on the signal quality assessment method, we propose a new remote heart estimation algorithm by signal quality attention mechanism and long short-term memory (LSTM) networks. The model consists of three parts: firstly, an LSTM network is used to estimate the heart rate sampling point by sampling point;secondly, a similar LSTM network predicts the signal quality;finally, an attention-based model uses the heart rates and quality scores predicted above to calculate the average heart rate of a period of time. The model allocates higher weights to the reliable heart rates estimated from high-quality signals, meanwhile, ignores unreliable results estimated from low-quality signals. Experiments show that LSTM with attention mechanism accurately estimates heart rate from corruption rPPG signal and it performs well on cross-subject tasks and cross-dataset tasks. The results also demonstrate that the scores predicted by the signal quality model is valuable to extract reliable heart rate.

关键词： Heart rate Training Uncertainty computer network reliability Estimation Predictive models Prediction algorithms

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Guided Deep Metric Learning

Guided Deep Metric Learning

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

作者： Gonzalez-Zapata, Jorge Reyes-Amezcua, Ivan Flores-Araiza, Daniel Mendez-Ruiz, Mauricio Ochoa-Ruiz, Gilberto Mendez-Vazquez, Andres CINVESTAV Unidad Guadalajara Zapopan Mexico Tecnol Monterrey Sch Engn & Sci Monterrey Mexico

ISBN: (纸本)9781665487399

Deep Metric Learning (DML) methods have been proven relevant for visual similarity learning. However, they sometimes lack generalization properties because they are trained often using an inappropriate sample selection strategy or due to the difficulty of the dataset caused by a distributional shift in the data. These represent a significant drawback when attempting to learn the underlying data manifold. Therefore, there is a pressing need to develop better ways of obtaining generalization and representation of the underlying manifold. In this paper, we propose a novel approach to DML that we call Guided Deep Metric Learning, a novel architecture oriented to learning more compact clusters, improving generalization under distributional shifts in DML. This novel architecture consists of two independent models: A multi-branch master model, inspired from a Few-Shot Learning (FSL) perspective, generates a reduced hypothesis space based on prior knowledge from labeled data, which guides or regularizes the decision boundary of a student model during training under an offline knowledge distillation scheme. Experiments have shown that the proposed method is capable of a better manifold generalization and representation to up to 40% improvement (Recall@1, CIFAR10), using guidelines suggested by Musgrave et al. to perform a more fair and realistic comparison, which is currently absent in the literature.

关键词： Measurement Manifolds Training Analytical models Adaptation models Visualization computer architecture

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Multi-Head Distillation for Continual Unsupervised Domain Adaptation in Semantic Segmentation

Multi-Head Distillation for Continual Unsupervised Domain Ad...

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

作者： Saporta, Antoine Douillard, Arthur Vu, Tuan-Hung Perez, Patrick Cord, Matthieu Sorbonne Univ Paris France valeo ai Paris France Heuritech Paris France

ISBN: (纸本)9781665487399

Unsupervised Domain Adaptation (UDA) is a transfer learning task which aims at training on an unlabeled target domain by leveraging a labeled source domain. Beyond the traditional scope of UDA with a single source domain and a single target domain, real-world perception systems face a variety of scenarios to handle, from varying lighting conditions to many cities around the world. In this context, UDAs with several domains increase the challenges with the addition of distribution shifts within the different target domains. This work focuses on a novel framework for learning UDA, continuous UDA, in which models operate on multiple target domains discovered sequentially, without access to previous target domains. We propose MuHDi, for Multi-Head Distillation, a method that solves the catastrophic forgetting problem, inherent in continual learning tasks. MuHDi performs distillation at multiple levels from the previous model as well as an auxiliary target-specialist segmentation head. We report both extensive ablation and experiments on challenging multi-target UDA semantic segmentation benchmarks to validate the proposed learning scheme and architecture.

关键词： Training Adaptation models Urban areas Semantics Transfer learning computer architecture Benchmark testing

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An Once-for-All Budgeted Pruning Framework for ConvNets Considering Input Resolution

An Once-for-All Budgeted Pruning Framework for ConvNets Cons...

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

作者： Sun, Wenyu Cao, Jian Xu, Pengtao Liu, Xiangcheng Zhang, Yuan Wang, Yuan Peking Univ Sch Software & Microelect Beijing Peoples R China Peking Univ Sch Integrated Circuits Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We propose an efficient once-for-all budgeted pruning framework (OFARPruning) to find many compact network architectures close to winner tickets in the early training stage considering the effect of input resolution during the pruning process. In architecture searching stage, we measure the similarity of the pruning mask to get high-quality network architecture with low energy and time consumption. After searching stage, our proposed method randomly sample the compact architectures with different pruning rates and input resolution to achieve joint optimization. Ultimately, we can obtain a cohort of compact networks adaptive to various resolution to meet dynamic FLOPs constraints on different edge devices with only once training. The experiments based on image classification and object detection show that OFARPruning has a higher accuracy than the once-for-all compression methods such as US-Net and MutualNet (1-2% better with less FLOPs), and achieve competitive performance as the conventional pruning methods (72.6% vs. 70.5% on MobileNetv2 under 170 MFLOPs) with much higher efficiency.

关键词： Training Image resolution Adaptive systems Image edge detection Energy resolution Object detection computer architecture

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Unsupervised Multi-View Gaze Representation Learning

Unsupervised Multi-View Gaze Representation Learning

引用

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

作者： Gideon, John Su, Shan Stent, Simon Toyota Res Inst Cambridge MA 02139 USA Univ Penn Philadelphia PA 19104 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We present a method for unsupervised gaze representation learning from multiple synchronized views of a person's face. The key assumption is that images of the same eye captured from different viewpoints differ in certain respects while remaining similar in others. Specifically, the absolute gaze and absolute head pose of the same subject should be different from different viewpoints, while appearance characteristics and gaze angle relative to the head coordinate frame should remain constant. To leverage this, we adopt a cross-encoder learning framework, in which our encoding space consists of head pose, relative eye gaze, eye appearance and other common features. Image pairs which are assumed to have matching subsets of features should be able to swap those subsets among themselves without any loss of information, computed by decoding the mixed features back into images and measuring reconstruction loss. We show that by applying these assumptions to an unlabelled multi-view video dataset, we can generate more powerful representations than a standard gaze cross-encoder for few-shot gaze estimation. Furthermore, we introduce a new feature-mixing method which results in higher performance, faster training, improved testing flexibility with multiple views, and added interpretability with learned confidence.

关键词： Representation learning Training Head Face recognition Estimation Loss measurement Synchronization

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Analysis of Temporal Tensor Datasets on Product Grassmann Manifold

Analysis of Temporal Tensor Datasets on Product Grassmann Ma...

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

作者： Batalo, Bojan Souza, Lincon S. Gatto, Bernardo B. Sogi, Naoya Fukui, Kazuhiro Univ Tsukuba Tsukuba Ibaraki Japan AIST Tokyo Japan AIST Tsukuba Ibaraki Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Growing abundance of multi-dimensional data creates a need for efficient data exploration and analysis. In this paper, we address this need by tackling the task of tensor dataset visualization and clustering, as tensors are a natural form of multi-dimensional data. Previous work has shown that representing individual tensor modes via respective linear subspaces and unifying them on the product Grassmann manifold (PGM) is an effective and memory-efficient way of representation. However, such representation may lead to loss of valuable temporal information. To address this issue, we model temporal tensor modes with a Hankel-like matrix, preserving sequence information and encoding it with a linear subspace, fully compatible with PGM. Unifying regular tensor modes and Hankel-like representation of regular tensor modes then enriches representation on the PGM, with minimal increase in computational complexity. By relying on geodesic distance on the manifold, we facilitate analysis of multi-dimensional datasets in two ways: 1) by enabling straightforward visualizations using algorithms such as t-SNE;and 2) by fostering clustering of data using distance- or similarity-based methods such as spectral clustering. We evaluate our approach on hand gesture and action recognition datasets as exemplars of temporal tensor datasets.

关键词： Manifolds Measurement Geometry Tensors Data visualization pattern recognition Gyroscopes

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GLaMa: Joint Spatial and Frequency Loss for General Image Inpainting

GLaMa: Joint Spatial and Frequency Loss for General Image In...

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

作者： Lu, Zeyu Jiang, Junjun Huang, Junqin Wu, Gang Liu, Xianming Harbin Inst Technol Harbin Peoples R China Beihang Univ Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The purpose of image inpainting is to recover scratches and damaged areas using context information from remaining parts. In recent years, thanks to the resurgence of convolutional neural networks (CNNs), image inpainting task has made great breakthroughs. However, most of the work consider insufficient types of mask, and their performance will drop dramatically when encountering unseen masks. To combat these challenges, we propose a simple yet general method to solve this problem based on the LaMa image inpainting framework [35], dubbed GLaMa. Our proposed GLaMa can better capture different types of missing information by using more types of masks. By incorporating more degraded images in the training phase, we can expect to enhance the robustness of the model with respect to various masks. In order to yield more reasonable results, we further introduce a frequency-based loss in addition to the traditional spatial reconstruction loss and adversarial loss. In particular, we introduce an effective reconstruction loss both in the spatial and frequency domain to reduce the chessboard effect and ripples in the reconstructed image. Extensive experiments demonstrate that our method can boost the performance over the original LaMa method for each type of mask on FFHQ [18], ImageNet [7], Places2 [42] and WikiArt [28] dataset. The proposed GLaMa was ranked first in terms of PSNR, LPIPS [39] and SSIM [34] in the NTIRE 2022 Image Inpainting Challenge Track 1 Unsupervised [27].

关键词： Training computer vision Frequency-domain analysis conferences computer architecture Robustness pattern recognition

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Exploiting Distortion Information for Multi-degraded Image Restoration

Exploiting Distortion Information for Multi-degraded Image R...

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

作者： Shin, Wooksu Ahn, Namhyuk Moon, Jeong-Hyeon Sohn, Kyung-Ah Ajou Univ Dept Artificial Intelligence Suwon South Korea NAVER WEBTOON AI Seongnam Si South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In recent years, tremendous studies have been performed on the image distortion restoration task and deep learning-based methods have shown prominent performance improvement. However, assuming only a single distortion to an image may not be applicable in many real-world scenarios. To mitigate the issue, some studies have proposed multi-distortion datasets by applying the corruptions sequentially or spatially. In this work, we integrate the two perspectives on the multi-distortion nature and propose a new dataset that is a holistic multi-distortion dataset. To restore the multi-distortion effectively, we introduce a distortion information-guided restoration network, which exploits the conditional distortion information when reconstructing a given image. To do that, our framework first predicts the distortion type and their strength and delivers these to the restoration module. In our experiments, we show that the proposed model exceeds the others and we also demonstrate that any backbone network benefits from receiving the distortion information as prior knowledge.

关键词： Training Knowledge engineering Image recognition Fuses Supervised learning Distortion Image restoration

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