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检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"

共 22907 条记录，以下是4591-4600 订阅

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BABEL: Bodies, Action and Behavior with English Labels

BABEL: Bodies, Action and Behavior with English Labels

引用

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

作者： Punnakkal, Abhinanda R. Chandrasekaran, Arjun Athanasiou, Nikos Quiros-Ramirez, Alejandra Black, Michael J. Max Planck Inst Intelligent Syst Tubingen Germany Univ Konstanz Constance Germany

ISBN: (数字)9781665445092

ISBN: (纸本)9781665445092

Understanding the semantics of human movement - the what, how and why of the movement - is an important problem that requires datasets of human actions with semantic labels. Existing datasets take one of two approaches. Large-scale video datasets contain many action labels but do not contain ground-truth 3D human motion. Alternatively, motion-capture (mocap) datasets have precise body motions but are limited to a small number of actions. To address this, we present BABEL, a large dataset with language labels describing the actions being performed in mocap sequences. BABEL consists of language labels for over 43 hours of mocap sequences from AMASS, containing over 250 unique actions. Each action label in BABEL is precisely aligned with the duration of the corresponding action in the mocap sequence. BABELalso allows overlap of multiple actions, that may each span different durations. This results in a total of over 66000 action segments. The dense annotations can be leveraged for tasks like action recognition, temporal localization, motion synthesis, etc. To demonstrate the value of BABEL as a benchmark, we evaluate the performance of models on 3D action recognition. We demonstrate that BABEL poses interesting learning challenges that are applicable to real-world scenarios, and can serve as a useful benchmark for progress in 3D action recognition.

关键词： Location awareness Solid modeling computer vision Three-dimensional displays Motion segmentation Semantics Benchmark testing

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Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place recognition

Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descript...

引用

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

作者： Hausler, Stephen Garg, Sourav Xu, Ming Milford, Michael Fischer, Tobias Queensland Univ Technol QUT Ctr Robot Brisbane Qld Australia

ISBN: (纸本)9781665445092

Visual Place recognition is a challenging task for robotics and autonomous systems, which must deal with the twin problems of appearance and viewpoint change in an always changing world. This paper introduces Patch-NetVLAD, which provides a novel formulation for combining the advantages of both local and global descriptor methods by deriving patch-level features from NetVLAD residuals. Unlike the fixed spatial neighborhood regime of existing local keypoint features, our method enables aggregation and matching of deep-learned local features defined over the feature-space grid. We further introduce a multi-scale fusion of patch features that have complementary scales (i.e. patch sizes) via an integral feature space and show that the fused features are highly invariant to both condition (season, structure, and illumination) and viewpoint (translation and rotation) changes. Patch-NetVLAD achieves state-of-the-art visual place recognition results in computationally limited scenarios, validated on a range of challenging real-world datasets, including winning the Facebook Mapillary Visual Place recognition Challenge at ECCV2020. It is also adaptable to user requirements, with a speed-optimised version operating over an order of magnitude faster than the state-of-the-art. By combining superior performance with improved computational efficiency in a configurable framework, Patch-NetVLAD is well suited to enhance both stand-alone place recognition capabilities and the overall performance of SLAM systems.

关键词： Visualization computer vision Simultaneous localization and mapping Social networking (online) Autonomous systems Face recognition Lighting

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Learned Initializations for Optimizing Coordinate-Based Neural Representations

Learned Initializations for Optimizing Coordinate-Based Neur...

引用

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

作者： Tancik, Matthew Mildenhall, Ben Wang, Terrance Schmidt, Divi Srinivasan, Pratul P. Barron, Jonathan T. Ng, Ren Univ Calif Berkeley Berkeley CA 94720 USA Google Res Mountain View CA USA

ISBN: (纸本)9781665445092

Coordinate-based neural representations have shown significant promise as an alternative to discrete, array-based representations for complex low dimensional signals. However, optimizing a coordinate-based network from randomly initialized weights for each new signal is inefficient. We propose applying standard meta-learning algorithms to learn the initial weight parameters for these fully-connected networks based on the underlying class of signals being represented (e.g., images of faces or 3D models of chairs). Despite requiring only a minor change in implementation, using these learned initial weights enables faster convergence during optimization and can serve as a strong prior over the signal class being modeled, resulting in better generalization when only partial observations of a given signal are available. We explore these benefits across a variety of tasks, including representing 2D images, reconstructing CT scans, and recovering 3D shapes and scenes from 2D image observations.

关键词： Solid modeling Three-dimensional displays Limiting Shape Trajectory pattern recognition Task analysis

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Control Architecture for Multi-Step pattern recognition Algorithms in Distributed Industrial Systems

Control Architecture for Multi-Step Pattern Recognition Algo...

引用

2023 ieee International conference on Electrical, computer and Energy Technologies, ICECET 2023

作者： Moseyko, Ekaterina Granichin, Oleg Saint Petersburg State University St. Petersburg Russia Institute for Problem in Mechanical Engineering St. Petersburg Russia

ISBN: (纸本)9798350327816

The pipeline based software for digital twins was invented. Several gigabit ethernet cameras transmitting raw data with a resolution of 2048 ∗ 2680 at a frequency of 16fps connected to high-speed networks are used to monitor production process. The main feature of the proposed software is the ability to process live input video streams with a many complex algorithms implemented on different languages like C, C++ and Python. The special library was developed for integrate algorithms written on a different languages. The total duration of the sequence of algorithms is greater than the time of observation of each object in the system. To avoid excessive copying of large amounts of data, the proposed architecture uses shared memory. Specially organized storage of input images allows efficient use of the GPU. The pipeline is accompanied by a control system based on a human-oriented protocol, which allows you to connect, disconnect and control algorithms as nodes of a directed non looped process graph. The change of the process graph can happen on the fly. The pipeline manager operates on an open protocol, which combines the capabilities of a text API for controlling capture from cameras and a REST API for convenient control over the network. © 2023 ieee.

关键词： computer vision Control Systems Software Engineering

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GAIA: A Transfer Learning System of Object Detection that Fits Your Needs

GAIA: A Transfer Learning System of Object Detection that Fi...

引用

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

作者： Bu, Xingyuan Peng, Junran Yan, Junjie Tan, Tieniu Zhang, Zhaoxiang Univ Chinese Acad Sci Beijing Peoples R China Beijing Inst Technol Beijing Peoples R China CASIA Ctr Res Intelligent Percept & Comp Beijing Peoples R China SenseTime Grp Ltd Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Transfer learning with pre-training on large-scale datasets has played an increasingly significant role in computer vision and natural language processing recently. However, as there exist numerous application scenarios that have distinctive demands such as certain latency constraints and specialized data distributions, it is prohibitively expensive to take advantage of large-scale pre-training for per-task requirements. In this paper, we focus on the area of object detection and present a transfer learning system named GAIA, which could automatically and efficiently give birth to customized solutions according to heterogeneous downstream needs. GAIA is capable of providing powerful pre-trained weights, selecting models that conform to downstream demands such as latency constraints and specified data domains, and collecting relevant data for practitioners who have very few datapoints for their tasks. With GAIA, we achieve promising results on COCO, Objects365, Open Images, Caltech, CityPersons, and UODB which is a collection of datasets including KITTI, VOC, WiderFace, DOTA, Clipart, Comic, and more. Taking COCO as an example, GAIA is able to efficiently produce models covering a wide range of latency from 16ms to 53ms, and yields AP from 38.2 to 46.5 without whistles and bells.

关键词： computer vision Transfer learning Object detection computer architecture Natural language processing Data models pattern recognition

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Gradient-based Algorithms for Machine Teaching

Gradient-based Algorithms for Machine Teaching

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

作者： Wang, Pei Nagrecha, Kabir Vasconcelos, Nuno Univ Calif San Diego San Diego CA 92093 USA

ISBN: (纸本)9781665445092

The problem of machine teaching is considered. A new formulation is proposed under the assumption of an optimal student, where optimality is defined in the usual machine learning sense of empirical risk minimization. This is a sensible assumption for machine learning students and for human students in crowdsourcing platforms, who tend to perform at least as well as machine learning systems. It is shown that, if allowed unbounded effort, the optimal student always learns the optimal predictor for a classification task. Hence, the role of the optimal teacher is to select the teaching set that minimizes student effort. This is formulated as a problem of functional optimization where, at each teaching iteration, the teacher seeks to align the steepest descent directions of the risk of (1) the teaching set and (2) entire example population. The optimal teacher, denoted MaxGrad, is then shown to maximize the gradient of the risk on the set of new examples selected per iteration. MaxGrad teaching algorithms are finally provided for both binary and multiclass tasks, and shown to have some similarities with boosting algorithms. Experimental evaluations demonstrate the effectiveness of MaxGrad, which outperforms previous algorithms on the classification task, for both machine learning and human students from MTurk, by a substantial margin.

关键词： Training Machine learning algorithms Education Sociology Classification algorithms pattern recognition Risk management

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Deep Convolutional Sparse Coding Networks for Interpretable Image Fusion

Deep Convolutional Sparse Coding Networks for Interpretable ...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Zixiang Zhao Jiangshe Zhang Haowen Bai Yicheng Wang Yukun Cui Lilun Deng Kai Sun Chunxia Zhang Junmin Liu Shuang Xu Xi’an Jiaotong University Computer Vision Lab ETH Zurich The University of Melbourne Research and Development Institute of Northwestern Polytechnical University in Shenzhen Northwestern Polytechnical University

Image fusion is a significant problem in many fields including digital photography, computational imaging and remote sensing, to name but a few. Recently, deep learning has emerged as an important tool for image fusion. This paper presents CSCFuse, which contains three deep convolutional sparse coding (CSC) networks for three kinds of image fusion tasks (i.e., infrared and visible image fusion, multi-exposure image fusion, and multi-spectral image fusion). The CSC model and the iterative shrinkage and thresholding algorithm are generalized into dictionary convolution units. As a result, all hyper-parameters are learned from data. Our extensive experiments and comprehensive comparisons reveal the superiority of CSCF use with regard to quantitative evaluation and visual inspection.

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Sparse Multimodal vision Transformer for Weakly Supervised Semantic Segmentation

Sparse Multimodal Vision Transformer for Weakly Supervised S...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Joëlle Hanna Michael Mommert Damian Borth AIML Lab School of Computer Science University of St.Gallen

vision Transformers have proven their versatility and utility for complex computer vision tasks, such as land cover segmentation in remote sensing applications. While performing on par or even outperforming other methods like Convolutional Neural Networks (CNNs), Transformers tend to require even larger datasets with fine-grained annotations (e.g., pixel-level labels for land cover segmentation). To overcome this limitation, we propose a weakly-supervised vision Transformer that leverages image-level labels to learn a semantic segmentation task to reduce the human annotation load. We achieve this by slightly modifying the architecture of the vision Transformer through the use of gating units in each attention head to enforce sparsity during training and thereby retaining only the most meaningful heads. This allows us to directly infer pixel-level labels from image-level labels by post-processing the un-pruned attention heads of the model and refining our predictions by iteratively training a segmentation model with high fidelity. Training and evaluation on the DFC2020 dataset show that our method 1 not only generates high-quality segmentation masks using image-level labels, but also performs on par with fully-supervised training relying on pixel-level labels. Finally, our results show that our method is able to perform weakly-supervised semantic segmentation even on small-scale datasets.

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Mutual CRF-GNN for Few-shot Learning

Mutual CRF-GNN for Few-shot Learning

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

作者： Tang, Shixiang Chen, Dapeng Bai, Lei Liu, Kaijian Ge, Yixiao Ouyang, Wanli Univ Sydney SenseTime Comp Vis Grp Camperdown NSW Australia Sensetime Grp Ltd Hong Kong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Graph-neural-networks (GNN) is a rising trend for few-shot learning. A critical component in GNN is the affinity. Typically, affinity in GNN is mainly computed in the feature space, e.g., pairwise features, and does not take fully advantage of semantic labels associated to these features. In this paper, we propose a novel Mutual CRF-GNN (MCGN). In this MCGN, the labels and features of support data are used by the CRF for inferring GNN affinities in a principled and probabilistic way. Specifically, we construct a Conditional Random Field (CRF) conditioned on labels and features of support data to infer a affinity in the label space. Such affinity is fed to the GNN as the node-wise affinity. GNN and CRF mutually contributes to each other in MCGN. For GNN, CRF provides valuable affinity information. For CRF, GNN provides better features for inferring affinity. Experimental results show that our approach outperforms state-of-the-arts on datasets miniImageNet, tieredImageNet, and CIFAR-FS on both 5-way 1-shot and 5-way 5-shot settings.

关键词： computer vision Computational modeling Semantics Benchmark testing Probabilistic logic Market research pattern recognition

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Semi-supervised Semantic Segmentation with Directional Context-aware Consistency

Semi-supervised Semantic Segmentation with Directional Conte...

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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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