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

共 22908 条记录，以下是4381-4390 订阅

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Blocks-World Cameras

Blocks-World Cameras

引用

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

作者： Lee, Jongho Gupta, Mohit Univ Wisconsin Madison Madison WI 53715 USA

ISBN: (纸本)9781665445092

For several vision and robotics applications, 3D geometry of man-made environments such as indoor scenes can be represented with a small number of dominant planes. However, conventional 3D vision techniques typically first acquire dense 3D point clouds before estimating the compact piece-wise planar representations (e.g., by plane-fitting). This approach is costly, both in terms of acquisition and computational requirements, and potentially unreliable due to noisy point clouds. We propose Blocks-World Cameras, a class of imaging systems which directly recover dominant planes of piece-wise planar scenes (Blocks-World), without requiring point clouds. The Blocks-World Cameras are based on a structured-light system projecting a single pattern with a sparse set of cross-shaped features. We develop a novel geometric algorithm for recovering scene planes without explicit correspondence matching, thereby avoiding computationally intensive search or optimization routines. The proposed approach has low device and computational complexity, and requires capturing only one or two images. We demonstrate highly efficient and precise planar-scene sensing with simulations and real experiments, across various imaging conditions, including defocus blur, large lighting variations, ambient illumination, and scene clutter.

关键词： Geometry Three-dimensional displays Lighting Cameras Robot sensing systems Sensors pattern recognition

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Fast and Accurate Model Scaling

Fast and Accurate Model Scaling

引用

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

作者： Dollar, Piotr Singh, Mannat Girshick, Ross Facebook AI Res FAIR Menlo Pk CA 94025 USA

ISBN: (纸本)9781665445092

In this work we analyze strategies for convolutional neural network scaling;that is, the process of scaling a base convolutional network to endow it with greater computational complexity and consequently representational power. Example scaling strategies may include increasing model width, depth, resolution, etc. While various scaling strategies exist, their tradeoffs are not fully understood. Existing analysis typically focuses on the interplay of accuracy and flops (floating point operations). Yet, as we demonstrate, various scaling strategies affect model parameters, activations, and consequently actual runtime quite differently. In our experiments we show the surprising result that numerous scaling strategies yield networks with similar accuracy but with widely varying properties. This leads us to propose a simple fast compound scaling strategy that encourages primarily scaling model width, while scaling depth and resolution to a lesser extent. Unlike currently popular scaling strategies, which result in about O(s) increase in model activation w.r.t. scaling flops by a factor of s, the proposed fast compound scaling results in close to O(root s) increase in activations, while achieving excellent accuracy. Fewer activations leads to speedups on modern memory-bandwidth limited hardware (e.g., GPUs). More generally, we hope this work provides a framework for analyzing scaling strategies under various computational constraints.

关键词： computer vision Runtime Computational modeling Hardware pattern recognition Compounds Convolutional neural networks

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DELTA: Decoupling Long-Tailed Online Continual Learning

DELTA: Decoupling Long-Tailed Online Continual Learning

引用

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

作者： Siddeshwar Raghavan Jiangpeng He Fengqing Zhu School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

A significant challenge in achieving ubiquitous Artificial Intelligence is the limited ability of models to rapidly learn new information in real-world scenarios where data follows long-tailed distributions, all while avoiding forgetting previously acquired knowledge. In this work, we study the under-explored problem of Long-Tailed Online Continual Learning (LTOCL), which aims to learn new tasks from sequentially arriving class-imbalanced data streams. Each data is observed only once for training without knowing the task data distribution. We present DELTA, a decoupled learning approach designed to enhance learning representations and address the substantial imbalance in LTOCL. We enhance the learning process by adapting supervised contrastive learning to attract similar samples and repel dissimilar (out-of-class) samples. Further, by balancing gradients during training using an equalization loss, DELTA significantly enhances learning outcomes and successfully mitigates catastrophic forgetting. Through extensive evaluation, we demonstrate that DELTA improves the capacity for incremental learning, surpassing existing OCL methods. Our results suggest considerable promise for applying OCL in real-world applications. Code is available online 1

关键词： Continuing education Training computer vision Incremental learning conferences Contrastive learning Data models

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Temporally-Weighted Hierarchical Clustering for Unsupervised Action Segmentation

Temporally-Weighted Hierarchical Clustering for Unsupervised...

引用

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

作者： Sarfraz, M. Saquib Murray, Naila Sharma, Vivek Diba, Ali van Gool, Luc Stiefelhagen, Rainer Karlsruhe Inst Technol Karlsruhe Germany Facebook AI Res Menlo Pk CA USA MIT Cambridge MA 02139 USA Harvard Med Sch Boston MA 02115 USA Katholieke Univ Leuven Leuven Belgium Swiss Fed Inst Technol Zurich Switzerland Daimler TSS Ulm Germany

ISBN: (纸本)9781665445092

Action segmentation refers to inferring boundaries of semantically consistent visual concepts in videos and is an important requirement for many video understanding tasks. For this and other video understanding tasks, supervised approaches have achieved encouraging performance but require a high volume of detailed frame-level annotations. We present a fully automatic and unsupervised approach for segmenting actions in a video that does not require any training. Our proposal is an effective temporally-weighted hierarchical clustering algorithm that can group semantically consistent frames of the video. Our main finding is that representing a video with a 1-nearest neighbor graph by taking into account the time progression is sufficient to form semantically and temporally consistent clusters of frames where each cluster may represent some action in the video. Additionally, we establish strong unsupervised baselines for action segmentation and show significant performance improvements over published unsupervised methods on five challenging action segmentation datasets. Our code is available.

关键词： Training Measurement Visualization computer vision Codes Spatiotemporal phenomena pattern recognition

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Learning Tensor Low-Rank Prior for Hyperspectral Image Reconstruction

Learning Tensor Low-Rank Prior for Hyperspectral Image Recon...

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

作者： Zhang, Shipeng Wang, Lizhi Zhang, Lei Huang, Hua Xi An Jiao Tong Univ Xian Peoples R China Beijing Inst Technol Beijing Peoples R China Beijing Normal Univ Beijing Peoples R China

ISBN: (纸本)9781665445092

Snapshot hyperspectral imaging has been developed to capture the spectral information of dynamic scenes. In this paper, we propose a deep neural network by learning the tensor low-rank prior of hyperspectral images (HSI) in the feature domain to promote the reconstruction quality. Our method is inspired by the canonical-polyadic (CP) decomposition theory, where a low-rank tensor can be expressed as a weight summation of several rank-1 component tensors. Specifically, we first learn the tensor low-rank prior of the image features with two steps: (a) we generate rank-1 tensors with discriminative components to collect the contextual information from both spatial and channel dimensions of the image features;(b) we aggregate those rank-1 tensors into a low-rank tensor as a 3D attention map to exploit the global correlation and refine the image features. Then, we integrate the learned tensor low-rank prior into an iterative optimization algorithm to obtain an end-to-end HSI reconstruction. Experiments on both synthetic and real data demonstrate the superiority of our method.

关键词： Deep learning computer vision Tensors Three-dimensional displays Correlation Iterative algorithms pattern recognition

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Localised-NeRF: Specular Highlights and Colour Gradient Localising in NeRF

Localised-NeRF: Specular Highlights and Colour Gradient Loca...

引用

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

作者： Dharmendra Selvaratnam Dena Bazazian Faculty of Science and Engineering School of Engineering Computing and Mathematics (SECaM) University of Plymouth

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neural Radiance Field (NeRF) based systems predominantly operate within the RGB (Red, Green, and Blue) space; however, the distinctive capability of the HSV (Hue, Saturation, and Value) space to discern between specular and diffuse regions is seldom utilised in the literature. We introduce Localised-NeRF, which projects the queried pixel point onto multiple training images to obtain a multi-view feature representation on HSV space and gradient space to obtain important features that can be used to synthesise novel view colour. This integration is pivotal in identifying specular highlights within scenes, thereby enriching the model’s understanding of specular changes as the viewing angle alters. Our proposed Localised-NeRF model uses an attention-driven approach that not only maintains local view direction consistency but also leverages image-based features namely the HSV colour space and colour gradients. These features serve as effective indirect priors for both the training and testing phases to predict the diffuse and specular colour. Our model exhibits competitive performance with prior NeRF-based models, as demonstrated on the Shiny Blender and Synthetic datasets. The code of Localised-NeRF is publicly available 1 .

关键词： Training computer vision Visualization Image color analysis Neural radiance field Rendering (computer graphics) Mathematical models

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Complementary Relation Contrastive Distillation

Complementary Relation Contrastive Distillation

引用

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

作者： Zhu, Jinguo Tang, Shixiang Chen, Dapeng Yu, Shijie Liu, Yakun Rong, Mingzhe Yang, Aijun Wang, Xiaohua Xi An Jiao Tong Univ Xian Peoples R China Univ Sydney Sydney NSW Australia Sensetime Grp Ltd Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Knowledge distillation aims to transfer representation ability from a teacher model to a student model. Previous approaches focus on either individual representation distillation or inter-sample similarity preservation. While we argue that the inter-sample relation conveys abundant information and needs to be distilled in a more effective way. In this paper, we propose a novel knowledge distillation method, namely Complementary Relation Contrastive Distillation (CRCD), to transfer the structural knowledge from the teacher to the student. Specifically, we estimate the mutual relation in an anchor-based way and distill the anchor-student relation under the supervision of its corresponding anchor-teacher relation. To make it more robust, mutual relations are modeled by two complementary elements: the feature and its gradient. Furthermore, the low bound of mutual information between the anchor-teacher relation distribution and the anchor-student relation distribution is maximized via relation contrastive loss, which can distill both the sample representation and the inter-sample relations. Experiments on different benchmarks demonstrate the effectiveness of our proposed CRCD.

关键词： computer vision Benchmark testing pattern recognition Mutual information

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Learning Progressive Point Embeddings for 3D Point Cloud Generation

Learning Progressive Point Embeddings for 3D Point Cloud Gen...

引用

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

作者： Wen, Cheng Yu, Baosheng Tao, Dacheng Univ Sydney Fac Engn Sch Comp Sci 6 Cleveland St Darlington NSW 2008 Australia

ISBN: (纸本)9781665445092

Generative models for 3D point clouds are extremely important for scene/object reconstruction applications in autonomous driving and robotics. Despite recent success of deep learning-based representation learning, it remains a great challenge for deep neural networks to synthesize or reconstruct high-fidelity point clouds, because of the difficulties in 1) learning effective pointwise representations;and 2) generating realistic point clouds from complex distributions. In this paper, we devise a dual-generators framework for point cloud generation, which generalizes vanilla generative adversarial learning framework in a progressive manner. Specifically, the first generator aims to learn effective point embeddings in a breadth-first manner, while the second generator is used to refine the generated point cloud based on a depth-first point embedding to generate a robust and uniform point cloud. The proposed dual-generators framework thus is able to progressively learn effective point embeddings for accurate point cloud generation. Experimental results on a variety of object categories from the most popular point cloud generation dataset, ShapeNet, demonstrate the state-of-the-art performance of the proposed method for accurate point cloud generation.

关键词： Deep learning Solid modeling computer vision Three-dimensional displays Generators Adversarial machine learning pattern recognition

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Learning To Count Everything

Learning To Count Everything

引用

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

作者： Ranjan, Viresh Sharma, Udbhav Thu Nguyen Hoai, Minh SUNY Stony Brook Stony Brook NY 11794 USA VinAI Res Hanoi Vietnam

ISBN: (纸本)9781665445092

Existing works on visual counting primarily focus on one specific category at a time, such as people, animals, and cells. In this paper, we are interested in counting everything, that is to count objects from any category given only a few annotated instances from that category. To this end, we pose counting as a few-shot regression task. To tackle this task, we present a novel method that takes a query image together with a few exemplar objects from the query image and predicts a density map for the presence of all objects of interest in the query image. We also present a novel adaptation strategy to adapt our network to any novel visual category at test time, using only a few exemplar objects from the novel category. We also introduce a dataset of 147 object categories containing over 6000 images that are suitable for the few-shot counting task. The images are annotated with two types of annotation, dots and bounding boxes, and they can be used for developing few-shot counting models. Experiments on this dataset shows that our method outperforms several state-of-the-art object detectors and few-shot counting approaches. Our code and dataset can be found at https://***/cvlab-stonybrook/LearningToCountEverything.

关键词： Visualization computer vision Codes Annotations Animals Detectors pattern recognition

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Enhance Curvature Information by Structured Stochastic Quasi-Newton Methods

Enhance Curvature Information by Structured Stochastic Quasi...

引用

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

作者： Yang, Minghan Xu, Dong Chen, Hongyu Wen, Zaiwen Chen, Mengyun Peking Univ Sch Math Sci Beijing Peoples R China Peking Univ Beijing Int Ctr Math Res Beijing Peoples R China Peking Univ Ctr Data Sci Beijing Peoples R China Peking Univ Natl Engn Lab Big Data Anal & Applicat Beijing Peoples R China Huawei Technol Co Ltd Shenzhen Peoples R China

ISBN: (纸本)9781665445092

In this paper, we consider stochastic second-order methods for minimizing a finite summation of nonconvex functions. One important key is to find an ingenious but cheap scheme to incorporate local curvature information. Since the true Hessian matrix is often a combination of a cheap part and an expensive part, we propose a structured stochastic quasi-Newton method by using partial Hessian information as much as possible. By further exploiting either the low-rank structure or the kronecker-product properties of the quasi-Newton approximations, the computation of the quasi-Newton direction is affordable. Global convergence to stationary point and local superlinear convergence rate are established under some mild assumptions. Numerical results on logistic regression, deep autoencoder networks and deep convolutional neural networks show that our proposed method is quite competitive to the state-of-the-art methods.

关键词： Training computer vision pattern recognition Convolutional neural networks Artificial intelligence Portfolios Optimization

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