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检索条件"任意字段=2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016"

共 21008 条记录，以下是1451-1460 订阅

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Universal Photometric Stereo Network using Global Lighting Contexts

Universal Photometric Stereo Network using Global Lighting C...

引用

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

作者： Ikehata, Satoshi Natl Inst Informat NII Chiyoda City Tokyo Japan

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This paper tackles a new photometric stereo task, named universal photometric stereo. Unlike existing tasks that assumed specific physical lighting models;hence, drastically limited their usability, a solution algorithm of this task is supposed to work for objects with diverse shapes and materials under arbitrary lighting variations without assuming any specific models. To solve this extremely challenging task, we present a purely data-driven method, which eliminates the prior assumption of lighting by replacing the recovery of physical lighting parameters with the extraction of the generic lighting representation, named global lighting contexts. We use them like lighting parameters in a calibrated photometric stereo network to recover surface normal vectors pixelwisely. To adapt our network to a wide variety of shapes, materials and lightings, it is trained on a new synthetic dataset which simulates the appearance of objects in the wild. Our method is compared with other state-of-the-art uncalibrated photometric stereo methods on our test data to demonstrate the significance of our method.

关键词： Training computer vision Shape Computational modeling Lighting Cameras pattern recognition

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Generating High Fidelity Data from Low-density Regions using Diffusion Models

Generating High Fidelity Data from Low-density Regions using...

引用

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

作者： Sehwag, Vikash Hazirbas, Caner Gordo, Albert Ozgenel, Firat Ferrer, Cristian Canton Princeton Univ Princeton NJ 08544 USA Meta AI Menlo Pk CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Our work focuses on addressing sample deficiency from low-density regions of data manifold in common image datasets. We leverage diffusion process based generative models to synthesize novel images from low-density regions. We observe that uniform sampling from diffusion models predominantly samples from high-density regions of the data manifold. Therefore, we modify the sampling process to guide it towards low-density regions while simultaneously maintaining the fidelity of synthetic data. We rigorously demonstrate that our process successfully generates novel high fidelity samples from low-density regions. We further examine generated samples and show that the model does not memorize low-density data and indeed learns to generate novel samples from low-density regions.

关键词： Manifolds computer vision Computational modeling Diffusion processes Data models pattern recognition

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Video Frame Interpolation with Transformer

Video Frame Interpolation with Transformer

引用

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

作者： Lu, Liying Wu, Ruizheng Lin, Huaijia Lu, Jiangbo Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China SmartMore Hong Kong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Video frame interpolation (VFI), which aims to synthesize intermediate frames of a video, has made remarkable progress with development of deep convolutional networks over past years. Existing methods built upon convolutional networks generally face challenges of handling large motion due to the locality of convolution operations. To overcome this limitation, we introduce a novel framework, which takes advantage of Transformer to model long-range pixel correlation among video frames. Further, our network is equipped with a novel cross-scale window-based attention mechanism, where cross-scale windows interact with each other. This design effectively enlarges the receptive field and aggregates multi-scale information. Extensive quantitative and qualitative experiments demonstrate that our method achieves new state-of-the-art results on various benchmarks.

关键词： Interpolation computer vision Correlation Convolution Aggregates Face recognition Computational modeling

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Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via Discretisation

Coarse-to-Fine Q-attention: Efficient Learning for Visual Ro...

引用

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

作者： James, Stephen Wada, Kentaro Laidlow, Tristan Davison, Andrew J. Imperial Coll London Dyson Robot Lab London England

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present a coarse-to-fine discretisation method that enables the use of discrete reinforcement learning approaches in place of unstable and data-inefficient actor-critic methods in continuous robotics domains. This approach builds on the recently released ARM algorithm, which replaces the continuous next-best pose agent with a discrete one, with coarse-to-fine Q-attention. Given a voxelised scene, coarse-to-fine Q-attention learns what part of the scene to `zoom' into. When this `zooming' behaviour is applied iteratively, it results in a near-lossless discretisation of the translation space, and allows the use of a discrete action, deep Q-learning method. We show that our new coarseto-fine algorithm achieves state-of-the-art performance on several difficult sparsely rewarded RLBench vision-based robotics tasks, and can train real-world policies, tabula rasa, in a matter of minutes, with as little as 3 demonstrations.

关键词： Visualization computer vision Q-learning Machine vision Robot vision systems pattern recognition Task analysis

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Quantifying Societal Bias Amplification in Image Captioning

Quantifying Societal Bias Amplification in Image Captioning

引用

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

作者： Hirota, Yusuke Nakashima, Yuta Garcia, Noa Osaka Univ Suita Osaka Japan

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We study societal bias amplification in image captioning. Image captioning models have been shown to perpetuate gender and racial biases, however, metrics to measure, quantify, and evaluate the societal bias in captions are not yet standardized. We provide a comprehensive study on the strengths and limitations of each metric, and propose LIC, a metric to study captioning bias amplification. We argue that, for image captioning, it is not enough to focus on the correct prediction of the protected attribute, and the whole context should be taken into account. We conduct extensive evaluation on traditional and state-of-the-art image captioning models, and surprisingly find that, by only focusing on the protected attribute prediction, bias mitigation models are unexpectedly amplifying bias.

关键词： Measurement computer vision Equalizers Computational modeling Focusing Predictive models Skin

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Tencent-MVSE: A Large-Scale Benchmark Dataset for Multi-Modal Video Similarity Evaluation

Tencent-MVSE: A Large-Scale Benchmark Dataset for Multi-Moda...

引用

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

作者： Zeng, Zhaoyang Luo, Yongsheng Liu, Zhenhua Rao, Fengyun Li, Dian Guo, Weidong Wen, Zhen Tencent QQ Browser Lab Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Multi-modal video similarity evaluation is important for video recommendation systems such as video deduplication, relevance matching, ranking, and diversity control. However, there still lacks a benchmark dataset that can support supervised training and accurate evaluation. In this paper, we propose the Tencent-MVSE dataset, which is the first benchmark dataset for the multi-modal video similarity evaluation task. The Tencent-MVSE dataset contains video pairs similarity annotations, and diverse metadata including Chinese title, automatic speech recognition (ASR) text, as well as human-annotated categories/tags. We provide a simple baseline with a multi-modal Transformer architecture to perform supervised multi-modal video similarity evaluation. We also explore pre-training strategies to make use of the unpaired data. The whole dataset as well as our baseline will be released to promote the development of the multi-modal video similarity evaluation. The dataset has been released in https://***/.

关键词： Training computer vision Annotations computer architecture Benchmark testing Transformers pattern recognition

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HARA: A Hierarchical Approach for Robust Rotation Averaging

HARA: A Hierarchical Approach for Robust Rotation Averaging

引用

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

作者： Lee, Seong Hun Civera, Javier Univ Zaragoza I3A Zaragoza Spain

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose a novel hierarchical approach for multiple rotation averaging, dubbed HARA. Our method incrementally initializes the rotation graph based on a hierarchy of triplet support. The key idea is to build a spanning tree by prioritizing the edges with many strong triplet supports and gradually adding those with weaker and fewer supports. This reduces the risk of adding outliers in the spanning tree. As a result, we obtain a robust initial solution that enables us to filter outliers prior to nonlinear optimization. With minimal modification, our approach can also integrate the knowledge of the number of valid 2D-2D correspondences. We perform extensive evaluations on both synthetic and real datasets, demonstrating state-of-the-art results.

关键词： computer vision pattern recognition Optimization

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Learning with Neighbor Consistency for Noisy Labels

Learning with Neighbor Consistency for Noisy Labels

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

作者： Iscen, Ahmet Valmadre, Jack Arnab, Anurag Schmid, Cordelia Google Res Meylan France Univ Adelaide AIML Adelaide SA Australia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent advances in deep learning have relied on large, labelled datasets to train high-capacity models. However, collecting large datasets in a time- and cost-efficient manner often results in label noise. We present a method for learning from noisy labels that leverages similarities between training examples in feature space, encouraging the prediction of each example to be similar to its nearest neighbours. Compared to training algorithms that use multiple models or distinct stages, our approach takes the form of a simple, additional regularization term. It can be interpreted as an inductive version of the classical, transductive label propagation algorithm. We thoroughly evaluate our method on datasets evaluating both synthetic (CIFAR-10, CIFAR-100) and realistic (mini-Webvision, Web vision, Clothing1M, mini-ImageNet-Red) noise, and achieve competitive or state-of-the-art accuracies across all of them.

关键词： Training Deep learning computer vision Stochastic processes computer architecture Semisupervised learning Prediction algorithms

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Eigencontours: Novel Contour Descriptors Based on Low-Rank Approximation

Eigencontours: Novel Contour Descriptors Based on Low-Rank A...

引用

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

作者： Park, Wonhui Jin, Dongkwon Kim, Chang-Su Korea Univ Seoul South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Novel contour descriptors, called eigencontours, based on low-rank approximation are proposed in this paper. First, we construct a contour matrix containing all object boundaries in a training set. Second, we decompose the contour matrix into eigencontours via the best rank-M approximation. Third, we represent an object boundary by a linear combination of the Al eigencontours. We also incorporate the eigencontours into an instance segmentation framework. Experimental results demonstrate that the proposed eigencontours can represent object boundaries more effectively and more e f ficiently than existing descriptors in a low-dimensional space. Furthermore, the proposed algorithm yields meaningfid performances on instance segmentation datasets.

关键词： Training computer vision Approximation algorithms pattern recognition Matrix decomposition

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Object-Relation Reasoning Graph for Action recognition

Object-Relation Reasoning Graph for Action Recognition

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

作者： Ou, Yangjun Mi, Li Chen, Zhenzhong Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Action recognition is a challenging task since the attributes of objects as well as their relationships change constantly in the video. Existing methods mainly use object-level graphs or scene graphs to represent the dynamics of objects and relationships, but ignore modeling the fine-grained relationship transitions directly. In this paper, we propose an Object-Relation Reasoning Graph (OR(2)G) for reasoning about action in videos. By combining an object-level graph (OG) and a relation-level graph (RG), the proposed OR(2)G catches the attribute transitions of objects and reasons about the relationship transitions between objects simultaneously. In addition, a graph aggregating module (GAM) is investigated by applying the multi-head edge-to-node message passing operation. GAM feeds back the information from the relation node to the object node and enhances the coupling between the object-level graph and the relation-level graph. Experiments in video action recognition demonstrate the effectiveness of our approach when compared with the state-of-the-art methods.

关键词： Couplings Visualization computer vision Message passing Semantics Cognition pattern recognition

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