h137437-优快云博客

翻译 MoViNets: Mobile Video Networks for Efficient Video Recognition

MoViNetsAbstract1 Introduction2. Related Work3. Mobile Video Networks (MoViNets)3.1. Searching for MoViNet3.2. The Stream Buffer with Causal Operations3.2.1 Causal Operations3.2.2 Training and Inference with Stream Buffers3.3. Temporal Ensembles4. Experime

2021-04-19 10:12:57 3266

翻译 Revisiting ResNets: Improved Training and Scaling Strategies

ResNet-RSAbstract1 Introduction2. Characterizing Improvements on ImageNet4. MethodologyExperimentsConclusion备注：如有侵权，立即删除code: https://github.com/tensorflow/tpu/tree/master/models/official/resnet/resnet_rssource: 2021Abstract新的计算机视觉架构占据了焦点，但模型架构的影响往往与

2021-04-13 15:09:27 875

翻译 Is Space-Time Attention All You Need for Video Understanding?

TimeSformerAbstract1 Introduction3 The TimeSformer ModelExperiments4.1. Analysis of Self-Attention Schemes4.2. Varying the Number of Tokens in Space and Time4.3. The Importance of Pretraining and Dataset Scale4.4. Comparison to the State-of-the-Art4.5. Lon

2021-04-06 16:58:30 907

翻译 Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting

InformerAbstract1 Introduction3 Proposed MethodExperimentsConclusionhttps://arxiv.org/abs/2004.03548备注：如有侵权，立即删除code: https://github.com/decisionforce/TPNsource: AAAI 2021Abstract1 Introduction3 Proposed MethodExperimentsConclusion...

2021-04-02 09:24:42 1544

翻译 Mutual Modality Learning for Video Action Classification

MMLAbstract1 Introduction3 Proposed MethodExperimentsConclusionhttps://arxiv.org/pdf/2011.02543v1.pdf备注：如有侵权，立即删除code: https://github.com/papermsucode/mutual-modality-learningsource: 2020Abstract1 Introduction3 Proposed MethodExperimentsConclusio

2020-12-13 20:14:13 292

翻译 MotionSqueeze: Neural Motion Feature Learning for Video Understanding

MotionSqueezeAbstract1 Introduction3 Proposed MethodExperimentsConclusionhttps://arxiv.org/pdf/2011.02543v1.pdf备注：如有侵权，立即删除code: https://github.com/arunos728/MotionSqueezesource: ECCV2020Abstract运动在视频理解领域非常重要，大多数视频分类的神经网络模型通过现有的光流提取方法来利用运动信息。因为计算帧与帧

2020-12-11 14:25:04 2021

翻译 TSM: Temporal Shift Module for Efficient Video Understanding

TSMAbstract1 Introduction3 Proposed MethodExperimentsConclusionhttps://arxiv.org/abs/1811.08383备注：如有侵权，立即删除code: https://github.com/mit-han-lab/temporal-shift-modulesource: ICCV2019Abstract视频流的爆炸性增长给高准确率和低计算成本的视频理解的带来了很大的挑战。传统的2D CNNs计算量很小但是无法捕获时序关系

2020-12-09 15:29:53 713

翻译 PAN: Towards Fast Action Recognition via Learning Persistence of Appearance

PANAbstract1 Introduction3 Proposed MethodExperimentsConclusion备注：如有侵权，立即删除code: https://github.com/zhang-can/PAN-PyTorchsource: 2020Abstract高效建模视频中的动态运动信息对于行为识别任务非常重要。大部分表现好的方法都依赖于用密集光流代表行为特征。尽管结合光流和RGB帧能够取得更好的效果，光流提取是非常耗时的。这无疑是不利于实时行为识别的。在本文中，我们解除了

2020-12-08 15:08:56 928 1

翻译 Late Temporal Modeling in 3D CNN Architectures with BERT for Action Recognition

R2+1D-BERTAbstract1 Introduction3 Proposed Method3.1 BERT-based Temporal Modeling with 3D CNNs for Action Recognition3.2 Proposed Feature Reduction Blocks: FRAB & FRMB3.3 Proposed BERT Implementations on SlowFast ArchitectureExperimentsConclusion备注：机

2020-11-29 22:59:34 1400

翻译 Omni-sourced Webly-supervised Learning for Video Recognition

Omni-sourcedAbstract1 Introduction3 Proposed Method3.1 Overview3.2 Framework formulation3.3 Task-specific data collection3.4 Teacher filtering3.5 Transforming to the target domainJoint trainingDatasetsExperimentsVideo architecturesVerifying the efficacy of

2020-11-26 15:35:12 1571

翻译 SF-Net: Single-Frame Supervision for Temporal Action Localization

SF-NetAbstract1 IntroductionProposed Method3.1 Problem Definition3.2 Framework3.3 Pseudo Label Mining and Training Objectives3.4 InferenceExperimentsDatasetsImplementation DetailsConclusion备注：机翻，如有侵权，立即删除code: https://github.com/Flowerfan/SF-Netsource:

2020-11-21 16:22:45 2055

翻译 AlphAction：Asynchronous Interaction Aggregation for Action Detection

AlphAction行为识别abstract1 Introduction3 Proposed Method3.1 Instance Level and Temporal Memory Features3.2 Interaction Modeling and Aggregation3.3 Asynchronous Memory Update AlgorithmExperimentsConclusion备注：机翻code: https://github.com/MVIG-SJTU/AlphActions

2020-11-18 16:32:16 1466 5

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