人工智能 | ShowMeAI资讯日报 #2022.06.06

人工智能 | ShowMeAI资讯日报 #2022.06.06

ShowMeAI日报系列全新升级!覆盖AI人工智能 工具&框架 | 项目&代码 | 博文&分享 | 数据&资源 | 研究&论文 等方回归模型有哪些向。点击查看 历史文章列表,在公众号内订阅话题 #ShowMeAI资讯日报,可接收每日最新推送。点击 专题合辑&电子月刊 快速浏览各专题全集。回归模型分析

1.公积金工具&框架

人工智能 | ShowMeAI资讯日报 #2022.06.06

工具箱:Lite.AI.ToolKit –哪里拍婚纱照最美 开箱即用的C++ AI 工具箱,包括70+流行的开源模型,如最新的RVM, YOLOX, YOLOP, YOLOR, YoloV5, DeepLa线性回归模型bV3, ArcFace等模型,支持ONNXRuntime/NCNN/MNN/TNN

tags:[C++,目标检测,人脸检测,部署]

‘Lite.AI.ToolKit: A lite C++ toolkit of awesome AI m回归模型中引入虚拟变量的作用odels,能力拼音 such as RobustVideoMpython保留字atting, YOLOX, YOLOP etc.’ bGoy DefTruth

Gi嫩绿拼音tHub:github.copython保留字m/DefTruth/li…

人工智能 | ShowMeAI资讯日报 #2022.06.06

工具库:Zennit – PyTorch神经网络开源解释/python123探索框架

tags:[深度学习,解释,Pypython基础教程torch]

‘Zennit – a high-level开源节流 framework in Python using PyTorch for explaining/exploring neural networks using attribu能力培养与测试tion methods like LRP.’ by Christopher

GitHub:github.com/chr5tphr/线性回归模型ze…

工具库:PyDaddy – 分析随机时间序列数据

tags:[时间序列]

‘PyDaddy – Package to analyse st能力拼音ochastic time series data’ by TEE Lab

GitHub:github.com/tee-lab/PyD…

人工智能 | ShowMeAI资讯日报 #2022.06.06

工具库:continual-inference – 用于构建持续推理网络的PyTorch开源众包组件工资超过5000怎么扣税

tags:[持续推理,开源阅读神经网络,pytorch]

‘continual-inference – PyTorch building bloc回归模型有哪些ks for Continual Inference NetworkGos’ by LukasHedegaard

GitHub:githu开源阅读app下载安装b.com/LukasHedega…

人工智能 | ShowMeAI资讯日报 #2022.06.06

工具库:gget – 用于高效查询基因组数开源节流是什么意思据库的开源命令行工具和Python包

tags:[基因组,基因数据,生物医疗]

‘gget – a free and open-sourc回归模型拟合效果判断e command-line tool and Python package that ena能力拼音bles efficient querying of genomi那里拍婚纱照好c databases’ b公司让员工下班发手机电量截图y Pachter Lab

GitHub:github.com/ppython可以做什么工作achterlab/…

人工智能 | ShowMeAI资讯日报 #2022.06.06

2.项目&代码

人工智能 | ShowMeAI资讯日报 #2022.06.06

项目:Python-Mini-Projects:Python迷你练手项目集锦

tags:[Pyth年龄拼音on,项目]

‘Python-Mini-Projects – A collection of simple python mini projects to enhance your python sk回归模型的显著性检验ills’ by PYTHON WORLD

Git回归模型拟合效果判断Hub:github.com/Python-Worl…

项目:工龄差一年工资差多少WorkAttendanceSystem – 基于opencv、dilb的员工公积金人脸识别考勤系统

tags:[opencv,dilib,人脸识别,自动考勤]

GitHub:github.com/inspurer/Wo…

人工智能 | ShowMeAI资讯日报 #2022.06.06

人工智能 | ShowMeAI资讯日报 #2022.06.06

3.博文&amp能力拼音;分享

人工智能 | ShowMeAI资讯日报 #2022.06.06

教程:( ACL 2022 Tutorial Slides): 基于预训练语言模型的零样本和少样本 NLP

t能力拼音ags:[语言模型,少样回归模型公式本学习,自然语言处理]

‘ACL 202开源2 Tutorial: Zero- and Fepython编程w-Shot NLP w哪里拍婚纱照最美ith Pretrained Language Models’ by AI2

GitHub:github.com/allenai/acl…

教程:回归模型有哪些Th工龄越长退休金越多吗e System Design Primer:系统设计入门,学习如何设计可扩展系统

tags:能力拼音[系统设计]

‘T回归模型的作用he System Design Primer – Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards回归模型公式.’ b回归模型分析y Donne Martin

GitHub:github.com/donnemartin…

教程:GitHub为Markdown回归模型怎么建立设置的一些特殊的引用块样式,方便设python基础教程

tags:[资讯,工具]

GitHub:github.com/github/feed…

4.数据&资源

人工智能 | ShowMeAI资讯日报 #2022.06.06

数据集:3D人体数google据集

tags:[数据集,人体]

‘THUmanpython是什么意思3.0 Dataset’ by fwbx529

GitHub:github.com/fwbx529/THu…

人工智能 | ShowMeAI资讯日报 #2022.06.06

资源数据:mNLPyosuite:用肌肉骨骼模型解决的环境/任务集合

tags:[数据,任务,肌肉,骨骼]

‘MyoSuite – a collection of environments/tasks to be solved by mu回归模型的显著性检验sculoskeletal models simulated with the Mu开源节流JoCo physics engine and wrapped in the OpenAI gypython基础教程m API.’ by Meta Research

GitHub:github.com/facebookres…

人工智能 | ShowMeAI资讯日报 #2022.06.06

数据集:MuCGECpython是什么意思中文纠错数据集及文本纠错SOTA模型

tags:[纠错,文本纠错,数据集]

GitHub:github.com/HillZhang19…

资源列表:图和表格数据上的联邦学习文开源节流是什么意思献资NLP源列表

tags:[图,表格,结构化,联邦学习]

‘Federated-Learning-on-Graph-and-Tabular-Dapython语言ta – Federated learning on graphpython是什么意思 and tabular data related papers, frameworks, and datasets.’ by YoungFish

GitHub:github.com/youngfish42…

5.研究&apython保留字mp;论文

人工智能 | ShowMeAI资讯日报 #2022.06.06

可以点击 这里 回复关键字 日报,免费获取整理好的6月论文合辑。

宫颈癌文:Pretraining is All You Need for Image-to-Image Translation

论文标题:Pretraining is All You Negoogleed for Image-to-Image TranslatioPythonn

论文时间:25 May 2022

所属领域:Adversarial/对抗性

对应任务:Image-to-Image Tran能力培养与测试slation,Texture Synthe回归模型有哪些sis,Tra哪里拍婚纱照最美nslation,图图转换,纹理合成,转换

论文地址:arxi开源矿工v.org/abs/2205.12…

代码实现:github.com/PITI-Synthe…

论文作者:Tengfei Wang, Ting Zhang, Bo Zhang, Hao Ouyang, Dong Chen, Qifeng Che脑颅膨大的意思n, Fang Wen

论文简介:We propose to use pretraining to boost general image-to-image translation. / 我们建议使用预训练来提高一般的图NLP图转换(Image-to-Image Translation)。

论文摘要:We propose to use pretrainpython怎么读ing to boost general image-to-image translation.多元回归模型 Prior image-to-image translation methods usually need dedic开源ated那里拼音 architectural design and train individual translation models from scratch, struggling for hNLPigh-quality generation of complex scenes, especially when paired training data are not abundant. In this paper, we regard each image-to-image tr公司让员工下班发手机电量截图anslation problem as a dowpython可以做什么工作nstream task and introduce a simple and geneGoric framework that adapts a pretpython可以做什么工作rained回归模型有哪些 diffusion model to accommodate various kinds of回归模型拟合效果判断 image-to-image translation. We also propose adversarial training to enhance the texture synthpython123esis in the diffusion model trainnlping, in conjunction with normalized guidance sampling to哪里拍婚纱照最美 improve the generation quality. We present extensive empirical comparison across various tasks on challenging benchmarks such as ADE20K, COCO-Stuff, and DIODE, showing the proposed pretraining-based image-to-image translation (PITI) is capable of synthesizing images of unprecedented realism and faithfulness.

我们建议使用预训练来提高一般的图图转换回归模型有哪些(Image-to-Image Transl年龄拼音ation)效果。枸杞先前的图图转换方法通常需要专门的架构设计和从头开始训练单个翻译模google型,难以高质量地生成复杂场景下的图像,尤其是在配对训练数python基础教程据不丰富的情况下。在本文中,我们将每个图图转换问python123平台登录题视为一个下游任务,并引入了一个简单且通用的框架,该框架采用预训练的扩散模型来适应各种图像到图像的翻译。Go我们还提出对抗性训练以增强扩散模型训练中的纹理合成,并结合归一化引导采样以提高生成质量。我们在 ADE20K、COCO-Stuffpython编程 和 DIODE 等具有挑战性的基准上对各种任务进行了广泛的实证比较,表明脑颅膨大的意思所提出的基于预训练的图像到图像转换 (PITI) 能够合成具有前所未有的真实可靠的图像。

人工智能 | ShowMeAI资讯日报 #2022.06.06

人工智能 | ShowMeAI资讯日报 #2022.06.06

论文:Vagoogleriational Diffusion Models

论文标题:Variational Diffusion Models

论文时间:NeurIPS 2021

所属领域:Methodology

对应任务:Density Estimation,密度龚俊估计

论文地址:arxiv.org/abs/2107.00…

代码实现:github.com/google-res开源e…

论文作者:Diederik Kingma, Tim Salimans, Ben Poole, Jonathan Ho

论文简介线性回归模型:In addition, we sh回归模型ow that the continuous-time VLB枸杞 is invariant to the noise schedule, except for the signal-python基础教程to-noise ratio at its endpoints.开源中国 / 此外,开源我们证明了连续时间 VLB 对噪声调度是不变的,除了端python语言点处的信噪回归模型怎么建立比。

论文摘要:Diffusion-based generative models have demonstrpython编程ated a capacity for perceptualpython基础教程ly impressive synthesis, but can they also be great lik年龄拼音elihood开源中国-based models? We answer this in the affirmative, and introduce a family of diffu开源众包sion-based开源众包 generative models that obpython保留字tain state-of-the-art likelihoods on standard image density estimatio回归模型分析n benchmarks. Unlike othe开源阅读r diffusion-线性回归模型based models脑颅膨大的意思, our method allows for efficient optimization of the noise schedule jointly with the rest of the model. W开源阅读app下载安装e show that the variational lower bound (VLB) sGoimplifies to a remarkably short e努力拼音xpression in回归模型拟合效果判断 terms of th回归模型e signal-to-noise ratio of the diffused data, thereby improving our theoretical understanding of this model class. Using枸杞 this inpython123平台登录sigh开源软件t, we prove an equivalen脑颅膨大的意思ce between several models proppython怎么读osed in the literature. In addpython安装教程ition, we show that the continuo开源是什么意思us-time VLB is invariant to the noise schedule, except for the signal-to-noise ratio at its endpoints. This enables us to learn a noise sch回归模型的作用edule that minimizes the variance of the resulting VLB estimator, lea开源众包ding to faster optimization. Combining the回归模型分析se advances with a回归模型拟合效果判断rchitectural improve开源软件ments, we obtain state-of-thNLPe-art likelihoods on image density estimation benchmarks, outperforming autoregressi能力拼音ve mod公积金els that hav工资超过5000怎么扣税e dominated these benchmarks for many years, with often significantly faster optimization. In addition, we show ho开源是什么意思w to use the model as part of a bits-back compression scheme, and demonstrate lospython语言sless compression r哪里拍婚纱照最美ates close to the theoretica回归模型有哪些l optimum.

基于扩散的生成模型已经证明了一种强大的综合python怎么读能力,但它们也可以是很好的基于可能性的模型吗?我们的回答是肯定的,并引入了一系列基于扩散的生成模型,这些模型在标准图像密度估计基准上获得了最先进的效果。与其他基于扩散的模型不同,我们的方法允许与模型的其余部分一起有效地优化噪声调度。我们表明,变分下限(VLB)在扩散数据的信噪比方面简化为一个非常短的表达式,从而提高了我们对这个模Python型类的理论理解。利用这一想法,我们证明了文献中提出的几个模型之间的等价性。此外,我们证明了连续时间 VLB 对噪声调度是不变的,除了其端点的信噪比。这使我们能够学习最小化结果 VLB 估计器的方差的噪声调度,从而实现更快的google优化。将这些进步与架构改进相结合,我们在图像密度估计基准上获得了最先进的可能性,优于多年来主多元回归模型导这些基准的自回归模型,而且优化速度通常要快得多。此外,我们展示了如何将那里拍婚纱照好该模型用作位回压缩方案的一部分,并展示了接近理论最优值的无损压缩率。

人工智能 | ShowMeAI资讯日报 #2022.06.06

人工智能 | ShowMeAI资讯日报 #2022.06.06

人工智能 | ShowMeAI资讯日报 #2022.06.06

论文:Top1 Solution of QQ Browser 202Python1 Ai A工资超过5000怎么扣税lgorithm Competition Track 1 : Multimodal Video Similarity

论文标题:Top1 Solution of QQ Browser 2021 Ai Algorithm Competition Track 1 : Multimodal Video Siminlplarity

论文时间:30 Oct 2021

所属领域:Natural Language Processing/自然语言处理

对应任务:Langu开源众包age Modelling,TAG,Video Similarity,语言建模,视频相似度

论文地址:arxiv.org/abs/2111.01…

代码实现:github.com/zr2021/2021…

论文作者:Zhuoran Ma, Majing Lou, Xuan Ouyang

论文简介:I开源众包n thpython怎么读is paper, we describe the solut嫩绿拼音ion to the QQ Browser 2021 Ai Algorithm Competition (AIAC) Track 1. / 在本文中,我们描述了 QQ 浏览器 2021 人工智能算法竞赛 (AIAC) Track 1 的解决方案。

论文摘要:Inpython语言 this paper, we describe the solution tgoogleo the QQ BrNLPowser 2021 Ai Algorithm Competition (AIAC) Track 1. We use the mu工龄越长退休金越多吗lti-modal transformer model for the video embed回归模型ding e回归模型xtraction. In the pretrain phas龚俊e, we train the model with three tasks, (1) Video Tag Classification (VTC), (2) Mask Language Mogoogledeling (MLM) and (3) Mask Frame Modeling (MFM).python怎么读 In the finetune phase, we train the model wit线性回归模型h vide线性回归模型o similarity based on rank normalized humpython可以做什么工作an lpython是什么意思abe枸杞ls. Ou多元回归模型r full pipelin哪里拍婚纱照最美e, after ensembling several models, scores 0.852 on the leaderbo回归模型怎么建立ard, whiNLPch we achieved the 1st place in the competition. The source codes have been rel多元回归模型eased at Gi回归模型的显著性检验thu那里拼音b.

在本文中,我们描述了 QQ 浏览器 2021 人工智能算法竞赛 (AIAC) Track 1 的NLP解决方案。我们使用多模态转换器模型进行视频嵌入提取。 在预训练阶段,我们用三个任务训练模型,(1)视频标签分类(VTC),(2)掩码语言建模(MLM)和(3)掩码帧建模(MFM)。Python 在微调回归模型分析阶段,我们根据等级归一化的人类标签训练具有视频相似性的模型。 我们Go的完整管道在集成了几个模型后,在排行榜上的得分为 0.852开源众包,我们在比赛中获得了第一名。 源代码已在 Github 上发布。

人工智能 | ShowMeAI资讯日报 #2022.06.06

人工智能 | ShowMeAI资讯日报 #2022.06.06

论文:Datase开源中国t Condensation via Efficient Synthetic-Dat回归模型有哪些a Parameterization

论文标题:Dataset Condensation vi开源矿工a Efficient Synthetic-Data Papython语言rameterization

论文时间:30 May 2022

论文地址:arxiv.org/abs/2205.14…

代码实现:github.com/snu-mllab/e…

论文作者:Jang-Hyun Kim, Jinuk Kim, Seong Joon Oh, Sangdogoogleo Yunpython怎么读, Hwanjun Song,线性回归模型 Joonpython怎么读Hyun Jeong, Jung-Woo Ha, Hyun Oh Song

论文简介:The great success of machine learning with mas公司让员工下班发手机电量截图sive amounts of data comes at a price of hu开源代码网站githubge co工商银行mputation costs and storage for training and tuning. / 具有大量数据的机器学习的巨大成功是以巨大的嫩绿拼音计算成本和用于训练和调整的存储为python基础教程代价的。

论文摘要:The great success of machine learning with massive amounts of data comes at a price of huge computation costs and storage for training and tuning. Recent studies on dataset co回归模型怎么建立ndensation attempt to reduc开源众包e the dependence on such massive data by synthesizing a compact training dataset. Howe回归模型的显著性检验ver, the existing apprgoogleoaches have fundamental limitations in optimization due to the limited representability of synthetic datasets without considering any dGoata regularity characteristics. To this e那里拼音nd, we propose a novel conde开源节流nsation frampython123平台登录ework that generates multiple synthetic data with a limited storage budget via efficient parameterization consid开源代码网站githubering data regularity. We further analyze the shortcomings of the existing gradient matching-based condensation methods and develop an effec年龄拼音tive optimization technique for improving the condensation of training data information.工龄越长退休金越多吗 We prgoogleopose a unified algorithm that d线性回归模型rastically improves the quality of condensed data能力培养与测试 against the current state-of-the-a龚俊rt on CIFAR-10, ImageNet, and Speech Commands.

海量数据上的机器学习的巨大成功是以巨大的计算成本和用于训练工龄越长退休金越多吗和调整的存储为代价的。最近关于数据集压缩的研究试图通过合成一个紧凑的训练数据集来减少对如此大量数据的依赖。然而,由于合成数据集的可表示性有限,没有考虑任何数据规律性特征,现有方法在优化方面存在根本限制。为此,我们提出了一种新颖的压缩框架,该框架通过考虑数据规律性的有效参数化生成具有有限存储预算的多个合成数据。我们进一步分析了现开源矿工有基于梯度匹配回归模型怎么建立的压缩方法的缺点,并开发了一种有效的优化技术来改进训练数据Python信息的压缩。我们提出了一种统一的算法,可以相Go对于 CIFAR-10、ImageNet 和 Speech Commands 上的当前最先进技术大幅提高压缩数据的质量回归模型的显著性检验

人工智能 | ShowMeAI资讯日报 #2022.06.06

人工智能 | ShowMeAI资讯日报 #2022.06.06

论文:High-Resolution Image Harmonization via Collaborative Dual Transformations

论文标题:High-Resolution Image Harmonization via Collaborative Dual Transformations

论文时间:14 Sep 2021

所属领域:计算机视觉

论文地址能力培养与测试arxiv.org/abs/2109.06…

代码实现:github多元回归模型.com/bcmi/CDTNet…

论文作者:Wenyan Cong, Xi开源是什么意思nhao Tao, Li Niu, Jing Liang, Xupython怎么读esong Gao, Qihao Sun, Liqing Zhang

论文简介:Conven那里拍婚纱照好tional ipython语言mage harmonization met回归模型的显著性检验h回归模型有哪些ods learn global RGB-to-RGB transformation which could effortlessly scale to high resolutio开源节流是什么意思n, but ignore diverse local context. / 传统的图像协调方法学习工龄差一年工资差多少全局 RGB 到 RGB 的转换,可以毫不费回归模型的作用力地扩展到高分辨率,但忽略了不同的局部上下文。

论文摘要:Given a composite image, image harmonization aims to adjust the for线性回归模型eground to make it compatible with the background. H开源igh-resolution image harmonizati回归模型有哪些on is in high demand, but still remains unexplored. Conventional image harmonization methods learn global RGB-to-RGB transformation which could effortlessly scale to high resolution, bpython是什么意思ut ign能力拼音ore diverse local co那里拍婚纱照好ntext. Recegooglent deep learning methods learn the dense pixel-to-pixel transformation which could generate harmonious outputs, but are highly constrained in low resolution. In this work, we propose a high-resolutio回归模型n image harmoni那里拼音zation network with Collaborative Dual Transformapython保留字tion (CDTNet) to combine pixel-to-pixel transformation and RGB-to-RGB transformation coherently in an end-to-end network. Ourpython保留字 CDTNet con开源众包sists of a low-resolution generanlptor for pixel-tNLPo-pixel transformation, a color mapping module for RGB-to-RGB transformation, and a refinement module to take advantage of both. Extensive experiments多元回归模型 onpython基础教程 high-resolution benchmark dataset and our created high-resolution real composite imagpython安装教程es demonstrate that our CDTNet strikes a good balance between efficiency and effec宫颈癌tiveness. Our used datasets can be found in gipython保留字thub.com/b开源众包cmi/CDTNet… .

给定线性回归模型一张合成图像,图像协调旨在调整前景使其与python编程背景兼容。高分辨率图像协调的需求量很大,但仍未得到探索。传统的图像协调方法学习全开源中国局 RGB 到 RGB 转换,可以毫不费力地扩展到高分辨率,但忽回归模型的作用略了不同的局部上下文。最近的深度学习方法学习密集的像素到像素的转换,可以产生和谐的输出,但在低分辨率下受到高度限制。在这项工作中,我们提开源节流是什么意思出了一个高分辨率图像协调网络嫩绿拼音python怎么读协作双变换 (CDTN能力拼音et),以在端到端网络中连贯地结合像素到像素的变换和 RGB 到 RGB 的变换。我回归模型的显著性检验们的 CDTNet 包括一个用于像素到像素转换的低分辨率回归模型怎么建立生成器、一个用于 RGB那里拼音 到 RGB 转换的颜色映射模块,以及一个利用两者的细化模块。高分辨率基准数据集和我们创建的高分辨率真实哪里拍婚纱照最美合成图像的广泛实验表明,我们的 CDTNet 在效率和有效性之间取得了良好的平衡。我开源软件们使用的数据集可以在 github.com/bcmi/CDTNet… 中找到。

人工智能 | ShowMeAI资讯日报 #2022.06.06

人工智能 | ShowMeAI资讯日报 #2022.06.06

人工智能 | ShowMeAI资讯日报 #2022.06.06

论文那里拼音:ConvMAE: Masked Convolution Me那里拼音ets Masked Autoencoders

论文标题:ConvMAE: Masked Convolution Meets Masked Autoencoders

论文时间:8 May 2022

所属领域:Naturalpython安装教程 Language Processing/计算机视觉

对应任务:Image Classification哪里拍婚纱照最美,Object Detection,Semantic Segmentat宫颈癌ion,图像分类,目标检测,语义分割

论文地址:arxiv.org/abs/2205.03…

代码实现:github.com/alpha-vl/co…

论文作者:Peng Gao, Teli Ma, Hongsheng Li, Ziyi Lin, Jifeng Dai, Yu Qiao

论文简介:Masked auto-encoding for feature pretraining and multi-scale hybrid convolution-transformer arch多元回归模型itectures canGo further unleash the potentials of ViT, leading to state-of-the-art performances on image classification, detection and semantic segmentation. / 用于特征预python123训练和多尺度混合卷积变换器架构的掩码自动编码可以进一步释放 ViT 的潜力,从而在图像分类、检测和语义分割方面取得最先进的性能。

论文摘要:Vision Transformers (ViT) become widel那里拍婚纱照好y-adopted architectures for various visionNLP tasks. Masked auto-encoding for feature p枸杞retraining and multi-scale hybripython基础教程d convolution努力拼音-transformer architectur工龄差一年工资差多少es can further unleash the potentials of ViT, leading to state-of-the-art performances on image classificat公司让员工下班发手机电量截图ion, detection and semantic segmentation开源节流是什么意思. In那里拍婚纱照好 this paper, our ConvMAE framework demonstrates that multi那里拼音-scale hyb脑颅膨大的意思rid convolution-transformer can learn morepython保留字 discriminative repre线性回归模型sentations via the mask auto-encoding scheme. However, directly using the original mask开源中国ing strategy leads to the heavy computational cost and pretrainin回归模型中引入虚拟变量的作用g-finetun开源矿工ing disc开源rep回归模型ancy. To tack开源软件le the issupython编程e, we adopt th开源矿工e masked convolution to prevent information leakage in the convolution blocks. A simpl开源e开源矿工 block-wise masking strategy is proposed to ensupython是什么意思re computational efficiency. We also propose to more directly supervise th工资超过5000怎么扣税e mu那里拍婚纱照好lti-scale featurespython怎么读 of the encoder to boost multi-scale公积金 features. Based on our pretrained ConvMAE models, ConvMAE-Base improves ImageNet-1K finetuning accurpython123acy by 1.4% compared with MAE-Base. On object detection, ConvMAE-Base finetuned for onl开源阅读app下载安装y 25 epochs surpasses MAE-Bapython怎么读se fined-tuned for 100 epochs by 2.9% box AP and 2.2% mask AP respectively. Code and pretrained models apython安装教程re available at github.com/Alpha-VL/Co… .

视觉T回归模型有哪些ransformer (ViT) 已成为各种视工资超过5000怎么扣税觉任务广泛采用的架构。用于特征预训练和多尺度混合卷积变换器架构的掩码自动编码可以进一步释放 ViT 的潜力,从而在图像分那里拼音类、python123检测和语义分割方面取NLP得最先进的性能。在本文中,我们的 ConvMAE 框架证明了多尺度Go混合卷积变换器可以通过掩码自动编码方案学习更多的判别表示。然python基础教程而,直接使用原始掩码策略会工龄越长退休金越多吗导致开源矿工计算成本和预训练-微调差异。为了解决这个问题,我们采用掩码卷积来防止卷积块中的信息泄漏。提工商银行出了一种简单的分块屏蔽策略来确保计算效率。我们还建议更直接地监督编码器的多尺度特征以提升多尺度特征。基于我们预训练的 ConvMAE 模型,与 MAE-Base 相比,ConvMAE-Base 将 ImageNet-1K 微调精度提高了 1.4%。在目标检测方面,仅微调python可以做什么工作 25 个 epocGoh 的 ConvMAE-Base 比微调 100 个 epoch 的 MAE-Base 分别高出 2.9% 的框 AP 和 2.2% 的掩码 AP。代码和预训练模型可在 gitpython语言hub.com/Alpha-VL/Co开源节流… 获得。

人工智能 | ShowMeAI资讯日报 #2022.06.06

论文:GANimator: Neural Motion Synthepython基础教程sis from a Single Sequence

论文标题:GANimator: Neural Motion Synthesis from a Single Sequence

论文时间:5 May 2022

所属领域:Computer Vision

对应任务:motion synthesis,Style Transfer,运动合成,风格转移

论文地址:arxipython可以做什么工作v.org/abs/2205.02…

代码实现:github.c回归模型中引入虚拟变量的作用om/PeizhuoLi/g…

论文作者:Peizhuo Li, Kfir Aberman,脑颅膨大的意思 Zihan Zhang, Rana Hanocka, Olga Sorkine-Hornung

论文简介:We present GANimator, a generatipython可以做什么工作ve model that learn龚俊s to synthesize novel motions from a single, short motion sequence. / 我们提出了 GANimator,这是一种生成模型,可以枸杞学习从单个短运动序列中合成新的运动。

论文摘要:Wepython可以做什么工作 present GA开源Nimator, a generative model that learns to synthesize novel motions from a single, short motion sequence. GANimator generates motions that resemble the core elements of the original m回归模型有哪些otio公积金n, while simultaneously synthe回归模型的作用sizing novel and diverse movements. Existing data-driven年龄拼音 techniques for motion synthesis require a large motion d回归模型怎么建立ataset which contains the desired and speci回归模型怎么建立fic skeletal structure. By contrast, GANimator only requirespython123 training on a single motion sequencpython保留字e, enabling novel motion synthesis forpython123 a variety of skeletal structures e.g., bipeds, quadropeds, hexapeds, and more. Our framework contains a series of generative and adversarial neural networks, each responsible for generating motions in a specific frame rate. The framework progressively lea开源代码网站githubrns to synthesize motion from random noise, enabling hierarchical contnlprol ove开源节流r the generated motion content across varying levels of detail. We show a number of applications, including crowd simulpython可以做什么工作ation, key-frame editing, style transfer, and interactive control, which all learn from a single input sequence. Co回归模型中引入虚拟变量的作用de and data for this回归模型中引入虚拟变量的作用 paper are at peizh开源矿工uoli.github.io/ganimator .

我们提出了 GANimator,这是一种生成模型,可以学习从单个短运动序列中合成新的运动。 GANimator 生成类似于原始动作核心元素的动作,同时合成新颖多样的动作。用于运动合成的现有数据驱动技术需要包含所需和特定骨骼结构的大型运动数据集。相比之下,GANimator 只需要对单个运动序列进行训练,从而为各种骨骼结构(例如双足动物、四足动物、六足动物等)实现新颖的运动合成。我们的回归模型有哪些框架包含一系列生成和对抗神经脑颅膨大的意思网络,每个网络负责以特定帧速率生成运动。该框架逐步学习从开源软件随机噪声中合成运动,从而回归模型公式能够对生成的运动内容进行分层控制,涵盖不同的细节级别。我们展示了许多应用工龄差一年工资差多少程序,包括人群模拟、关键帧编辑、样式转换和交互式控制,它们都从单个输入序列中学习。本文的代码和数据在peizhuoli.github.io/ganimator。

人工智能 | ShowMeAI资讯日报 #2022.06.06

人工智能 | ShowMeAI资讯日报 #2022.06.06

人工智能 | ShowMeAI资讯日报 #2022.06.06

论文:MAE-AST: Masked Autoencoding Audio Spepython123ctrogram Transformer

论文标题:MAE-AST: Masked Autoencoding Audio Spectrogram Transformer

论文时间:30 Mar开源是什么意思 2022

所属领域:Audio/音频

对应任务:Audio Classification,音频分类

论文地址:arxiv.org/abs/2203.16…

代码实现:github.com/AlanBaade/M…

论文作者:Alan Baade, Puyuan Peng, David Harwath

论文简介:In this paper, we propose a simple yet powerful i年龄拼音mprovement over the recent Sel回归模型分析f-Supervised Audio Spectrogram Transform开源阅读app下载安装er (SSAST) model forpython是什么意思 sp公积金eech and audio clas回归模型拟合效果判断sification. / 在这篇论文中python编程,我们提出了一个简单而强大的改进,对最近用于语音和音频分类的自监督音频频谱图变换器 (SSAST) 模型进行了改进。

论文摘要:In this paper, we propose a simple yet powerful improvement over the recent Self-Supervised Audio Spectr哪里拍婚纱照最美ogram Transformer (SSAST) model for speech and audio classi脑颅膨大的意思fication. Specifically, we leverage the insight that the SSAST uses a very high masking ratio (75%)哪里拍婚纱照最美 during pretraining, meaning那里拼音 that the vast majority of self-attention compute is performpython是什么意思ed on mask tokens. We address this by integrating the enco龚俊der-decoder architecture from Masked Autoencoders are Scalable Vision Learners (MAE) into the SSAST, where a deep encoder opeNLPrates on only unmasked input, and那里拍婚纱照好 a shallow dec回归模型有哪些oder operates on encoder outputs and mask tokens. We find that MAE-like pretraining can provide a 3x speedup and 2x memory usage re开源软件duction over the vanilla SSAST using current audio pretraining strategies with ordinary model and input sizes. When fine-tuning on downstream tasks, which only uses the encoder, we find that our approach outperforms the SSAST on a variety of downstream tasks. We further conduct comprehensive evaluations into different stra开源代码网站githubtegies of pretraining and explore differences in MAE-style pretraining between the visual and audio domainspython123平台登录.

在本回归模型的作用文中,我们提出了一个简单而强大的改进,该改进是对最近用于语音和音频分类的自监督音频频谱图Transformer (Self-Supervised Audio Spectrogram Transformer SSAST) 模型。具体来说,我们认为 SSAST 在预训练期间使用非常高的掩码率 (75%) ,这意味着工商银行绝大多数自注意力计算都是在掩码令牌上执行的。我们通过将 Masked Autoencoders are Scalable Vision Learners (MAE) 的编码器-解码器架构集成到 SSAST 中来解决这个问题,其中深度编码器仅对未屏蔽的输入进行操作,开源节流是什么意思而浅解码器对编码器输出和掩码令牌进行操作。python语言我们发现,使用当前具有普通模能力拼音型和输入大小的音频预训练策开源节流略,与普通 SSAST 相比,类似 MAE 的预训练可以提供 3 倍的加速和python基础教程 2 倍的内存开源代码网站github使用减少。在对仅使用编码器的下脑颅膨大的意思游任务进行微调时,我们发现我们的方法在各种下游任务上都优于 SSAST。我们进一步对不同的预训练公积金策略进行综合评估,python123平台登录并探索视觉和音频领域之NLP间 MAE 式预训练的差异。

人工智能 | ShowMeAI资讯日报 #2022.06.06

人工智能 | ShowMeAI资讯日报 #2022.06.06

人工智能 | ShowMeAI资讯日报 #2022.06.06

论文:Are Tpython编程ransformers Effective for Time Series Forecasting?

论文标题:Are Transformers Effective for Time Series Forecasting?工龄越长退休金越多吗

论文时间:26 Maypython123 2022

所属领域:时间序列

对应任务:Anomaly Detection,Relation Extraction,Time Series,Time Series Analysis,Time Series Forecasting,异常检测,关系提取,时间序列,时间序python怎么读列分析,时间序列预测

论文地址:arxiv.org/abs那里拍婚纱照好/2205.13…

代码实现:github.com/cure-lab/DL…

论文作者:Ailing Zeng, Muxi Chen, Lei Zhang, Qiang Xu

论文简介:Rece回归模型拟合效果判断ntly, there has been a surgepython语言 of Transformer-工龄差一年工资差多少based solutionpython基础教程s for the time series forecasting (TSF) ta工龄差一年工资差多少sk, especially for the challenging long-term TSF problem. / 最近,针对时间序列预测 (TSF) 任务的基于 Transformer 的解决方案激增,努力拼音尤其是针对具有挑战性的长期 TSF 问工龄越长退休金越多吗题。

论文摘要:Recently, there has bepython安装教程en a surge of Transformer-bgoogleased哪里拍婚纱照最美 solutions for the time series forecasting (TSF) task, especially for the challenging long-term TS宫颈癌F problem. Transformer architecture relies开源矿工 on self-att开源中国ention mechanisms to effectively extract开源节流是什么意思 the semantic correlations between paired elements in a long sequence, which is permutatipython怎么读on-invariant and anti-ordering to some extent. H回归模型怎么建立owever, in time series modeling, we are to extract the temporal relations among an ordering set of continuous p开源节流是什么意思oints. Consequently, whether Transformer-based tec龚俊hniques are the right solutions for longpython安装教程-term time series forecasting is an interesting problem to investigate, despite the performance improvements shown in these studies. In this work, we question the va能力拼音lidity of Transformer-based TSF s开源olutions. In t能力培养与测试heir experiments, the compared (non-Transformer) baselines宫颈癌 are mainly autoregressiv工龄越长退休金越多吗e forecasting solutions, which usually have工商银行 a poor开源是什么意思 long-term predictpython可以做什么工作ion capability due to inevitable error accumulation effects. In contrPythonast, we use an embarrassingly simple architecture龚俊 name开源矿工d DLinear that conducts direct multi-step (DMS) forecasting for comparison. DLinear decomposes the time series into a trend开源是什么意思 and a remainder series and employs two one-layer linear networks to model these tw龚俊o series for the forecasting task. Surprisingly, it开源众包 outper回归模型有哪些forms existing complex Transfo开源软件rmer-based models in most cases by a large margin. Therefore, we conclude that the relatively higher long-term forecasting accuracy of Transformer-based TSF solut回归模型中引入虚拟变量的作用ions线性回归模型 shown in existing works has little to do with the回归模型拟合效果判断 temporal relation extraction capabilities of the Tran回归模型拟合效果判断sforme回归模型的显著性检验r architecture能力培养与测试. Instead, it is main龚俊ly due to the no工资超过5000怎么扣税n-autoregressiv年龄拼音e DMS for回归模型有哪些ecasting strategy used in them. We hope this study also advocates revisitin开源软件g the validity of Transformer-based solutions for other time series analysis tasks (e.python是什么意思g., anomaly detection) in the future.

最近,针对时间序列预测 (TSF) 任务的基于 Transformer 的解决方案激增,尤其是针对具有挑战性的长期 TSF(时间序列预测) 问题。 Transformer 架构依靠自注意力机制来有效提取长序列中配对元素之间的语义相关性,这在一定程度上是置换不变和反序的。然而,在时间序列建模中,我们要提取一组有序的连续点之间的时间关系。因此,尽管这些研究显示了性能改进,但基于 Transformpython是什么意思er 的技术是否是长期时间序列预测的正确解决方案是一个值得研究的有趣问题。在这项工作中,我们质疑基于 Transformer 的 TSF 解决方案的有效性。实验中,比较的(非 TrNLPansforme能力拼音r)基线主要是自回归预测解决方案,由于不可避免的误差累积效应,它们的长期预测能力通常很差。相比之下,我们使用了工龄越长退休金越多吗一种非常简单的架构,名为 DL哪里拍婚纱照最美inear,它进行直接多能力培养与测试步 (DMS) 预测进行比较。 D工龄差一年工资差多少Linear 将时间序列分解为趋势序列和余数序列,并采用两线性回归模型个单层线性网络对这两个序列进行建模以用于预测任务。令回归模型怎么建立人惊讶的是,在大多数情况下,它大大优于现有的基于 Transformer 的复杂模型。因此,我们得出结论,现Go有作品中基于NLP Transformer 的 TSF 解决方案相对较高的长期开源软件预测精度与 Transformer 架构的时间关系提取能力关系不大。相反,这主要是由于其中使用了非开源软件自回归 DMS 预测策略。我们希望这项研究还提倡在未来重新审视基于 Transformer 的解决方案对其他时间序列分析任务(例如异常检测)的有效性。

人工智能 | ShowMeAI资讯日报 #2022.06.06

人工智能 | ShowMeAI资讯日报 #2022.06.06

论文:Mult开源代码网站githubipython123平台登录modal Image Synthesis and Editing: A Survey

论文标题工商银行:Multimodal Image Synthesis and Editing: A Survey

论文时间:27 Dec 2021

所属领线性回归模型:计算机视觉

对应任务:Image Generation,图像生成

论文地址:arxiv.org/abs/2112.13…

代码实现:github.com/fnzh回归模型分析an/mise

论文作者:Fangneng Zhan, Yingchen Yu, Rongliang W宫颈癌u, Jiahui Zhang, Shijian Lu

论文简介:We start with an introduction to different types of guidance mod嫩绿拼音alities in image synthesis and editing. / 我们首先介绍图像合成和编辑中不同类型的引导模式。

论文摘要:As information exists in various mopython编程dalities in real world, effective interaction and fusion among multimodal informatio开源n playgoogles a key role for the creation and perception of multimodal data in c脑颅膨大的意思omputer vision and deep learning research. With superb power in modelling the in回归模型分析teractio开源是什么意思n among multimodal informati那里拍婚纱照好on, multimodal image synthesis and editing have become a hot research topic in recent years. Different from traditional visual guidance which provides e开源是什么意思xplicit clues, multimodal guidance offers intuitive and flexible means in image synthesis and editing. On the other hand, this field is also facing several challenges in alignm回归模型ent of features with igooglenherent modali回归模型ty gaps, synthesis of hipython基础教程gh-resolution images, faithful evaluation metrics, etc. In this survey, we copython123平台登录mprehensively contextualize the advance of the recent multimodal image synthesis &a哪里拍婚纱照最美mp; editing and formulate taxonomies according to data modality and model architectures. We start with an introduction to different types of guidance modalities in imag工资超过5000怎么扣税e synthesis and ed工商银行iting. We th嫩绿拼音en describe multimodal image sgoogleynthesis and editing a开源节流是什么意思pproache回归模型的作用s extensively with detailed frameworks inc开源众包luding Generative Adversarial Networks (GANs),nlp GAN Inversion, Transf能力拼音ormers, and other methods such as NeRF and Diffusion models. This is followed by a comprGoehensi公积金ve description of benchmark da回归模型公式tasets and corresponding evaluation metrics as widely adopted in multimodal image synthesis and edi公司让员工下班发手机电量截图tpython安装教程ing, as well as detailed comparisons of different synthesis methods with analysis of res工龄差一年工资差多少pective advantages and limitationsgoogle. Finally, w开源是什么意思e provide insights into the current resepython安装教程arch challenges and po多元回归模型ssible futPythonure research directions. A pro开源软件ject associated with this survey is availa工龄差一年工资差多少ble at gi开源中国thub.com/fnzhan/MISE.开源软件

由于现实世界中信息以各种模态存在,多模态信息之间的有效交互和融合对于计算机视觉那里拼音和深度学习研究中多模态数据的创建和感知起着关键作用。凭借对多模态信息交互python基础教程建模的强大能力,多模态图像合成与编辑成为近年来的研究热点。与传统的提供明确线索的视觉引导不同,多模态引导在图像合成和编辑方面提供了直观灵活的手段。另一方面,该领域在特征与固有模态差距的对齐、高分辨率图像的合成、真实性的评估指标等方面也面临着一些挑战。在本次调查中,哪里拍婚纱照最美我们全面介绍了最近多模态图像合成的进展,以及根据数据模python怎么读式和模型架构编辑和制定分类法。我们首先介绍图像合成和编辑中不同类型的引导方式。然后,我们使用详细的框架广泛描述多模态图像合成和编辑方法,包括生成对抗网络 (GAN)、GAN 反宫颈癌转、Transformers 和其他方法,例如 NeRF 和扩散模型。随后对多模态图像合成和编辑中广泛采用的基准数据集和相应的评估指标进行了全面描述,并对不同的合成方法进行了详细比较,并分析了各自的优缺点。最后,我们提供了对当前研究挑战和未来可能的研究方向的见解。与此调查相关的项目可在 github.com/fnzhan/MISE 获得

人工智能 | ShowMeAI资讯日报 #2022.06.06

人工智能 | ShowMeAI资讯日报 #2022.06.06

人工智能 | ShowMeAI资讯日报 #2022.06.06

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人工智能 | ShowMeAI资讯日报 #2022.06.06

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