For the masking strategy, the image was resized to a square and divided into patches before being fed into the transformer encoder; then, we mapped the probability heatmap to the same size as the input image. . n c from torchvision import models, transforms ; data curation, H.N. WiSPatially-Adaptive (DE)normalization(SPADE)Batch Normalizationchannel-wise mannerscale and bias2SPADE The design of the Pose Mask does not exactly follow the original MAE masking strategy, which randomly masks out up to 75% of all image patches. ; Tan, M. Coatnet: Marrying convolution and attention for all data sizes. The authors declare no conflict of interest. Zhang, H.; Wu, C.; Zhang, Z. Resnest: Split-attention networks. y 1 Solid developments have been seen in deep-learning-based pose estimation, but few works have explored performance in dense crowds, such as a classroom scene; furthermore, no specific knowledge is considered in the design of image augmentation for pose estimation. y https://github.com/XiaohangZhan/mix-an, CVPR2022 Crafting Better Contrastive Views for Siamese Representation Learning, Crafting Better Contrastive Views for Siamese Representation Learning C^i, H [, Liu, Z.; Mao, H.; Wu, C.Y. ^i_c In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA, 1624 September 2022. ) Consider the game of chess. 1.Abstract2.Introduction3.Approach3.1. import torch.nn.functional as F This paper shows that masked autoencoders (MAE) are scalable self-supervised learners for computer vision. ^i_{cyx} Flax), PyTorch, and/or TensorFlow. h To test the effectiveness of our proposed method, we collected a pose dataset from real-world surveillance cameras in classrooms. There are various choices available regarding the SideCar structure, and here we chose three common CNN structures for the associated experiments. i Subscribe to receive issue release notifications and newsletters from MDPI journals, You can make submissions to other journals. c W y L IntroThe idea of masked autoencoders, a form of more general denoising autoencoders [48], is natural and applicable in computer vision as well. Considering the excellent transfer performance of an MAE, we pre-trained it using the MS COCO dataset (only the person category was selected), which was different from the original MAE to better adapt to the dense human pose estimation in classroom scenes, and the 75% random mask was replaced by the proposed Pose Mask. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Marina Bay Sands Expo & Convention Center, Singapore, 2227 May 2022. Inspired by the masked autoencoders in image reconstruction, we proposed a model-based data augmentation method named Pose Mask, which served to fine-tune the pose estimation model using the reconstructed images as the new training set that was generated by the MAE trained with Pose Mask. Zhang, F.; Zhu, X.; Dai, H.; Ye, M.; Zhu, C. Distribution-aware coordinate representation for human pose estimation. So reinforcement learners must deal with the credit assignment problem: determining which actions to credit or blame for an outcome. 2 # train generator i y 4SPADESPADE1x1, Semantic manipulation and guided image synthesis. and M.M. C y Liu, H.; Liu, F.; Fan, X.; Huang, D. Polarized self-attention: Towards high-quality pixel-wise regression. 2 26:25 MAE!! i y 1 s=[] In the booming development of computer vision, the use of human pose estimation and behavior recognition technology [, In classroom scenarios, the problems of student limbs being obscured by desks and overlapping between students in classroom images can pose a considerable challenge to pose estimation, and poor accuracy of pose estimation results can also result in poor data quality for downstream tasks. Cai, Z.; Vasconcelos, N. Cascade r-cnn: Delving into high quality object detection. Andriluka, M.; Pishchulin, L.; Gehler, P.; Schiele, B. print(sigma) Therefore, we use reconstructed images from MS COCO using the original MAE to compare with our method, and the results can be seen in. nN,cCi,yHi,xWi. Masked Autoencoders in PyTorch. [, Xiao, B.; Wu, H.; Wei, Y. C Editors Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. !Masked Autoencoders Are Scalable Vision Learners 1.8 2021-11-25 38:41 n x 2 Simple baselines for human pose estimation and tracking. c Feature In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 1822 June 2018. ) A simple, unofficial implementation of MAE ( Masked Autoencoders are Scalable Vision Learners) using pytorch-lightning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA, 1624 September 2022. Deep residual learning for image recognition. m [, Sun, K.; Xiao, B.; Liu, D.; Wang, J. No special Multimodal: table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering. x In, In the original MAE, the training dataset used for image reconstruction was ImageNet-1K (IN-1K), which contains about 1.2 million training images, and thus, a sufficient amount of data ensured the generalization of pre-training. [648]mean Intersection-over-UnionmIoUpixel accuracyaccu:DeepLabV2 [540]mIoUaccuFrchet Inception Distance(FID) [17], Quantitative comparisons. N Xu, H.; Ding, S.; Zhang, X.; Xiong, H.; Tian, Q. Masked Autoencoders are Robust Data Augmentors. c x [, Yu, Z.; Li, Y.; Liu, Y. Synpose: A Large-Scale and Densely Annotated Synthetic Dataset for Human Pose Estimation in Classroom. import time decodersdecoderupsamplebilinear upsampling | Rukawa_Y | Sheryc_ SimCLR Self-Supervised LearningSelf-Supervised , demdsm30m12.5m, https://blog.csdn.net/weixin_42990464/article/details/125633568, Exploring Simple Siamese Representation Learning, Bootstrap Your Own Latent A New Approach to Self-Supervised Learning, Dense Contrastive Learning for Self-Supervised Visual Pre-Training, Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals, Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning, Unsupervised Learning of Visual Features by Contrasting Cluster Assignments, Geography-Aware Self-Supervised LearningICCV, Barlow Twins: Self-Supervised Learning via Redundancy Reduction, How Well Do Self-Supervised Models Transfer? [. i L In Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Korea, 27 October3 November 2019. Disclaimer/Publishers Note: The statements, opinions and data contained in all publications are solely ; visualization, H.N. ; investigation, H.L. Machine learning models are increasingly used in materials studies because of their exceptional accuracy. i m, Cdf: y_1y_2\in \{ 12H^i\} These models support common tasks in different modalities, such as: Natural Language Processing: text classification, named entity recognition, question answering, language modeling, summarization, translation, multiple choice, and text generation. [, Gidaris, S.; Singh, P.; Komodakis, N. Unsupervised representation learning by predicting image rotations. For instance, by using several sets of keypoint coordinates, we can calculate the angle of the relevant body part and further predict the pose of the person. After the MAE was trained for 2000 epochs, its weights were used to generate reconstructed images of the same amount of data from MS COCO. SqueezeBERT: What can computer vision teach NLP about efficient neural networks? In Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition, Buenos Aires, Argentina, 1620 November 2020. Many studies [, The pre-trained weights of MAE were loaded to initialize the backbone of the pose estimator for fine-tuning the model on the MS COCO dataset with 500 images from Class Pose. import numpy as np Decoders Matter for Semantic Segmentation. # Training patch patch norm 3.PCA 4. dVAE, Data Augmentation Table1(e)MAEcropColorJitterMAE, Mask SamplingTable1(f), Masking ratio75%(linear probing)BERT15%, linear probing, linear probing20%, linear probing, Training Schedule(800epoch)1600epochlinear probingMoCoV3300epochepochMAE25%MoCoV3200%, ViT-BViT-L, MAEViT-H86.9%44887.8%VOLO87.1%(512)ViT, COCOMAEViT-BMAE2.4APViT-L4.0AP, ADE20KMAEViT-L3.7, NLPMAETransfromer, paperpaper, noveltyfollow, MAEreconstruct imagework(iGPTwork; BEITreconstructreconstruct feature), NLP reconstructhighly semantic; capacitymodelreconstruction, ()MAEpatchhigh level semanticsnetwokfithigh-level feature? SPADE[2248][363954][2060], 4ResNet[15]SPADESPADEsegmentation mask, hinge[313854]least squared[34]pix2pixHD [48]GANs [13639]ResNetGPUSPADEpix2pixHD, Why does the SPADE work better? ci i [, Lin, T.Y. h^i, C The use of a probabilistic heatmap makes the MAE focus on learning the missing information of the skeleton, thus providing a better representation of the occluded feature. x_1x_2\in \{ 12W^i\} } i i However, the most accurate machine learning models are usually difficult to explain. W i i H . Inspired by this self-supervised learning method, where the restoration of the feature loss induced by the mask is consistent with tackling the occlusion problem in classroom scenarios, we discovered that the transfer performance of the pre-trained weights could be used as a model-based augmentation to overcome the intractable occlusion in classroom pose estimation. , In downstream tasks, specifically, in behavior analysis or action recognition, the coordinates of the keypoints provided as input by pose estimation can affect the final performance. y ( The MS COCO train2017 dataset contains 118,287 images of 80 object categories in various scenes; here, we cropped all the instances of the person category using the annotation of the object detection task to provide a total of 262,465 single-person samples, which was enough for pre-training the MAE. y c ; supervision, M.M. The MAE has the same structure as our pose estimator, which obtains the reconstructed images when a mini-batch of images is first input. c from models.networks.base_network import BaseNetwork paper provides an outlook on future directions of research or possible applications. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows, Swin Transformer V2: Scaling Up Capacity and Resolution, Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer, google-research/text-to-text-transfer-transformer, PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents, TAPAS: Weakly Supervised Table Parsing via Pre-training, TAPEX: Table Pre-training via Learning a Neural SQL Executor, Offline Reinforcement Learning as One Big Sequence Modeling Problem, Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context, TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models, UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data, UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING, VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training, ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision, An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, VisualBERT: A Simple and Performant Baseline for Vision and Language, Masked Autoencoders Are Scalable Vision Learners, Masked Siamese Networks for Label-Efficient Learning, wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations, FAIRSEQ S2T: Fast Speech-to-Text Modeling with FAIRSEQ, Simple and Effective Zero-shot Cross-lingual Phoneme Recognition, WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing, Robust Speech Recognition via Large-Scale Weak Supervision, Expanding Language-Image Pretrained Models for General Video Recognition, Few-shot Learning with Multilingual Language Models, Unsupervised Cross-lingual Representation Learning at Scale, Larger-Scale Transformers for Multilingual Masked Language Modeling, XLNet: Generalized Autoregressive Pretraining for Language Understanding, XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale, Unsupervised Cross-Lingual Representation Learning For Speech Recognition, You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection, You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling. This repo contains a comprehensive paper list of Vision Transformer & Attention, including papers, codes, and related websites. These handcraft augmentations are fast and stable in most scenarios; however, in some cases, pixel-level transformation, such as channel shuffle or color jitter, which uses a single pixel as the operational unit to enrich the diversity of the training data by directly changing the pixel value, sometimes have a detrimental effect on model learning. y 2 2 and M.M. MDPI and/or Decoders Matter for Semantic Segmentation All the code was implemented using Python 3.8 and Pytorch 1.8. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 1419 June 2020. x ; Lee, K.; Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. CSDN ## https://blog.csdn.net/nav/advanced-technology/paper-reading https://gitcode.net/csdn/csdn-tags/-/issues/34 , qq_41161212: c code: https://paperswithcode.com/paper/exploring-simple-siamese-representation (i)(ii)(iii)SimSiamImageNet, code: https://paperswithcode.com/paper/bootstrap-your-own-latent-a-new-approach-to, (BYOL)BYOLBYOLResNet-50BYOLImageNet74.3%ResNet79.6%BYOL, code: https://paperswithcode.com/paper/dense-contrastive-learning-for-self, (DenseCL)(dis) MoCo-v2(1%)()mance;MoCo-v2PASCAL VOC2.0% APCOCO1.1% APCOCO 0.9% APPASCAL VOC s3.0% mIoU, code: https://paperswithcode.com/paper/unsupervised-semantic-segmentation-by, , pascalK-MeansCOCOdavis, code: https://paperswithcode.com/paper/propagate-yourself-exploring-pixel-level, PascalVOC(C4)COCO(FPN/C4)60.2AP60.2AP41.4/40.5mAP77.2mIoU2.6AP0.8/1.0mAP1.0mIoU , code: https://paperswithcode.com/paper/unsupervised-learning-of-visual-features-by, SWAVResNet-50ImageNet75.3%1, , code: https://paperswithcode.com/paper/decoupled-contrastive-learning-1 CLSSLclInfoNCE-NPCNPCDCLDCLSimCLRMoCoDCLSimCLR20025668.2%ImageNet-1K1SimCLR6.4%DCLSOTANNCLR400512ImageNet-1K1SOTADCLSSL, code: https://paperswithcode.com/paper/barlow-twins-self-supervised-learning-via, SSLSSLH.ImageNetImageNet, 1340ImageNet Top-1, code: https://paperswithcode.com/paper/masked-autoencoders-are-scalable-vision, (MAE)MAE-75%3ImageNet-1KViT-Huge87.8%, code: https://github.com/microsoft/simmim, SimMIMVAE1)322)RGB3)ViT-BImageNet-1K83.8%1+0.6%6.5SwinV2-HImageNet-1KImageNet-1K87.1%13B(SwinV2-G)(JFT-3B)40, code: https://github.com/bwconrad/decoder-denoising, ImageNetPascalADE20K, code: https://github.com/xyupeng/ContrastiveCrop https://mp.weixin.qq.com/s?__biz=MjM5MjgwNzcxOA==&mid=2247486171&idx=1&sn=99271087396ef01edfad6e70fe0c3027&chksm=a6a1e49291d66d84ebb2722aa732866b357ea41137ce5105eeab666f49ba9efc442ed69435db#rd abstract: CIFAR-10CIFAR-100TinyMmageNetSTL-10SimCLRMoCoBYOLSimSiam0.4%2.0%ImageNet-1K, code: https://github.com/MkuuWaUjinga/leopart, 17%-3%, code: https://paperswithcode.com/paper/efficient-visual-pretraining-with-contrastive 10ImageSEERSEER1000COCOCOCOpascal, code: https://paperswithcode.com/paper/detco-unsupervised-contrastive-learning-for, DetCoDetCo12, VOCCOCOImageNetDetCoImageNetDetCoDenseCL6.9%5.0%COCODetCoMaskR-CNNC4SwAV6.9 APDetCoR-CNN45.0 AP46.5AP(+1.5AP)COCOSOTA, code: https://github.com/SwinTransformer/Transformer-SSL, CNNTransformersMoBYTransformersMoCo v2BYOLImageNet-1K300epochDeiT-SSwin-T172.8%75.0%MoCo v3DINODeiT/DeiTImageNet-1K/, transformerDETRtransformerfaster R-CNNDETRUP-DETR(1)CNN(2)UP-DETRUP-DETRDETR, transformer, , code: https://paperswithcode.com/paper/context-autoencoder-for-self-supervised (MIM)(CAE), code: https://paperswithcode.com/paper/self-supervised-transformers-for-unsupervised, DINO, VOC07VOC12COCO20K6.9%8.1%8.1%CADECSSDDUTSDUT-OMRON4.9%5.2%12.9%CUBImageNet, code: https://paperswithcode.com/paper/refine-and-represent-region-to-object, R2OR2OR2O, R2OR2OVOC+0.7 mIOU+0.4 mIOUMS COCO+0.3 APmkImageNetR2O-ucsd200-2011+2.9 mIoU, code: https://paperswithcode.com/paper/detreg-unsupervised-pretraining-with-region, DETRegDETRegDETRDETRegCOCOVOC, code: https://paperswithcode.com/paper/self-supervised-learning-of-remote-sensing, , code: https://github.com/michaeltrs/deepsatmodels, 25%mIoU, code: https://paperswithcode.com/paper/semantic-aware-dense-representation-learning, (CD)CDImageNet(RS)(SSL)RSRSCDCDRSCDImageNetSSL20%100%, code: https://github.com/flyakon/SSLRemoteSensing, /Levir_CSImageNet10%50%, SSLSSLSSLSSLSSLSSLSSL4EO, kakalt6: The original MAE masking method involves splitting the image into patches of the same sizes first and then using positional embedding to stretch all the patches into vectors, and after shuffling patches randomly, selects the first 25% of the patches as input into the encoder, thus reducing 75% of the computational burden of the transformer blocks. Human pose estimation and its application to action recognition: A survey. and S.L. i sigma.append(item) from torch.utils.data import DataLoader and H.N. ,, ybacm: m \mu^i_c import shutil 1 h^i_{ncyx} y Pascal@https://www.zhihu.com/people/pascal_cs/posts0 https://blog.csdn.net/weixin_39589455/article/details/114680465?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522164250665916781685395052%2522%252C%2522scm%2522%253A%252220140713.130102334.pc%255Fall.%2522%257D&request_id=16425066591678168539505, | | |https://zhuanlan.zhihu.com/p/2204036741, , CSDN ## https://blog.csdn.net/nav/advanced-technology/paper-reading https://gitcode.net/csdn/csdn-tags/-/issues/34 , abstractfeiduichen , good worktranformer, https://blog.csdn.net/m0_61899108/article/details/127340090, Gradient visualization with vanilla backpropagation, Gradient visualization with guided backpropagation, Gradient visualization with saliency maps, Gradient-weighted class activation mapping, Guided, gradient-weighted class activation mapping, Element-wise gradient-weighted class activation mapping, Learning Deep Features for Discriminative Localization, Masked Autoencoders Are Scalable Vision Learners, Remote Sensing Image Change Detection with Transformers, Multi-Content Complementation Network for Salient Object Detection in Optical RSI, Transformers in Remote Sensing: A Survey , Transformers in Remote Sensing: A Survey. ScalableViT. , Python Win http://www.github.com/deaconjs/, https://arxiv.org/abs/1903.07291 abstractfeiduichen , qq_41161212: c i ) import torch.nn as nn The advantage of a CNN regarding local features can be added as compensation to the heatmap of the ViT parts output. i , : permission provided that the original article is clearly cited. Our method adopted the MS COCO dataset for pre-training the MAE that used Pose Mask. \mu^i_c, We summarized the properties of Class Pose into the following aspects: Our model-based image augmentation method was implemented based on the official codes of the MAE [. 2 i y CONCEPTUAL GUIDES offers more discussion and explanation of the underlying concepts and ideas behind models, tasks, and the design philosophy of Transformers. for itemset in list: c This type of [2060]VAE [28]1KL-[28] , Implementation details. i Models can also be exported to a format like ONNX and TorchScript for deployment in production environments. ^i_c, Masked Autoencoders Are Scalable Vision Learners 6828; python 6288; pytorch 5288 Remote Sensing Image Change Detection with Transformers 5100 ( Finally, a linear weighted randomization (LWR) algorithm was applied to weight the patches, which was calculated as follows: The ViT part of our backbone was the same as the pre-training phase, only without masking. Feature Papers represent the most advanced research with significant potential for high impact in the field. Xiao, T.; Singh, M.; Mintun, M.; Darrell, T.; Dollr, P.; Girshick, R. Early convolutions help transformers see better. ^i_{cy_1x_1} ^i_{cy_2x_2} i ^i_{cyx}(m), x In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 1822 June 2018. CVPR2022 ICLR2022Finetuned Language Models Are Zero-Shot Learners Masked Autoencoders Are Scalable Vision LearnersMAE 2 for name in sigma: Papers are submitted upon individual invitation or recommendation by the scientific editors and undergo peer review Audio: automatic speech recognition and audio classification. Spectral Norm[38]0.00010.0004[17]1= 02= 0.999ADAM solver[27]NVIDIA DGX1832GBV100 GPUs, , Performance metrics. Masked Autoencoders Are Scalable Vision Learners 6048; Patches Are All You Need? . x1x2{12Wi})BN[11]Conditinoal BatchNormmN = 1AdaIN [19]SPADE, SPADE generator. hiNbatch https://github.com/NVlabs/, Pattern Evaluation support-confidence association rule mining frameworkliftchi-square measures , 1.encoder All authors have read and agreed to the published version of the manuscript. W cy1x1icy2x2i x y Toshev, A.; Szegedy, C. Deeppose: Human pose estimation via deep neural networks. 72817293. Transformers support framework interoperability between PyTorch, TensorFlow, and JAX. and M.M. Class Pose comprised 1000 images of 56,732 instances in various resolutions, making it adequate for evaluation. c import torch Xu, X.; Xin, T. Classroom attention analysis based on multiple euler angles constraint and head pose estimation. Contributions in any form to make this list x However, it is very important that the students are willing to do the hard work to learn and use these two frameworks as the course progresses. Different from the above shuffling strategy in the original MAE, we statistically calculated the distribution of all keypoints in the MS COCO dataset, as shown in. import torchvision.utils as vutil AAAI Conference on Artificial Intelligence (AAAI) 2018 ^i_{cyx} HOW-TO GUIDES show you how to achieve a specific goal, like finetuning a pretrained model for language modeling or how to write and share a custom model. c ^i_{cy_1x_1} ^i_{cy_2x_2}, y A novel pose estimator was proposed, which uses ViT [. ( 1 1 H In Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, 1214 April 2017. the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, ; Zhao, K. Res2net: A new multi-scale backbone architecture. ; Park, T.; Isola, P.; Efros, A.A. Unpaired image-to-image translation using cycle-consistent adversarial networks. if item not in sigma: 1 i sigma=[] cy1x1icy2x2i h { Although their backbone structures are similar, they cannot share the same parameters, which produces a double burden on video memory and processing speed.
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