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dundancy of the data a high masking ratio leads to a large speedup e g 4x in wall clock time or even more we report competitive results on several challenging video datasets using vanilla vision transformers we observe that mae can outperform supervised pre training by large margins we further report encouraging results of training on real world uncurated instagram data our study suggests that the general framework of masked autoencoding bert mae etc can be a unified methodology for representation learning with minimal domain knowledge arxiv code pytorch code has been open sourced in pyslowfast pytorchvideo masked autoencoders that listen po yao huang hu xu juncheng li alexei baevski michael auli wojciech galuba florian metze christoph feichtenhofer advances in neural information processing systems neurips 2022 this paper studies a simple extension of image based masked autoencoders mae to self supervised representation learning from audio spectrograms following the transformer encoder decoder design in mae our audio mae first encodes audio spectrogram patches with a high masking ratio feeding only the non masked tokens through encoder layers the decoder then re orders and decodes the encoded context padded with mask tokens in order to reconstruct the input spectrogram we find it beneficial to incorporate local window attention in the decoder as audio spectrograms are highly correlated in local time and frequency bands we then fine tune the encoder with a lower masking ratio on target datasets empirically audio mae sets new state of the art performance on six audio and speech classification tasks outperforming other recent models that use external supervised pre training arxiv code memvit memory augmented multiscale vision transformer for efficient long term video recognition chao yuan wu yanghao li karttikeya mangalam haoqi fan bo xiong jitendra malik christoph feichtenhofer equal technical contribution conference on computer vision and pattern recognition cvpr 2022 oral in this paper we propose a new strategy to overcome this challenge instead of trying to process more frames at once like most existing methods we propose to process videos in an online fashion and cache memory at each iteration through the memory the model can reference prior context for long term modeling with only a marginal cost based on this idea we build memvit a memory augmented multiscale vision transformer that has a temporal support 30x longer than existing models with only 4 5 more compute traditional methods need 3 000 more compute to do the same on a wide range of settings the increased temporal support enabled by memvit brings large gains in recognition accuracy consistently memvit obtains state of the art results on the ava epic kitchens 100 action classification and action anticipation datasets code and models will be made publicly available arxiv code pytorch code will be open sourced in pyslowfast pytorchvideo masked feature prediction for self supervised visual pre training chen wei haoqi fan saining xie chao yuan wu alan yuille christoph feichtenhofer equal technical contribution conference on computer vision and pattern recognition cvpr 2022 we present masked feature prediction maskfeat for self supervised pre training of video models our approach first randomly masks out a portion of the input sequence and then predicts the feature of the masked regions we study five different types of features and find histograms of oriented gradients hog a hand crafted feature descriptor works particularly well in terms of both performance and efficiency we observe that the local contrast normalization in hog is essential for good results which is in line with earlier work using hog for visual recognition our approach can learn abundant visual knowledge and drive large scale transformer based models without using extra model weights or supervision maskfeat pre trained on unlabeled videos achieves unprecedented results of 86 7 with mvit l on kinetics 400 88 3 on kinetics 600 80 4 on kinetics 700 38 8 map on ava and 75 0 on ssv2 maskfeat further generalizes to image input which can be interpreted as a video with a single frame and obtains competitive results on imagenet arxiv code pytorch code has been open sourced in pyslowfast pytorchvideo mvitv2 improved multiscale vision transformers for classification and detection yanghao li chao yuan wu haoqi fan karttikeya mangalam bo xiong jitendra malik christoph feichtenhofer equal technical contribution conference on computer vision and pattern recognition cvpr 2022 in this paper we study multiscale vision transformers mvit as a unified architecture for image and video classification as well as object detection we present an improved version of mvit that incorporates decomposed relative positional embeddings and residual pooling connections we instantiate this architecture in five sizes and evaluate it for imagenet classification coco detection and kinetics video recognition where it outperforms prior work we further compare mvits pooling attention to window attention mechanisms where it outperforms the latter in accuracy compute without bells and whistles mvit has state of the art performance in 3 domains 88 8 accuracy on imagenet classification 56 1 box ap on coco object detection as well as 86 1 on kinetics 400 video classification arxiv code reversible vision transformers karttikeya mangalam haoqi fan yanghao li chao yuan wu bo xiong christoph feichtenhofer jitendra malik equal technical contribution conference on computer vision and pattern recognition cvpr 2022 oral we present reversible vision transformers a memory efficient architecture design for visual recognition by decoupling the gpu memory footprint from the depth of the model reversible vision transformers enable memory efficient scaling of transformer architectures we adapt two popular models namely vision transformer and multi scale vision transformers to reversible variants and benchmark extensively across both model sizes and tasks of image classification object detection and video classification reversible vision transformers achieve a reduced memory footprint of up to 15 5x at identical model complexity parameters and accuracy demonstrating the promise of reversible vision transformers as an efficient backbone for resource limited training regimes finally we find that the additional computational burden of recomputing activations is more than overcome for deeper models where throughput can increase up to 3 9x over their non reversible counterparts arxiv website code min code a convnet for the 2020s zhuang liu hanzi mao chao yuan wu christoph feichtenhofer trevor darrell saining xie conference on computer vision and pattern recognition cvpr 2022 the roaring 20s of visual recognition began with the introduction of vision transformers vits which quickly superseded convnets as the state of the art image classification model a vanilla vit on the other hand faces difficulties when applied to general computer vision tasks such as object detection and semantic segmentation it is the hierarchical transformers e g swin transformers that reintroduced several convnet priors making transformers practically viable as a generic vision backbone and demonstrating remarkable performance on a wide variety of vision tasks however the effectiveness of such hybrid approaches is still largely credited to the intrinsic superiority of transformers rather than the inherent inductive biases of convolutions in this work we reexamine the design spaces and test the limits of what a pure convnet can achieve we gradually modernize a standard resnet toward the design of a vision transformer and discover several key components that contribute to the performance difference along the way the outcome of this exploration is a family of pure convnet models dubbed convnext constructed entirely from standard convnet modules convnexts compete favorably with transformers in terms of accuracy and scalability achieving 87 8 imagenet top 1 accuracy and outperforming swin transformers on coco detection and ade20k segmentation while maintaining the simplicity and efficiency of standard convnets arxiv code trackformer multi object tracking with transformers tim meinhardt alexander kirillov laura leal taixé christoph feichtenhofer conference on computer vision and pattern recognition cvpr 2022 we present trackformer an end to end multi object tracking and segmentation model based on an encoder decoder transformer architecture our approach introduces track query embeddings which follow objects through a video sequence in an autoregressive fashion new track queries are spawned by the detr object detector and embed the position of their corresponding object over time the transformer decoder adjusts track query embeddings from frame to frame thereby following the changing object positions trackformer achieves a seamless data association between frames in a new tracking by attention paradigm by self and encoder decoder attention mechanisms which simultaneously reason about location occlusion and object identity trackformer yields state of the art performance on the tasks of multi object tracking mot17 and segmentation mots20 we hope our unified way of performing detection and tracking will foster future research in multi object tracking and video understanding arxiv code ego4d around the world in 3 000 hours of egocentric video kristen grauman et al conference on computer vision and pattern recognition cvpr 2022 oral best paper finalist we introduce ego4d a massive scale egocentric video dataset and benchmark suite it offers 3 025 hours of daily life activity video spanning hundreds of scenarios household outdoor workplace leisure etc captured by 855 unique camera wearers from 74 worldwide locations and 9 different countries the approach to collection is designed to uphold rigorous privacy and ethics standards with consenting participants and robust de identification procedures where relevant ego4d dramatically expands the volume of diverse egocentric video footage publicly available to the research community portions of the video are accompanied by audio 3d meshes of the environment eye gaze stereo and or synchronized videos from multiple egocentric cameras at the same event furthermore we present a host of new benchmark challenges centered around understanding the first person visual experience in the past querying an episodic memory present analyzing hand object manipulation audio visual conversation and social interactions and future forecasting activities by publicly sharing this massive annotated dataset and benchmark suite we aim to push the frontier of first person perception arxiv website pytorchvideo a deep learning library for video understanding haoqi fan tullie murrell heng wang kalyan vasudev alwala yanghao li yilei li bo xiong nikhila ravi meng li haichuan yang jitendra malik ross girshick matt feiszli aaron adcock wan yen lo christoph feichtenhofer proceedings of the 29th acm international conference on multimedia 2021 we introduce pytorchvideo an open source deep learning library that provides a rich set of modular efficient and reproducible components for a variety of video understanding tasks including classification detection self supervised learning and low level processing the library covers a full stack of video understanding tools including multimodal data loading transformations and models that reproduce state of the art performance pytorchvideo further supports hardware acceleration that enables real time inference on mobile devices the library is based on pytorch and can be used by any training framework for example pytorchlightning pyslowfast or classy vision arxiv code website multiscale vision transformers haoqi fan bo xiong karttikeya mangalam yanghao li zhicheng yan jitendra malik christoph feichtenhofer equal technical contribution international conference on computer vision iccv 2021 we present multiscale vision transformers mvit for video and image recognition by connecting the seminal idea of multiscale feature hierarchies with transformer models multiscale transformers have several channel resolution scale stages starting from the input resolution and a small channel dimension the stages hierarchically expand the channel capacity while reducing the spatial resolution this creates a multiscale pyramid of features with early layers operating at high spatial resolution to model simple low level visual information and deeper layers at spatially coarse but complex high dimensional features we evaluate this fundamental architectural prior for modeling the dense nature of visual signals for a variety of video recognition tasks where it outperforms concurrent vision transformers that rely on large scale external pre training and are 5 10x more costly in computation and parameters we further remove the temporal dimension and apply our model for image classification where it outperforms prior work on vision transformers arxiv code talk pytorch code has been open sourced in pyslowfast pytorchvideo a large scale study on unsupervised spatiotemporal representation learning christoph feichtenhofer haoqi fan bo xiong ross girshick kaiming he conference on computer vision and pattern recognition cvpr 2021 we present a large scale study on unsupervised spatiotemporal representation learning from videos with a unified perspective on four recent image based frameworks we study a simple objective that can easily generalize all these methods to space time our objective encourages temporally persistent features in the same video and in spite of its simplicity it works surprisingly well across i different unsupervised frameworks ii pre training datasets iii downstream datasets and iv backbone architectures we draw a series of intriguing observations from this study e g we discover that encouraging long spanned persistency can be effective even if the timespan is 60 seconds in addition to state of the art results in multiple benchmarks we report a few promising cases in which unsupervised pre training can outperform its supervised counterpart arxiv code pytorch code will be open sourced in pyslowfast pytorchvideo show older publications multiview pseudo labeling for semi supervised learning from video bo xiong haoqi fan kristen grauman christoph feichtenhofer international conference on computer vision iccv 2021 we present a multiview pseudo labeling approach to video learning a novel framework that uses complementary views in the form of appearance and motion information for semi supervised learning in video the complementary views help obtain more reliable pseudo labels on unlabeled video to learn stronger video representations than from purely supervised data though our method capitalizes on multiple views it nonetheless trains a model that is shared across appearance and motion input and thus by design incurs no additional computation overhead at inference time ...