Video Recognition
147 papers with code • 0 benchmarks • 10 datasets
Video Recognition is a process of obtaining, processing, and analysing data that it receives from a visual source, specifically video.
Benchmarks
These leaderboards are used to track progress in Video Recognition
Libraries
Use these libraries to find Video Recognition models and implementationsDatasets
Kinetics
BosphorusSign22k
MIDV-2019
3DYoga90
L-SVD
Crowd 11
RAISE-LPBF
Most implemented papers
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
facebookresearch/OctConv
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ICCV 2019
Similarly, the output feature maps of a convolution layer can also be seen as a mixture of information at different frequencies.
SlowFast Networks for Video Recognition
facebookresearch/SlowFast
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ICCV 2019
We present SlowFast networks for video recognition.
Video Swin Transformer
SwinTransformer/Video-Swin-Transformer
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CVPR 2022
The vision community is witnessing a modeling shift from CNNs to Transformers, where pure Transformer architectures have attained top accuracy on the major video recognition benchmarks.
TSM: Temporal Shift Module for Efficient Video Understanding
MIT-HAN-LAB/temporal-shift-module
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ICCV 2019
The explosive growth in video streaming gives rise to challenges on performing video understanding at high accuracy and low computation cost.
Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?
kenshohara/3D-ResNets-PyTorch
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Therefore, in the present paper, we conduct exploration study in order to improve spatiotemporal 3D CNNs as follows: (i) Recently proposed large-scale video datasets help improve spatiotemporal 3D CNNs in terms of video classification accuracy.
Micro-Batch Training with Batch-Channel Normalization and Weight Standardization
joe-siyuan-qiao/WeightStandardization
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Batch Normalization (BN) has become an out-of-box technique to improve deep network training.
X3D: Expanding Architectures for Efficient Video Recognition
facebookresearch/SlowFast
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CVPR 2020
This paper presents X3D, a family of efficient video networks that progressively expand a tiny 2D image classification architecture along multiple network axes, in space, time, width and depth.
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
garythung/torch-lrcn
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CVPR 2015
Models based on deep convolutional networks have dominated recent image interpretation tasks; we investigate whether models which are also recurrent, or "temporally deep", are effective for tasks involving sequences, visual and otherwise.
Multiscale Vision Transformers
facebookresearch/SlowFast
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ICCV 2021
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.
MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
facebookresearch/detectron2
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CVPR 2022
In this paper, we study Multiscale Vision Transformers (MViTv2) as a unified architecture for image and video classification, as well as object detection.
