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Video Classification

36 papers with code · Computer Vision
Subtask of Video

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Group Normalization

ECCV 2018 facebookresearch/detectron

FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.

OBJECT DETECTION VIDEO CLASSIFICATION

Non-local Neural Networks

CVPR 2018 facebookresearch/detectron

Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time.

INSTANCE SEGMENTATION KEYPOINT DETECTION OBJECT DETECTION VIDEO CLASSIFICATION

YouTube-8M: A Large-Scale Video Classification Benchmark

27 Sep 2016google/youtube-8m

Despite the size of the dataset, some of our models train to convergence in less than a day on a single machine using TensorFlow.

 SOTA for Action Recognition In Videos on ActivityNet (using extra training data)

ACTION RECOGNITION IN VIDEOS

Temporal Segment Networks for Action Recognition in Videos

8 May 2017yjxiong/temporal-segment-networks

Furthermore, based on the temporal segment networks, we won the video classification track at the ActivityNet challenge 2016 among 24 teams, which demonstrates the effectiveness of TSN and the proposed good practices.

#5 best model for Action Classification on Moments in Time (Top 5 Accuracy metric)

ACTION CLASSIFICATION ACTION RECOGNITION IN VIDEOS

Video Classification with Channel-Separated Convolutional Networks

4 Apr 2019facebookresearch/R2Plus1D

It is natural to ask: 1) if group convolution can help to alleviate the high computational cost of video classification networks; 2) what factors matter the most in 3D group convolutional networks; and 3) what are good computation/accuracy trade-offs with 3D group convolutional networks.

ACTION CLASSIFICATION ACTION RECOGNITION IN VIDEOS IMAGE CLASSIFICATION

TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity Recognition

30 Mar 2017jeffreyhuang1/two-stream-action-recognition

We demonstrate that using both RNNs (using LSTMs) and Temporal-ConvNets on spatiotemporal feature matrices are able to exploit spatiotemporal dynamics to improve the overall performance.

ACTION CLASSIFICATION ACTION RECOGNITION IN VIDEOS VIDEO UNDERSTANDING

Learnable pooling with Context Gating for video classification

21 Jun 2017antoine77340/Youtube-8M-WILLOW

In particular, we evaluate our method on the large-scale multi-modal Youtube-8M v2 dataset and outperform all other methods in the Youtube 8M Large-Scale Video Understanding challenge.

VIDEO CLASSIFICATION VIDEO UNDERSTANDING

Deep Temporal Linear Encoding Networks

CVPR 2017 bryanyzhu/two-stream-pytorch

Advantages of TLEs are: (a) they encode the entire video into a compact feature representation, learning the semantics and a discriminative feature space; (b) they are applicable to all kinds of networks like 2D and 3D CNNs for video classification; and (c) they model feature interactions in a more expressive way and without loss of information.

REPRESENTATION LEARNING VIDEO CLASSIFICATION

Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks

ICCV 2017 ZhaofanQiu/pseudo-3d-residual-networks

In this paper, we devise multiple variants of bottleneck building blocks in a residual learning framework by simulating $3\times3\times3$ convolutions with $1\times3\times3$ convolutional filters on spatial domain (equivalent to 2D CNN) plus $3\times1\times1$ convolutions to construct temporal connections on adjacent feature maps in time.

ACTION RECOGNITION IN VIDEOS

Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks

19 Nov 2015pbashivan/EEGLearn

One of the challenges in modeling cognitive events from electroencephalogram (EEG) data is finding representations that are invariant to inter- and intra-subject differences, as well as to inherent noise associated with such data.

EEG TIME SERIES VIDEO CLASSIFICATION