Browse > Computer Vision > Video > Video Classification

# Video Classification Edit

39 papers with code · Computer Vision

TREND DATASET BEST METHOD PAPER TITLE PAPER CODE COMPARE

# Group Normalization

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

22,740

# Non-local Neural Networks

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

22,740

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

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)

1,864

# 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)

1,082

# Video Classification with Channel-Separated Convolutional Networks

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.

690

# 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.

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# Learnable pooling with Context Gating for video classification

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.

398

# Deep Temporal Linear Encoding Networks

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.

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# 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.

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# Learning Spatio-Temporal Representation with 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.

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