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Video Classification Edit

35 papers with code · Computer Vision

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Group Normalization

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

#22 best model for Object Detection on COCO

21,555

Non-local Neural Networks

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

21,555

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.

1,708

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.

931

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.

522

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.

436

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.

366

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.

308

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.

307

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.

287