• Corpus ID: 195346008

C3D: Generic Features for Video Analysis

@article{Tran2014C3DGF,
  title={C3D: Generic Features for Video Analysis},
  author={Du Tran and Lubomir D. Bourdev and Rob Fergus and Lorenzo Torresani and Manohar Paluri},
  journal={ArXiv},
  year={2014},
  volume={abs/1412.0767}
}
Videos have become ubiquitous due to the ease of capturing and sharing via social platforms like Youtube, Facebook, Instagram, and others. The computer vision community has tried to tackle various video analysis problems independently. As a consequence, even though some really good hand-crafted features have been proposed there is a lack of generic features for video analysis. On the other hand, the image domain has progressed rapidly by using features from deep convolutional networks. These… 
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373 Citations
Highly Influential Citations
65
Background Citations
173
Methods Citations
181
Results Citations
5

373 Citations

Efficient Large Scale Video Classification

This work proposes two models for frame-level and video-level classification, the first is a highly efficient mixture of experts while the latter is based on long short term memory neural networks.

Handcrafted Local Features are Convolutional Neural Networks

This paper proposes a two-stream Convolutional ISA (ConvISA) that adopts the convolution-pooling structure of the state-of-the-art handcrafted video feature with greater modeling capacities and a cost-effective training algorithm.

VidSage: Unsupervised Video Representational Learning with Graph Convolutional Networks

This work proposed ”VidSage”, a system that transforms the input video into a generic representation in an unsupervised and self-supervised fashion, which obtained 54% and 28% classification accuracy on Charade, and Moments in Time datasets, outperforming previous unsuper supervised methods by 9% and 17% respectively, and on par with the recent meta-learning-based work by Google.

ECO: Efficient Convolutional Network for Online Video Understanding

A network architecture that takes long-term content into account and enables fast per-video processing at the same time and achieves competitive performance across all datasets while being 10 to 80 times faster than state-of-the-art methods.

Attention Transfer from Web Images for Video Recognition

This work proposes a novel approach to transfer knowledge from image domain to video domain, and designs a novel Siamese EnergyNet structure to learn energy functions on the attention maps by jointly optimizing two loss functions, such that the attention map corresponding to a ground truth concept would have higher energy.

Compact CNN for indexing egocentric videos

A compact 3D Convolutional Neural Network architecture for long-term activity recognition in egocentric videos and a novel visualization of CNN kernels as flow fields to better understand what the network actually learns is proposed.

Dynamic scene classification using convolutional neural networks

This paper analyzes the performance of statistical aggregation techniques on various pre-trained convolutional neural network models to address the problem of dynamic scene classification and shows that the proposed approach performs better than the-state-of-the art works for the Maryland and YUPenn dataset.

Real-Time Action Recognition with Enhanced Motion Vector CNNs

This paper accelerates the deep two-stream architecture by replacing optical flow with motion vector which can be obtained directly from compressed videos without extra calculation, and introduces three strategies for this, initialization transfer, supervision transfer and their combination.

(Deep) Learning from Frames

A novel classification method that encapsulates multiple distinct ConvNets to perform genre classification, namely CoNNECT, where each ConvNet learns features that capture distinct aspects from the movie frames, significantly outperforms the state-of-the-art approaches in this task.

Order-aware Convolutional Pooling for Video Based Action Recognition

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