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20162025
most citedTemporal Segment Networks: Towards Good Practices for Deep Action Recognition

289 citations · 917 across the 73 of their papers we have counts for

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Showing 2016Show all

11 papers · 1 filter

cs.CV201612 cited

Deep Temporal Linear Encoding Networks

Ali Diba, Vivek Sharma, Luc Van Gool

The CNN-encoding of features from entire videos for the representation of human actions has rarely been addressed. Instead, CNN work has focused on approaches to fuse spatial and t…

cs.CV201631 cited

Efficient Two-Stream Motion and Appearance 3D CNNs for Video Classification

Ali Diba, Ali Mohammad Pazandeh, Luc Van Gool

The video and action classification have extremely evolved by deep neural networks specially with two stream CNN using RGB and optical flow as inputs and they present outstanding p…

cs.CV20163 cited

Transferring Object-Scene Convolutional Neural Networks for Event Recognition in Still Images

Limin Wang, Zhe Wang, Yu Qiao +1

Event recognition in still images is an intriguing problem and has potential for real applications. This paper addresses the problem of event recognition by proposing a convolution…

cs.CV20164 cited

Does V-NIR based Image Enhancement Come with Better Features?

Vivek Sharma, Luc Van Gool

Image enhancement using the visible (V) and near-infrared (NIR) usually enhances useful image details. The enhanced images are evaluated by observers perception, instead of quantit…

cs.CV20161 cited

Geometry-aware Similarity Learning on SPD Manifolds for Visual Recognition

Zhiwu Huang, Ruiping Wang, Xianqiu Li +4

Symmetric Positive Definite (SPD) matrices have been widely used for data representation in many visual recognition tasks. The success mainly attributes to learning discriminative…

cs.CV2016140 cited

Convolutional Oriented Boundaries

Kevis-Kokitsi Maninis, Jordi Pont-Tuset, Pablo Arbeláez +1

We present Convolutional Oriented Boundaries (COB), which produces multiscale oriented contours and region hierarchies starting from generic image classification Convolutional Neur…